Papers | Parallel Computing
2024
Gianluca Mittone, Giulio Malenza, Marco Aldinucci, Robert Birke
Distributed Edge Inference: an Experimental Study on Multiview Detection Proceedings Article
In: Proc. of the 16th IEEE/ACM Intl. Conference on Utility and Cloud Computing Companion (UCC), pp. 1-6, ACM, Taormina, Italy, 2024, (eupilot, icsc).
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@inproceedings{23:mittone:multiview,
title = {Distributed Edge Inference: an Experimental Study on Multiview Detection},
author = {Gianluca Mittone and Giulio Malenza and Marco Aldinucci and Robert Birke},
url = {https://iris.unito.it/handle/2318/1950083},
doi = {10.1145/3603166.3632561},
year = {2024},
date = {2024-12-01},
booktitle = {Proc. of the 16th IEEE/ACM Intl. Conference on Utility and Cloud Computing Companion (UCC)},
volume = {30},
pages = {1-6},
publisher = {ACM},
address = {Taormina, Italy},
institution = {Computer Science Department, University of Torino},
abstract = {Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum others to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.},
note = {eupilot, icsc},
keywords = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruno Casella, Alessio Barbaro Chisari, Marco Aldinucci, Sebastiano Battiato, Mario Valerio Giuffrida
Federated Learning in a Semi-Supervised Environment for Earth Observation Data Proceedings Article
In: Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, Bruges, Belgium, 2024.
Abstract | Links | BibTeX | Tags: ai, epi, icsc
@inproceedings{24:casella:fedrec,
title = {Federated Learning in a Semi-Supervised Environment for Earth Observation Data},
author = {Bruno Casella and Alessio Barbaro Chisari and Marco Aldinucci and Sebastiano Battiato and Mario Valerio Giuffrida},
url = {https://iris.unito.it/retrieve/a798d7b8-6b98-48c2-92f4-327d2aaa8788/ES2024-214.pdf},
doi = {10.14428/esann/2024.es2024-214},
year = {2024},
date = {2024-10-01},
booktitle = {Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN},
address = {Bruges, Belgium},
abstract = {We propose FedRec, a federated learning workflow taking advantage of unlabelled data in a semi-supervised environment to assist in the training of a supervised aggregated model. In our proposed method, an encoder architecture extracting features from unlabelled data is aggregated with the feature extractor of a classification model via weight averaging. The fully connected layers of the supervised models are also averaged in a federated fashion. We show the effectiveness of our approach by comparing it with the state-of-the-art federated algorithm, an isolated and a centralised baseline, on novel cloud detection datasets.},
keywords = {ai, epi, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruno Casella, Jakobs Matthias, Marco Aldinucci, Sebastian Buschjager
Federated Time Series Classification with ROCKET features Proceedings Article
In: Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, Bruges, Belgium, 2024.
Abstract | Links | BibTeX | Tags: ai, epi, icsc
@inproceedings{24:casella:frocks,
title = {Federated Time Series Classification with ROCKET features},
author = {Bruno Casella and Jakobs Matthias and Marco Aldinucci and Sebastian Buschjager},
url = {https://iris.unito.it/retrieve/51b63fc1-3e22-4ad4-8926-84af69cde739/ES2024-61.pdf},
doi = {10.14428/esann/2024.es2024-61},
year = {2024},
date = {2024-10-01},
booktitle = {Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN},
address = {Bruges, Belgium},
abstract = {This paper proposes FROCKS, a federated time series classification method using ROCKET features. Our approach dynamically adapts the models’ features by selecting and exchanging the best-performing ROCKET kernels from a federation of clients. Specifically, the server gathers the best-performing kernels of the clients together with the associated model parameters, and it performs a weighted average if a kernel is best-performing for more than one client. We compare the proposed method with state-of-the-art approaches on the UCR archive binary classification datasets and show superior performance on most datasets.},
keywords = {ai, epi, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Samuele Fonio, Mirko Polato, Roberto Esposito
FedHP: Federated Learning with Hyperspherical Prototypical Regularization Proceedings Article
In: 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN), Bruges, Belgium, 2024.
Abstract | Links | BibTeX | Tags: ai, icsc
@inproceedings{24:esann:fonio:fedhp,
title = {FedHP: Federated Learning with Hyperspherical Prototypical Regularization},
author = {Samuele Fonio and Mirko Polato and Roberto Esposito},
url = {https://www.esann.org/sites/default/files/proceedings/2024/ES2024-183.pdf},
year = {2024},
date = {2024-10-01},
booktitle = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN)},
address = {Bruges, Belgium},
abstract = {This paper presents FedHP, an algorithm that amalgamates federated learning, hyperspherical geometries, and prototype learning. Federated Learning (FL) has garnered attention as a privacy-preserving method for constructing robust models across distributed datasets. Traditionally, FL involves exchanging model parameters to uphold data privacy; however, in scenarios with costly data communication, exchanging large neural net- work models becomes impractical. In such instances, prototype learning provides a feasible solution by necessitating the exchange of a few class prototypes instead of entire deep learning models. Motivated by these considerations, our approach leverages recent advancements in prototype learning, particularly the benefits offered by non-Euclidean geometries. Alongside introducing FedHP, we provide empirical evidence demonstrat- ing its comparable performance to other state-of-the-art approaches while significantly reducing communication costs.},
keywords = {ai, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Iacopo Colonnelli, Doriana Medić, Alberto Mulone, Viviana Bono, Luca Padovani, Marco Aldinucci
Introducing SWIRL: An Intermediate Representation Language for Scientific Workflows Proceedings Article
In: Platzer, André, Rozier, Kristin Yvonne, Pradella, Matteo, Rossi, Matteo (Ed.): Formal Methods. FM 2024, pp. 226–244, Springer Nature Switzerland, Milano, Italy, 2024.
Abstract | Links | BibTeX | Tags: eupex, icsc
@inproceedings{24:fm:swirl,
title = {Introducing SWIRL: An Intermediate Representation Language for Scientific Workflows},
author = {Iacopo Colonnelli and Doriana Medić and Alberto Mulone and Viviana Bono and Luca Padovani and Marco Aldinucci},
editor = {André Platzer and Kristin Yvonne Rozier and Matteo Pradella and Matteo Rossi},
url = {https://iris.unito.it/retrieve/b39a6f09-a8d3-4974-abf6-c109916694fa/PDFEditoriale.pdf},
doi = {10.1007/978-3-031-71162-6_12},
year = {2024},
date = {2024-09-01},
booktitle = {Formal Methods. FM 2024},
volume = {14933},
pages = {226–244},
publisher = {Springer Nature Switzerland},
address = {Milano, Italy},
series = {Lecture Notes in Computer Science},
abstract = {In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires new approaches to workflow execution, like seamless interoperability between different workflow systems, distributed-by-design workflow models, and automatic optimisation of data movements. In order to address this need, this article introduces SWIRL, an intermediate representation language for scientific workflows. In contrast with other product-agnostic workflow languages, SWIRL is not designed for human interaction but to serve as a low-level compilation target for distributed workflow execution plans. The main advantages of SWIRL semantics are low-level primitives based on the send/receive programming model and a formal framework ensuring the consistency of the semantics and the specification of translating workflow models represented by Directed Acyclic Graphs (DAGs) into SWIRL workflow descriptions. Additionally, SWIRL offers rewriting rules designed to optimise execution traces, accompanied by corresponding equivalence. An open-source SWIRL compiler toolchain has been developed using the ANTLR Python3 bindings.},
keywords = {eupex, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Simone Leo, Michael R. Crusoe, Laura Rodríguez-Navas, Raül Sirvent, Alexander Kanitz, Paul De Geest, Rudolf Wittner, Luca Pireddu, Daniel Garijo, José M. Fernández, Iacopo Colonnelli, Matej Gallo, Tazro Ohta, Hirotaka Suetake, Salvador Capella-Gutierrez, Renske Wit, Bruno P. Kinoshita, Stian Soiland-Reyes
Recording provenance of workflow runs with RO-Crate Journal Article
In: PLoS ONE, vol. 19, no. 9, pp. 1–35, 2024.
Abstract | Links | BibTeX | Tags: across, eupex, icsc, streamflow
@article{24:pone:wfrunrocrate,
title = {Recording provenance of workflow runs with RO-Crate},
author = {Simone Leo and Michael R. Crusoe and Laura Rodríguez-Navas and Raül Sirvent and Alexander Kanitz and Paul De Geest and Rudolf Wittner and Luca Pireddu and Daniel Garijo and José M. Fernández and Iacopo Colonnelli and Matej Gallo and Tazro Ohta and Hirotaka Suetake and Salvador Capella-Gutierrez and Renske Wit and Bruno P. Kinoshita and Stian Soiland-Reyes},
url = {https://iris.unito.it/retrieve/57752f7b-9f8f-4013-8cef-5b498703d882/journal.pone.0309210.pdf},
doi = {10.1371/journal.pone.0309210},
year = {2024},
date = {2024-09-01},
journal = {PLoS ONE},
volume = {19},
number = {9},
pages = {1–35},
publisher = {Public Library of Science},
abstract = {Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and Schema.org to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain.},
keywords = {across, eupex, icsc, streamflow},
pubstate = {published},
tppubtype = {article}
}
Alberto Mulone, Doriana Medić, Marco Aldinucci
A Fault Tolerance mechanism for Hybrid Scientific Workflows Proceedings Article
In: 1st workshop about High-Performance e-Science (HiPES), Madrid, Spain, 2024.
Abstract | BibTeX | Tags: eupex, icsc, streamflow
@inproceedings{24:madrid:hipes,
title = {A Fault Tolerance mechanism for Hybrid Scientific Workflows},
author = {Alberto Mulone and Doriana Medić and Marco Aldinucci},
year = {2024},
date = {2024-08-01},
booktitle = {1st workshop about High-Performance e-Science (HiPES)},
address = {Madrid, Spain},
abstract = {In large distributed systems, failures are a daily event occurring frequently, especially with growing numbers of computation tasks and locations on which they are deployed. The advantage of representing an application as a workflow is possibility to utilize the Workflow Management Systems which are reliable systems guaranteeing the correct execution of the application and providing the features such as portability, scalability, and fault tolerance. Over recent years, the emergence of hybrid workflows has posed new and intriguing challenges by increasing the possibility of distributing computations involving heterogeneous and independent environments. As a consequence, the number of possible points of failure in the execution augmented, creating different important challenges interesting to study.},
keywords = {eupex, icsc, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Massimo Torquati, Giacomo Corridori, Daniele Gregori, Francesco Polzella, Gianmarco Spinatelli, Marco Aldinucci
Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework Journal Article
In: The Journal of Supercomputing, vol. 80, no. 11, pp. 15799-15833, 2024, ISSN: 1573-0484.
Abstract | Links | BibTeX | Tags: analytics, icsc
@article{Antelmi_JSUPE_2024,
title = {Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework},
author = {Alessia Antelmi and Massimo Torquati and Giacomo Corridori and Daniele Gregori and Francesco Polzella and Gianmarco Spinatelli and Marco Aldinucci},
url = {https://doi.org/10.1007/s11227-024-06069-x},
doi = {10.1007/s11227-024-06069-x},
issn = {1573-0484},
year = {2024},
date = {2024-07-01},
journal = {The Journal of Supercomputing},
volume = {80},
number = {11},
pages = {15799-15833},
abstract = {The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {article}
}
Miruna Bețianu, Abele Mălan, Marco Aldinucci, Robert Birke, Lydia Chen
DALLMi: Domain Adaption for LLM-based Multi-label Classifier Proceedings Article
In: Yang, De-Nian, Xie, Xing, Tseng, Vincent S., Pei, Jian, Huang, Jen-Wei, Lin, Jerry Chun-Wei (Ed.): Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 277–289, Springer, Taipei, Taiwan, 2024.
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@inproceedings{24:betianu:llm,
title = {DALLMi: Domain Adaption for LLM-based Multi-label Classifier},
author = {Miruna Bețianu and Abele Mălan and Marco Aldinucci and Robert Birke and Lydia Chen},
editor = {De-Nian Yang and Xing Xie and Vincent S. Tseng and Jian Pei and Jen-Wei Huang and Jerry Chun-Wei Lin},
url = {https://hdl.handle.net/2318/1976672},
doi = {10.1007/978-981-97-2259-4_21},
year = {2024},
date = {2024-05-01},
booktitle = {Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining},
volume = {14647},
pages = {277–289},
publisher = {Springer},
address = {Taipei, Taiwan},
series = {Lecture Notes in Computer Science},
abstract = {Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten Tomatoes, adapting such an LLM-based multi-label classifier is challenging due to incomplete label sets at the target domain and daunting training overhead. The existing domain adaptation methods address either image multi-label classifiers or text binary classifiers. In this paper, we design DALLMi, Domain Adaptation Large Language Model interpolator, a first-of-its-kind semi-supervised domain adaptation method for text data models based on LLMs, specifically BERT. The core of DALLMi is the novel variation loss and MixUp regularization, which jointly leverage the limited positively labeled and large quantity of unlabeled text and, importantly, their interpolation from the BERT word embeddings. DALLMi also introduces a label-balanced sampling strategy to overcome the imbalance between labeled and unlabeled data. We evaluate DALLMi against the partial-supervised and unsupervised approach on three datasets under different scenarios of label availability for the target domain. Our results show that DALLMi achieves higher mAP than unsupervised and partially-supervised approaches by 19.9% and 52.2%, respectively.},
keywords = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Chi Hong, Robert Birke, Pin-Yu Chen, Lydia Chen
On Dark Knowledge for Distilling Generators Proceedings Article
In: Yang, De-Nian, Xie, Xing, Tseng, Vincent S., Pei, Jian, Huang, Jen-Wei, Lin, Jerry Chun-Wei (Ed.): Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 235–247, Springer, Taipei, Taiwan, 2024.
Abstract | Links | BibTeX | Tags: ai, epi, icsc
@inproceedings{24:chen:llm,
title = {On Dark Knowledge for Distilling Generators},
author = {Chi Hong and Robert Birke and Pin-Yu Chen and Lydia Chen},
editor = {De-Nian Yang and Xing Xie and Vincent S. Tseng and Jian Pei and Jen-Wei Huang and Jerry Chun-Wei Lin},
url = {https://hdl.handle.net/2318/1976671},
doi = {10.1007/978-981-97-2253-2_19},
year = {2024},
date = {2024-05-01},
booktitle = {Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining},
volume = {14646},
pages = {235–247},
publisher = {Springer},
address = {Taipei, Taiwan},
series = {Lecture Notes in Computer Science},
abstract = {Knowledge distillation has been applied on generative models, such as Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). To distill the knowledge, the synthetic outputs of a teacher generator are used to train a student model. While the dark knowledge, i.e., the probabilistic output, is well explored in distilling classifiers, little is known about the existence of an equivalent dark knowledge for generative models and its extractability. In this paper, we derive the first kind of empirical risk bound for distilling generative models from a Bayesian perspective. Through our analysis, we show the existence of the dark knowledge for generative models, i.e., Bayes probability distribution of a synthetic output from a given input, which achieves lower empirical risk bound than merely using the synthetic output of the generators. Furthermore, we propose a Dark Knowledge based Distillation , DKtill, which trains the student generator based on the (approximate) dark knowledge. Our extensive evaluation on distilling VAE, conditional GANs, and translation GANs on Facades and CelebA datasets show that the FID of student generators trained by DKtill combining dark knowledge are lower than student generators trained only by the synthetic outputs by up to 42.66%, and 78.99%, respectively.},
keywords = {ai, epi, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruno Casella, Iacopo Colonnelli, Gianluca Mittone, Robert Birke, Walter Riviera, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci
A Performance Analysis for Confidential Federated Learning Proceedings Article
In: Proceedings of the 2024 Deep Learning Security and Privacy Workshop, IEEE Symposium on Security and Privacy 2024, San Francisco, CA, 2024.
Abstract | Links | BibTeX | Tags: ai, confidential, epi, icsc
@inproceedings{24:casella:sgx,
title = {A Performance Analysis for Confidential Federated Learning},
author = {Bruno Casella and Iacopo Colonnelli and Gianluca Mittone and Robert Birke and Walter Riviera and Antonio Sciarappa and Carlo Cavazzoni and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/b5877a97-2d8d-4e95-8791-0aa4a1b953b3/DLSP___CONFIDENTIAL_FL.pdf},
doi = {10.1109/SPW63631.2024.00009},
year = {2024},
date = {2024-05-01},
booktitle = {Proceedings of the 2024 Deep Learning Security and Privacy Workshop, IEEE Symposium on Security and Privacy 2024},
address = {San Francisco, CA},
abstract = {Federated Learning (FL) has emerged as a solution to preserve data privacy by keeping the data locally on each participant's device. However, FL alone is still vulnerable to attacks that can cause privacy leaks. Therefore, it becomes necessary to take additional security measures at the cost of increasing runtimes. The Trusted Execution Environment (TEE) approach promises to offer the highest degree of security during execution. However, TEEs suffer from memory limits which prevent safe end-to-end FL training of modern deep models. State-of- the-art approaches limit secure training to selected layers, failing to avert the full spectrum of attacks or adopt layer-wise training affecting model performance. We benchmark the usage of a library OS (LibOS) to run the full, unmodified end-to-end FL training inside the TEE. We extensively evaluate and model the overhead of the different security mechanisms needed to protect the data and model during computation (TEE), communication (TLS), and storage (disk encryption). The obtained results across three datasets and two models demonstrate that LibOSes are a viable way to seamlessly inject security into FL with limited overhead (at most 2x), offering valuable guidance for researchers and developers aiming to apply FL in data-security-focused contexts.},
keywords = {ai, confidential, epi, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Giulio Malenza, Valentina Cesare, Marco Aldinucci, Ugo Becciani, Alberto Vecchiato
Toward HPC application portability via C++ PSTL: the Gaia AVU-GSR code assessment Journal Article
In: The Journal of Supercomputing, 2024, ISSN: 09208542.
Abstract | Links | BibTeX | Tags: eupex, HPC, icsc
@article{24:jsupe:Gaia,
title = {Toward HPC application portability via C++ PSTL: the Gaia AVU-GSR code assessment},
author = {Giulio Malenza and Valentina Cesare and Marco Aldinucci and Ugo Becciani and Alberto Vecchiato},
doi = {10.1007/s11227-024-06011-1},
issn = {09208542},
year = {2024},
date = {2024-03-01},
journal = {The Journal of Supercomputing},
publisher = {Springer},
abstract = {The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of $$sim10^8$$stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture.},
keywords = {eupex, HPC, icsc},
pubstate = {published},
tppubtype = {article}
}
Marco Edoardo Santimaria, Samuele Fonio, Giulio Malenza, Iacopo Colonnelli, Marco Aldinucci
Benchmarking Parallelization Models through Karmarkar Interior-point method Proceedings Article
In: Chis, Horacio González-Vélez Adriana E. (Ed.): Proc. of 32nd Euromicro intl. Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 1-8, IEEE, Dublin, Ireland, 2024, ISSN: 2377-5750.
Abstract | Links | BibTeX | Tags: HPC, icsc
@inproceedings{24:pdp:karmarkar,
title = {Benchmarking Parallelization Models through Karmarkar Interior-point method},
author = {Marco Edoardo Santimaria and Samuele Fonio and Giulio Malenza and Iacopo Colonnelli and Marco Aldinucci},
editor = {Horacio González-Vélez Adriana E. Chis},
url = {https://hdl.handle.net/2318/1964571},
doi = {10.1109/PDP62718.2024.00010},
issn = {2377-5750},
year = {2024},
date = {2024-03-01},
booktitle = {Proc. of 32nd Euromicro intl. Conference on Parallel, Distributed and Network-based Processing (PDP)},
pages = {1-8},
publisher = {IEEE},
address = {Dublin, Ireland},
abstract = {Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar's Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar's algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations},
keywords = {HPC, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci
Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios Journal Article
In: IEEE Access, vol. 12, pp. 47961-47971, 2024.
Links | BibTeX | Tags: epi, icsc
@article{24:casella:normalization,
title = {Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios},
author = {Bruno Casella and Roberto Esposito and Antonio Sciarappa and Carlo Cavazzoni and Marco Aldinucci},
doi = {10.1109/ACCESS.2024.3383783},
year = {2024},
date = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {47961-47971},
keywords = {epi, icsc},
pubstate = {published},
tppubtype = {article}
}
Lorenzo Brescia, Marco Aldinucci
Secure Generic Remote Workflow Execution with TEEs Proceedings Article
In: Proc. of the 2nd Workshop on Workflows in Distributed Environments (WiDE), pp. 8-13, ACM, Athens, Greece, 2024.
Abstract | Links | BibTeX | Tags: confidential, icsc
@inproceedings{23:brescia:wide,
title = {Secure Generic Remote Workflow Execution with TEEs},
author = {Lorenzo Brescia and Marco Aldinucci},
doi = {10.1145/3642978.3652834},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. of the 2nd Workshop on Workflows in Distributed Environments (WiDE)},
pages = {8-13},
publisher = {ACM},
address = {Athens, Greece},
abstract = {In scientific environments, the frequent need to process substantial volumes of data poses a common challenge. Individuals tasked with executing these computations frequently encounter a deficit in local computational resources, leading them to opt for the facilities of a Cloud Service Provider (CSP) for data processing. However, the data subjected to these calculations may be subject to confidentiality constraints. This paper introduces a proof-of-concept framework that leverages Gramine LibOS and Intel SGX, enabling the protection of generic remote workflow computations through SGX enclaves as Trusted Execution Environments (TEEs). The framework entails the delineation of user and CSP behavior and has been implemented using Bash scripts. Furthermore, an infrastructure has been designed for the Data Center Attestation Primitives (DCAP) remote attestation mechanism, wherein the user gains trust in the proper instantiation of the enclave within the CSP. To assess the framework efficacy, it has been tested on two distinct workflows, one trivial and the other involving real-world bioinformatics applications for processing DNA data. The performance study revealed that the framework incurred an acceptable overhead, ranging from a factor of x1.4 to x1.8 compared to unsafe execution practice.},
howpublished = {Proceedings of the 2nd Workshop on Workflows in Distributed Environments},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Simon Queyrut, Robert Birke, Pascal Felber, Valerio Schiavon
CLUES: Collusive Theft of Conditional Generative Adversarial Networks Proceedings Article
In: 43rd International Symposium on Reliable Distributed Systems SRDS, 2024.
@inproceedings{24:queyrut:srds,
title = {CLUES: Collusive Theft of Conditional Generative Adversarial Networks},
author = {Simon Queyrut and Robert Birke and Pascal Felber and Valerio Schiavon},
year = {2024},
date = {2024-01-01},
booktitle = {43rd International Symposium on Reliable Distributed Systems SRDS},
keywords = {ai, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Daniele De Vinco, Alessia Antelmi, Carmine Spagnuolo, Luca Maria Aiello
Deciphering Conversational Networks: Stance Detection via Hypergraphs and LLMs Proceedings Article
In: Companion Publication of the 16th ACM Web Science Conference, pp. 3–4, Association for Computing Machinery, Stuttgart, Germany, 2024, ISBN: 9798400704536.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_WebSci_2024,
title = {Deciphering Conversational Networks: Stance Detection via Hypergraphs and LLMs},
author = {Daniele De Vinco and Alessia Antelmi and Carmine Spagnuolo and Luca Maria Aiello},
url = {https://doi.org/10.1145/3630744.3658418},
doi = {10.1145/3630744.3658418},
isbn = {9798400704536},
year = {2024},
date = {2024-01-01},
booktitle = {Companion Publication of the 16th ACM Web Science Conference},
pages = {3–4},
publisher = {Association for Computing Machinery},
address = {Stuttgart, Germany},
series = {Websci Companion '24},
abstract = {Understanding the structural and linguistic properties of conversational data in social media is crucial for extracting meaningful insights to understand opinion dynamics, (mis-)information spreading, and the evolution of harmful behavior. Current state-of-the-art mathematical frameworks, such as hypergraphs and linguistic tools, such as large language models (LLMs), offer robust methodologies for modeling high-order group interactions and unprecedented capabilities for dealing with natural language-related tasks. In this study, we propose an innovative approach that blends these worlds by abstracting conversational networks via hypergraphs and analyzing their dynamics through LLMs. Our aim is to enhance the stance detection task by incorporating the high-order interactions naturally embedded within a conversation, thereby enriching the contextual understanding of LLMs regarding the intricate human dynamics underlying social media data.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Daniele De Vinco, Carmine Spagnuolo
HypergraphRepository: A Community-Driven and Interactive Hypernetwork Data Collection Proceedings Article
In: Dewar, Megan, Kamiński, Bogumił, Kaszyński, Daniel, Kraiński, Łukasz, Prałat, Paweł, Théberge, François, Wrzosek, Małgorzata (Ed.): Modelling and Mining Networks, pp. 159–173, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-59205-8.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_WAW_2024,
title = {HypergraphRepository: A Community-Driven and Interactive Hypernetwork Data Collection},
author = {Alessia Antelmi and Daniele De Vinco and Carmine Spagnuolo},
editor = {Megan Dewar and Bogumił Kamiński and Daniel Kaszyński and Łukasz Kraiński and Paweł Prałat and François Théberge and Małgorzata Wrzosek},
doi = {10.1007/978-3-031-59205-8_11},
isbn = {978-3-031-59205-8},
year = {2024},
date = {2024-01-01},
booktitle = {Modelling and Mining Networks},
pages = {159–173},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Hypergraph research has been thriving over the past few years, with a growing interest in a plethora of domains. Despite this remarkable surge, the lack of a comprehensive platform for searching and downloading diverse and well-curated datasets poses a significant obstacle to the continued advancement of the field. This absence hinders the ability of researchers and practitioners to validate and benchmark their hypergraph algorithms and models effectively.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Pasquale Caramante, Gennaro Cordasco, Giuseppe D'Ambrosio, Daniele De Vinco, Francesco Foglia, Luca Postiglione, Carmine Spagnuolo
Reliable and Efficient Agent-Based Modeling and Simulation Journal Article
In: Journal of Artificial Societies and Social Simulation, vol. 27, no. 2, pp. 4, 2024, ISSN: 1460-7425.
Abstract | Links | BibTeX | Tags: analytics, icsc
@article{Antelmi_JASSS_2024,
title = {Reliable and Efficient Agent-Based Modeling and Simulation},
author = {Alessia Antelmi and Pasquale Caramante and Gennaro Cordasco and Giuseppe D'Ambrosio and Daniele De Vinco and Francesco Foglia and Luca Postiglione and Carmine Spagnuolo},
url = {http://jasss.soc.surrey.ac.uk/27/2/4.html},
doi = {10.18564/jasss.5300},
issn = {1460-7425},
year = {2024},
date = {2024-01-01},
journal = {Journal of Artificial Societies and Social Simulation},
volume = {27},
number = {2},
pages = {4},
abstract = {Agent-based models represent a primary methodology to untangle and study complex systems. Over the last decade, the need for more elaborate computing-demanding models gave rise to many frameworks and tools to run ABM simulations. Current state-of-the-art ABM tools either focus on ease of use, performance, or a trade-off between these two elements. Still, efficiency-oriented solutions (required for both large and small-scale simulations) are vulnerable to memory flaws which could invalidate the experiment results. This work aims to merge efficiency, reliability, and safeness under an innovative ABM software framework based on the Rust programming language. Our framework, krABMaga, is an open-source library that offers a high-level environment by exploiting metaprogramming and expandable visualization features. We equipped our library with a dynamic simulation monitoring system and model exploration and optimization capabilities over parallel, distributed, and cloud architectures. After having presented the overall architecture and functionalities of krABMaga, we discuss a performance comparison of our framework against the mostly adopted ABM software and the scalability potential of our simulation engine on a model calibration experiment running over an AWS EC2 virtual cluster machine. All code and examples models are available on GitHub.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {article}
}
Raffaele Mineo, Federica Salanitri Proietto, Giovanni Bellitto, Isaak Kavasidis, Ovidio. De Filippo, Michele Millesimo, Gaetano Maria De Ferrari, Marco Aldinucci, Daniela Giordano, Simone Palazzo, Fabrizio D’Ascenzo, Concetto Spampinato
A Convolutional-Transformer Model for FFR and iFR Assessment from Coronary Angiography Journal Article
In: 2024.
Abstract | Links | BibTeX | Tags: ai
@article{angiography:TMI:24,
title = {A Convolutional-Transformer Model for FFR and iFR Assessment from Coronary Angiography},
author = {Raffaele Mineo and Federica Salanitri Proietto and Giovanni Bellitto and Isaak Kavasidis and Ovidio. De Filippo and Michele Millesimo and Gaetano Maria De Ferrari and Marco Aldinucci and Daniela Giordano and Simone Palazzo and Fabrizio D’Ascenzo and Concetto Spampinato},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582501},
doi = {10.1109/TMI.2024.3383283},
year = {2024},
date = {2024-01-01},
publisher = {IEEE},
abstract = {The quantification of stenosis severity from X-ray catheter angiography is a challenging task. Indeed, this requires to fully understand the lesion’s geometry by analyzing dynamics of the contrast material, only relying on visual observation by clinicians. To support decision making for cardiac intervention, we propose a hybrid CNN-Transformer model for the assessment of angiography-based non-invasive fractional flow-reserve (FFR) and instantaneous wave-free ratio (iFR) of intermediate coronary stenosis. Our approach predicts whether a coronary artery stenosis is hemodynamically significant and provides direct FFR and iFR estimates. This is achieved through a combination of regression and classification branches that forces the model to focus on the cut-off region of FFR (around 0.8 FFR value), which is highly critical for decision-making. We also propose a spatio-temporal factorization mechanisms that redesigns the transformer’s self-attention mechanism to capture both local spatial and temporal interactions between vessel geometry, blood flow dynamics, and lesion morphology. The proposed method achieves state-of-the-art performance on a dataset of 778 exams from 389 patients. Unlike existing methods, our approach employs a single angiography view and does not require knowledge of the key frame; supervision at training time is provided by a classification loss (based on a threshold of the FFR/iFR values) and a regression loss for direct estimation. Finally, the analysis of model interpretability and calibration shows that, in spite of the complexity of angiographic imaging data, our method can robustly identify the location of the stenosis and correlate prediction uncertainty to the provided output scores.},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Raffaele Mineo, Federica Salanitri Proietto, Giovanni Bellitto, Isaak Kavasidis, Ovidio. De Filippo, Michele Millesimo, Gaetano Maria De Ferrari, Marco Aldinucci, Daniela Giordano, Simone Palazzo, Fabrizio D'Ascenzo, Concetto Spampinato
A Convolutional-Transformer Model for FFR and iFR Assessment from Coronary Angiography Journal Article
In: IEEE Transaction on Medical Imaging, vol. 43, no. 8, pp. 2866-2877, 2024.
Abstract | Links | BibTeX | Tags: ai, cardio
@article{24:angiography:TMI,
title = {A Convolutional-Transformer Model for FFR and iFR Assessment from Coronary Angiography},
author = {Raffaele Mineo and Federica Salanitri Proietto and Giovanni Bellitto and Isaak Kavasidis and Ovidio. De Filippo and Michele Millesimo and Gaetano Maria De Ferrari and Marco Aldinucci and Daniela Giordano and Simone Palazzo and Fabrizio D'Ascenzo and Concetto Spampinato},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582501},
doi = {10.1109/TMI.2024.3383283},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transaction on Medical Imaging},
volume = {43},
number = {8},
pages = {2866-2877},
publisher = {IEEE},
abstract = {The quantification of stenosis severity from X-ray catheter angiography is a challenging task. Indeed, this requires to fully understand the lesion's geometry by analyzing dynamics of the contrast material, only relying on visual observation by clinicians. To support decision making for cardiac intervention, we propose a hybrid CNN-Transformer model for the assessment of angiography-based non-invasive fractional flow-reserve (FFR) and instantaneous wave-free ratio (iFR) of intermediate coronary stenosis. Our approach predicts whether a coronary artery stenosis is hemodynamically significant and provides direct FFR and iFR estimates. This is achieved through a combination of regression and classification branches that forces the model to focus on the cut-off region of FFR (around 0.8 FFR value), which is highly critical for decision-making. We also propose a spatio-temporal factorization mechanisms that redesigns the transformer's self-attention mechanism to capture both local spatial and temporal interactions between vessel geometry, blood flow dynamics, and lesion morphology. The proposed method achieves state-of-the-art performance on a dataset of 778 exams from 389 patients. Unlike existing methods, our approach employs a single angiography view and does not require knowledge of the key frame; supervision at training time is provided by a classification loss (based on a threshold of the FFR/iFR values) and a regression loss for direct estimation. Finally, the analysis of model interpretability and calibration shows that, in spite of the complexity of angiographic imaging data, our method can robustly identify the location of the stenosis and correlate prediction uncertainty to the provided output scores.},
keywords = {ai, cardio},
pubstate = {published},
tppubtype = {article}
}
Iacopo Colonnelli, Robert Birke, Giulio Malenza, Gianluca Mittone, Alberto Mulone, Jeroen Galjaard, Lydia Y. Chen, Sanzio Bassini, Gabriella Scipione, Jan Martinovič, Vit Vondrák, Marco Aldinucci
Cross-Facility Federated Learning Journal Article
In: Procedia Computer Science, vol. 240, pp. 3–12, 2024, ISSN: 1877-0509.
Abstract | Links | BibTeX | Tags: icsc, space, streamflow
@article{24:eurohpc:xffl,
title = {Cross-Facility Federated Learning},
author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Jeroen Galjaard and Lydia Y. Chen and Sanzio Bassini and Gabriella Scipione and Jan Martinovič and Vit Vondrák and Marco Aldinucci},
url = {https://www.sciencedirect.com/science/article/pii/S1877050924016909},
doi = {10.1016/j.procs.2024.07.003},
issn = {1877-0509},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the First EuroHPC user day},
journal = {Procedia Computer Science},
volume = {240},
pages = {3–12},
publisher = {Elsevier},
address = {Bruxelles, Belgium},
abstract = {In a decade, AI frontier research transitioned from the researcher's workstation to thousands of high-end hardware-accelerated compute nodes. This rapid evolution shows no signs of slowing down in the foreseeable future. While top cloud providers may be able to keep pace with this growth rate, obtaining and efficiently exploiting computing resources at that scale is a daunting challenge for universities and SMEs. This work introduces the Cross-Facility Federated Learning (XFFL) framework to bridge this compute divide, extending the opportunity to efficiently exploit multiple independent data centres for extreme-scale deep learning tasks to data scientists and domain experts. XFFL relies on hybrid workflow abstractions to decouple tasks from environment-specific technicalities, reducing complexity and enhancing reusability. In addition, Federated Learning (FL) algorithms eliminate the need to move large amounts of data between different facilities, reducing time-to-solution and preserving data privacy. The XFFL approach is empirically evaluated by training a full LLaMAv2 7B instance on two facilities of the EuroHPC JU, showing how the increased computing power completely compensates for the additional overhead introduced by two data centres.},
keywords = {icsc, space, streamflow},
pubstate = {published},
tppubtype = {article}
}
Emilio Sulis, Ilaria Angela Amantea, Marco Aldinucci, Guido Boella, Renata Marinello, Marco Grosso, Paolo Platter, Serena Ambrosini
An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective Journal Article
In: Journal of Ambient Intelligence and Humanized Computing, vol. 15, no. 5, pp. 2727-2735, 2024.
Abstract | Links | BibTeX | Tags: ai
@article{22:Sulis,
title = {An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective},
author = {Emilio Sulis and Ilaria Angela Amantea and Marco Aldinucci and Guido Boella and Renata Marinello and Marco Grosso and Paolo Platter and Serena Ambrosini},
url = {https://iris.unito.it/retrieve/c7eaab0b-f78b-4af0-8c17-fa5479d776e6/jaihc2021-preprint.pdf},
doi = {10.1007/s12652-022-04388-6},
year = {2024},
date = {2024-01-01},
journal = {Journal of Ambient Intelligence and Humanized Computing},
volume = {15},
number = {5},
pages = {2727-2735},
abstract = {The growing number of next-generation applications offers a relevant opportunity for healthcare services, generating an urgent need for architectures for systems integration. Moreover, the huge amount of stored information related to events can be explored by adopting a process-oriented perspective. This paper discusses an Ambient Assisted Living healthcare architecture to manage hospital home-care services. The proposed solution relies on adopting an event manager to integrate sources ranging from personal devices to web-based applications. Data are processed on a federated cloud platform offering computing infrastructure and storage resources to improve scientific research. In a second step, a business process analysis of telehealth and telemedicine applications is considered. An initial study explored the business process flow to capture the main sequences of tasks, activities, events. This step paves the way for the integration of process mining techniques to compliance monitoring in an AAL architecture framework.},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide Proceedings Article
In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 6534–6544, Association for Computing Machinery, Barcelona, Spain, 2024, ISBN: 9798400704901.
Abstract | Links | BibTeX | Tags: ai, analytics, icsc
@inproceedings{Antelmi_KDD_2024,
title = {A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide},
author = {Sunwoo Kim and Soo Yong Lee and Yue Gao and Alessia Antelmi and Mirko Polato and Kijung Shin},
url = {https://doi.org/10.1145/3637528.3671457},
doi = {10.1145/3637528.3671457},
isbn = {9798400704901},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {6534–6544},
publisher = {Association for Computing Machinery},
address = {Barcelona, Spain},
series = {KDD '24},
abstract = {Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.},
keywords = {ai, analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Lorenzo Brescia, Iacopo Colonnelli, Marco Aldinucci
Performance Analysis on DNA Alignment Workload with Intel SGX Multithreading Proceedings Article
In: Antelmi, Alessia, Carlini, Emanuele, Dazzi, Patrizio (Ed.): Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024, CEUR-WS.org, 2024.
Abstract | Links | BibTeX | Tags: confidential, icsc
@inproceedings{24:brescia:itadata,
title = {Performance Analysis on DNA Alignment Workload with Intel SGX Multithreading},
author = {Lorenzo Brescia and Iacopo Colonnelli and Marco Aldinucci},
editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},
url = {https://ceur-ws.org/Vol-3785/paper107.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},
volume = {3785},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {Data confidentiality is a critical issue in the digital age, impacting interactions between users and public services and between scientific computing organizations and Cloud and HPC providers. Performance in parallel computing is essential, yet techniques for establishing Trusted Execution Environments (TEEs) to ensure privacy in remote environments often negatively impact execution time. This paper aims to analyze the performance of a parallel bioinformatics workload for DNA alignment (Bowtie2) executed within the confidential enclaves of Intel SGX processors. The results provide encouraging insights regarding the feasibility of using SGX-based TEEs for parallel computing on large datasets. The findings indicate that, under conditions of high parallelization and with twice as many threads, workloads executed within SGX enclaves perform, on average, 15% faster than non-confidential execution. This empirical demonstration supports the potential of SGX-based TEEs to effectively balance the need for privacy with the demands of high-performance computing.},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Adriano Marques Garcia, Giulio Malenza, Robert Birke, Marco Aldinucci
Assessing Large Language Models Inference Performance on a 64-core RISC-V CPU with Silicon-Enabled Vectors Proceedings Article
In: Antelmi, Alessia, Carlini, Emanuele, Dazzi, Patrizio (Ed.): Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024, pp. 1-9, CEUR-WS.org, Pisa, Italy, 2024.
Abstract | Links | BibTeX | Tags: eupilot, icsc
@inproceedings{24:garcia:itadata,
title = {Assessing Large Language Models Inference Performance on a 64-core RISC-V CPU with Silicon-Enabled Vectors},
author = {Adriano Marques Garcia and Giulio Malenza and Robert Birke and Marco Aldinucci},
editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},
url = {https://iris.unito.it/retrieve/1540f675-5e88-4f57-95e7-df8e0fe5f1df/paper110.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},
volume = {3785},
pages = {1-9},
publisher = {CEUR-WS.org},
address = {Pisa, Italy},
series = {CEUR Workshop Proceedings},
abstract = {The rising usage of compute-intensive AI applications with fast response time requirements, such as text generation using large language models, underscores the need for more efficient and versatile hardware solutions. This drives the exploration of emerging architectures like RISC-V, which has the potential to deliver strong performance within tight power constraints. The recent commercial release of processors with RISC-V Vector (RVV) silicon-enabled extensions further amplifies the significance of RISC-V architectures, offering enhanced capabilities for parallel processing and accelerating tasks critical to large language models and other AI applications. This work aims to evaluate the BERT and GPT-2 language models inference performance on the SOPHON SG2042 64-core RISC-V architecture with silicon-enabled RVV v0.7.1. We benchmarked the models with and without RVV, using OpenBLAS and BLIS as BLAS backends for PyTorch to enable vectorization. Enabling RVV in OpenBLAS improved the inference performance by up to 40% in some cases.},
keywords = {eupilot, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Oussama Harrak, Bruno Casella, Samuele Fonio, Piero Fariselli, Gianluca Mittone, Tiziana Sanavia, Marco Aldinucci
Federated AdaBoost for Survival Analysis Proceedings Article
In: Proceedings of the ECML-PKDD Workshop, 2nd workshop on advancements in Federated Learning, Vilnius, Lithuania, 2024.
Abstract | BibTeX | Tags: epi, icsc
@inproceedings{harrak2024fedsurvboost,
title = {Federated AdaBoost for Survival Analysis},
author = {Oussama Harrak and Bruno Casella and Samuele Fonio and Piero Fariselli and Gianluca Mittone and Tiziana Sanavia and Marco Aldinucci},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the ECML-PKDD Workshop, 2nd workshop on advancements in Federated Learning},
address = {Vilnius, Lithuania},
abstract = {This work proposes FedSurvBoost, a federated learning pipeline for survival analysis based on the AdaBoost.F algorithm, which iteratively aggregates the best local weak hypotheses. Our method extends AdaBoost.F by removing the dependence on the number of classes coefficient from the computation of the weights of the best model. This makes it suitable for regression tasks, such as survival analysis. We show the effectiveness of our approach by comparing it with state-of-the-art methods, specifically developed for survival analysis problems, on two common survival datasets. Our code is available at https://github.com/oussamaHarrak/FedSurvBoost.},
keywords = {epi, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Zilong Zhao, Aditya Kunar, Robert Birke, Hiek Van Scheer, Lydia Y. Chen
CTAB-GAN+: enhancing tabular data synthesis Journal Article
In: Frontiers Big Data, vol. 6, 2024.
@article{24:fdata:zhao,
title = {CTAB-GAN+: enhancing tabular data synthesis},
author = {Zilong Zhao and Aditya Kunar and Robert Birke and Hiek Van Scheer and Lydia Y. Chen},
url = {https://doi.org/10.3389/fdata.2023.1296508},
doi = {10.3389/FDATA.2023.1296508},
year = {2024},
date = {2024-01-01},
journal = {Frontiers Big Data},
volume = {6},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Nur Zincir-Heywood, Robert Birke, Elias Bou-Harb, Takeru Inoue, Neeraj Kumar, Hanan Lutfiyya, Deepak Puthal, Abdallah Shami, Natalia Stakhanova
Guest Editorial: Special section on Networks, Systems, and Services Operations and Management Through Intelligence Journal Article
In: IEEE Trans. Netw. Serv. Manag., vol. 21, no. 3, pp. 2608–2612, 2024.
@article{24:tnsm:nur,
title = {Guest Editorial: Special section on Networks, Systems, and Services
Operations and Management Through Intelligence},
author = {Nur Zincir-Heywood and Robert Birke and Elias Bou-Harb and Takeru Inoue and Neeraj Kumar and Hanan Lutfiyya and Deepak Puthal and Abdallah Shami and Natalia Stakhanova},
url = {https://doi.org/10.1109/TNSM.2024.3416861},
doi = {10.1109/TNSM.2024.3416861},
year = {2024},
date = {2024-01-01},
journal = {IEEE Trans. Netw. Serv. Manag.},
volume = {21},
number = {3},
pages = {2608–2612},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Alessia Antelmi, Vincenzo Offertucci, Maria Angela Pellegrino
KGSnap! in Practice: a Block-based Programming Environment for Enabling Knowledge Graph Literacy Proceedings Article
In: Proceedings of the 9th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and Knowledge Graphs co-located with the 23rd International Semantic Web Conference (ISWC 2024), CEUR-WS.org, 2024.
Abstract | Links | BibTeX | Tags: analytics
@inproceedings{Antelmi_ISWCWrks_24,
title = {KGSnap! in Practice: a Block-based Programming Environment for Enabling Knowledge Graph Literacy},
author = {Alessia Antelmi and Vincenzo Offertucci and Maria Angela Pellegrino},
url = {https://ceur-ws.org/Vol-3773/paper6.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 9th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and Knowledge Graphs co-located with the 23rd International Semantic Web Conference (ISWC 2024)},
volume = {3773},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {The growing availability of (linked) open data requires lay users to master how to deal with data effectively, yet SPARQL presents a barrier to leveraging data represented as knowledge graphs. As the block programming paradigm has been successfully used to teach programming skills, we demonstrate how to use KGSnap!, an extension of the block-based programming environment Snap!, to foster knowledge graph literacy among individuals lacking expertise in query languages. This work mainly focuses on the visualization and interaction aspects of KGSnap!, a visual SPARQL query builder, when experienced by users without expertise in the Semantic Web technologies. The reported experience is discussed as a learning-by-doing protocol aimed at facilitating the reproducibility and transparency of the performed evaluation. KGSnap! ease of use has been verified by 14 Snap! experts and 24 high-school learners. The findings indicate that lay users perceived it as a promising approach to acquaint themselves with knowledge graphs.},
keywords = {analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Daniele De Vinco, Andrea Tranquillo, Alessia Antelmi, Carmine Spagnuolo, Vittorio Scarano
High-Performance Computation on a Rust-based distributed ABM engine Proceedings Article
In: Antelmi, Alessia, Carlini, Emanuele, Dazzi, Patrizio (Ed.): Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024, CEUR-WS.org, 2024.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_BigHPC_24,
title = {High-Performance Computation on a Rust-based distributed ABM engine},
author = {Daniele De Vinco and Andrea Tranquillo and Alessia Antelmi and Carmine Spagnuolo and Vittorio Scarano},
editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},
url = {https://ceur-ws.org/Vol-3785/paper124.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},
volume = {3785},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {An agent-based model (ABM) is a computational model for simulating autonomous agents' actions and interactions to understand a system's behavior and what governs its outcomes. When the data or number of agents grow or multiple runs are necessary, agent-based simulations are generally computationally costly. Therefore, adopting different computing paradigms, such as the distributed one, is essential to manage long-running simulations. The main problem with this approach is finding a way to distribute and balance the simulation field so that the agents can move from one machine to another with the least amount of synchronization overhead. Based on our experiences, we present a Rust-based ABM engine capable of distributing models on high-performance computing resources, gaining remarkable speedup against the sequential version.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Giulio Malenza, Valentina Cesare, Marco Edoardo Santimaria, Robert Birke, Alberto Vecchiato, Ugo Becciani, Marco Aldinucci
Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study Proceedings Article
In: SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1152-1163, IEEE, 2024, ISBN: 979-8-3503-5554-3.
Abstract | Links | BibTeX | Tags: eupex, icsc
@inproceedings{Malenza_P3HPC_24,
title = {Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study},
author = {Giulio Malenza and Valentina Cesare and Marco Edoardo Santimaria and Robert Birke and Alberto Vecchiato and Ugo Becciani and Marco Aldinucci},
url = {https://conferences.computer.org/sc-wpub/pdfs/SC-W2024-6oZmigAQfgJ1GhPL0yE3pS/555400b152/555400b152.pdf},
doi = {10.1109/SCW63240.2024.00157},
isbn = {979-8-3503-5554-3},
year = {2024},
date = {2024-01-01},
booktitle = {SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis},
pages = {1152-1163},
publisher = {IEEE},
abstract = {Applications that analyze data from modern scientific experiments will soon require a computing capacity of ExaFLOPs. The current trend to achieve such performance is to employ GPU-accelerated supercomputers and design applications to optimally exploit this hardware. Since each supercomputer is typically a one-off project, the necessity of having computational languages portable across diverse CPU and GPU architectures without performance losses is increasingly compelling. Here, we study the performance portability of the LSQR algorithm as found in the AVU-GSR code of the ESA Gaia mission. This code computes the astrometric parameters of the ~108 stars in our Galaxy. The LSQR algorithm is widely used across a broad range of high-performance computing (HPC) applications, elevating the study's relevance beyond the astrophysical domain. We developed different GPU-accelerated ports based on CUDA, C++ PSTL, SYCL, OpenMP, and HIP. We carefully verified the correctness of each port and tuned them to five different GPU-accelerated platforms from NVIDIA and AMD to evaluate the performance portability (PP) in terms of the harmonic mean of the application's performance efficiency across the tested hardware. HIP was demonstrated to be the most portable solution with a 0.94 average PP across the tested problem sizes, closely followed by SYCL coupled with AdaptiveCpp (ACPP) with 0.93. If we only consider NVIDIA platforms, CUDA would be the winner with 0.97. The tuning-oblivious C++ PSTL achieves 0.62 when coupled with vendor-specific compilers.},
keywords = {eupex, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruno Casella, Walter Riviera, Marco Aldinucci, Gloria Menegaz
Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis Journal Article
In: STAR Protocols, 2024, (https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf).
Abstract | Links | BibTeX | Tags: epi, icsc
@article{24:casella:starprotocol,
title = {Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis},
author = {Bruno Casella and Walter Riviera and Marco Aldinucci and Gloria Menegaz},
url = {https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf},
doi = {10.1016/j.xpro.2023.102812},
year = {2024},
date = {2024-01-01},
journal = {STAR Protocols},
institution = {Computer Science Department, University of Torino},
abstract = {Federated learning is a cooperative learning approach that has emerged as an effective way to address privacy concerns. Here, we present a protocol for training MERGE: a federated multi-input neural network (NN) for COVID-19 prognosis. We describe steps for collecting and preprocessing datasets. We then detail the process of training a multi-input NN. This protocol can be adapted for use with datasets containing both image- and table-based input sources.},
note = {https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf},
keywords = {epi, icsc},
pubstate = {published},
tppubtype = {article}
}
Adriano Marques Garcia, Dalvan Griebler, Claudio Schepke, José Daniel García, Javier Fernández Muñoz, Luiz Gustavo Fernandes
Performance and programmability of GrPPI for parallel stream processing on multi-cores Journal Article
In: The Journal of Supercomputing, vol. In press, no. In press, pp. 1-35, 2024, ISBN: 1573-0484.
Abstract | Links | BibTeX | Tags: admire
@article{GARCIA:JSuper:24,
title = {Performance and programmability of GrPPI for parallel stream processing on multi-cores},
author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and José Daniel García and Javier Fernández Muñoz and Luiz Gustavo Fernandes},
url = {https://iris.unito.it/retrieve/fff66640-fcbe-4080-a4f1-3279c9fadafb/s11227-024-05934-z.pdf},
doi = {10.1007/s11227-024-05934-z},
isbn = {1573-0484},
year = {2024},
date = {2024-01-01},
journal = {The Journal of Supercomputing},
volume = {In press},
number = {In press},
pages = {1-35},
publisher = {Springer},
abstract = {GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times.},
keywords = {admire},
pubstate = {published},
tppubtype = {article}
}
2023
Alberto Riccardo Martinelli, Massimo Torquati, Marco Aldinucci, Iacopo Colonnelli, Barbara Cantalupo
CAPIO: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows Proceedings Article
In: 2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC), IEEE, Goa, India, 2023.
Abstract | Links | BibTeX | Tags: admire, capio, eupex, icsc
@inproceedings{23:hipc:capio,
title = {CAPIO: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows},
author = {Alberto Riccardo Martinelli and Massimo Torquati and Marco Aldinucci and Iacopo Colonnelli and Barbara Cantalupo},
url = {https://iris.unito.it/retrieve/27380f37-0978-409e-a9d8-2b5e95a4bb85/CAPIO-HiPC23-preprint.pdf},
doi = {10.1109/HiPC58850.2023.00031},
year = {2023},
date = {2023-12-01},
booktitle = {2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)},
publisher = {IEEE},
address = {Goa, India},
abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to quickly expand, further exacerbating the performance gap between computing, memory, and storage technologies. This paper introduces CAPIO (Cross-Application Programmable I/O), a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. The contribution is twofold: 1) at design time, a new I/O coordination language allows users to annotate workflow data dependencies with synchronization semantics; 2) at run time, a user-space middleware automatically and transparently to the user turns a workflow batch execution into a streaming execution according to the semantics expressed in the configuration file. CAPIO has been tested on synthetic benchmarks simulating typical workflow I/O patterns and two real-world workflows. Experiments show that CAPIO reduces the execution time by 10% to 66% for data-intensive workflows that use the file system as a communication medium.},
keywords = {admire, capio, eupex, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Marco Aldinucci, Elena Maria Baralis, Valeria Cardellini, Iacopo Colonnelli, Marco Danelutto, Sergio Decherchi, Giuseppe Di Modica, Luca Ferrucci, Marco Gribaudo, Francesco Iannone, Marco Lapegna, Doriana Medic, Giuseppa Muscianisi, Francesca Righetti, Eva Sciacca, Nicola Tonellotto, Mauro Tortonesi, Paolo Trunfio, Tullio Vardanega
A Systematic Mapping Study of Italian Research on Workflows Proceedings Article
In: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, SC-W 2023, pp. 2065–2076, ACM, Denver, CO, USA, 2023.
Abstract | Links | BibTeX | Tags: icsc, jupyter-workflow, streamflow
@inproceedings{WORKS2023,
title = {A Systematic Mapping Study of Italian Research on Workflows},
author = {Marco Aldinucci and Elena Maria Baralis and Valeria Cardellini and Iacopo Colonnelli and Marco Danelutto and Sergio Decherchi and Giuseppe Di Modica and Luca Ferrucci and Marco Gribaudo and Francesco Iannone and Marco Lapegna and Doriana Medic and Giuseppa Muscianisi and Francesca Righetti and Eva Sciacca and Nicola Tonellotto and Mauro Tortonesi and Paolo Trunfio and Tullio Vardanega},
url = {https://doi.org/10.1145/3624062.3624285},
doi = {10.1145/3624062.3624285},
year = {2023},
date = {2023-11-01},
booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, SC-W 2023},
pages = {2065–2076},
publisher = {ACM},
address = {Denver, CO, USA},
abstract = {An entire ecosystem of methodologies and tools revolves around scientific workflow management. They cover crucial non-functional requirements that standard workflow models fail to target, such as interactive execution, energy efficiency, performance portability, Big Data management, and intelligent orchestration in the Computing Continuum. Characterizing and monitoring this ecosystem is crucial to develop an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, collecting and analyzing 25 tools and 10 applications from several scientific domains in the context of the ``National Research Centre for HPC, Big Data, and Quantum Computing'' (ICSC). The study aims to outline the main current research directions and determine how they address the critical needs of modern scientific applications. The findings highlight a variegated research ecosystem of tools, with a prominent interest in advanced workflow orchestration and still immature but promising efforts toward energy efficiency.},
keywords = {icsc, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Zilong Zhao, Robert Birke, Lydia Y. Chen
FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform Proceedings Article
In: 32nd ACM International Conference on Information and Knowledge Management (CIKM '23), ACM, Birmingham, United Kingdom, 2023.
Abstract | Links | BibTeX | Tags: icsc
@inproceedings{23:zhao:fctgan,
title = {FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform},
author = {Zilong Zhao and Robert Birke and Lydia Y. Chen},
url = {https://iris.unito.it/retrieve/966ba767-dbbd-41e1-b4e3-7ab7ba09303f/FCT-GAN.pdf},
doi = {10.1145/3583780.3615202},
year = {2023},
date = {2023-10-01},
booktitle = {32nd ACM International Conference on Information and Knowledge Management (CIKM '23)},
publisher = {ACM},
address = {Birmingham, United Kingdom},
abstract = {An alternative method for sharing knowledge while complying with strict data access regulations, such as the European General Data Protection Regulation (GDPR), is the emergence of synthetic tabular data. Mainstream table synthesizers utilize methodologies derived from Generative Adversarial Networks (GAN). Although several state-of-the-art (SOTA) tabular GAN algorithms inherit Convolutional Neural Network (CNN)-based architectures, which have proven effective for images, they tend to overlook two critical properties of tabular data: (i) the global correlation across columns, and (ii) the semantic invariance to the column order. Permuting columns in a table does not alter the semantic meaning of the data, but features extracted by CNNs can change significantly due to their limited convolution filter kernel size. To address the above problems, we propose FCT-GAN– the first conditional tabular GAN to adopt Fourier networks into table synthesis. FCT-GAN enhances permutation invariant GAN training by strengthening the learning of global correlations via Fourier layers. Extensive evaluation on benchmarks and real-world datasets show that FCT-GAN can synthesize tabular data with better (up to 27.8%) machine learning utility (i.e. a proxy of global correlations) and higher (up to 26.5%) statistical similarity to real data. FCT-GAN also has the least variation on synthetic data quality among 7 SOTA baselines on 3 different training-data column orders.},
keywords = {icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Samuele Fonio, Lorenzo Paletto, Mattia Cerrato, Dino Ienco, Roberto Esposito
Hierarchical priors for Hyperspherical Prototypical Networks Proceedings Article
In: 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, Bruges, Belgium, 2023, (In print).
Abstract | Links | BibTeX | Tags: ai, icsc
@inproceedings{23:esann:fonio,
title = {Hierarchical priors for Hyperspherical Prototypical Networks},
author = {Samuele Fonio and Lorenzo Paletto and Mattia Cerrato and Dino Ienco and Roberto Esposito},
url = {https://www.esann.org/sites/default/files/proceedings/2023/ES2023-65.pdf},
year = {2023},
date = {2023-10-01},
booktitle = {31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN},
address = {Bruges, Belgium},
abstract = {In this paper, we explore the usage of hierarchical priors to improve learning in contexts where the number of available examples is extremely low. Specifically, we consider a Prototype Learning setting where deep neural networks are used to embed data in hyperspherical geometries.In this scenario, we propose an innovative way to learn the prototypes by combining class separation and hierarchical information. In addition, we introduce a contrastive loss function capable of balancing the exploitation of prototypes through a prototype pruning mechanism. We compare the proposed method with state-of-the-art approaches on two public datasets.},
note = {In print},
keywords = {ai, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Samuele Fonio
Benchmarking Federated Learning Frameworks for Medical Imaging Tasks Proceedings Article
In: Foresti, G. L., Fusiello, A., Hancock, E. (Ed.): Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023, Springer, Cham, Udine, Italy, 2023, (In print).
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@inproceedings{23:iciap:fedmed:ws:fonio,
title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},
author = {Samuele Fonio},
editor = {G. L. Foresti and A. Fusiello and E. Hancock},
url = {https://link.springer.com/chapter/10.1007/978-3-031-51026-7_20},
doi = {10.1007/978-3-031-51026-7_20},
year = {2023},
date = {2023-09-01},
booktitle = {Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023},
volume = {14366},
publisher = {Springer, Cham},
address = {Udine, Italy},
abstract = {This paper presents a comprehensive benchmarking study of various Federated Learning (FL) frameworks applied to the task of Medical Image Classification. The research specifically addresses the often neglected and complex aspects of scalability and usability in off-the-shelf FL frameworks. Through experimental validation using real case deployments, we provide empirical evidence of the performance and practical relevance of open source FL frameworks. Our findings contribute valuable insights for anyone interested in deploying a FL system, with a particular focus on the healthcare domain—an increasingly attractive field for FL applications.},
note = {In print},
keywords = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Gianluca Mittone, Samuele Fonio
Benchmarking Federated Learning Scalability Proceedings Article
In: Proceedings of the 2nd Italian Conference on Big Data and Data Science, ITADATA 2023, September 11-13, 2023, CEUR, Naples, Italy, 2023.
Abstract | Links | BibTeX | Tags: eupilot, HPC, icsc
@inproceedings{23:itadata:extabstract:mittone:fonio,
title = {Benchmarking Federated Learning Scalability},
author = {Gianluca Mittone and Samuele Fonio},
url = {https://hdl.handle.net/2318/1933852},
year = {2023},
date = {2023-09-01},
booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science, ITADATA 2023, September 11-13, 2023},
publisher = {CEUR},
address = {Naples, Italy},
abstract = {Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.},
keywords = {eupilot, HPC, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Chi Hong, Jiyue Huang, Robert Birke, Lydia Y. Chen
Exploring and Exploiting Data-Free Model Stealing Proceedings Article
In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 2023.
Abstract | Links | BibTeX | Tags: eupilot, icsc
@inproceedings{23:hong:datafree,
title = {Exploring and Exploiting Data-Free Model Stealing},
author = {Chi Hong and Jiyue Huang and Robert Birke and Lydia Y. Chen},
url = {https://iris.unito.it/retrieve/ce44dec6-12c9-443d-99e7-f1141e50aa3a/Data-free%20Model%20Stealing.pdf},
doi = {10.1007/978-3-031-43424-2_2},
year = {2023},
date = {2023-09-01},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},
address = {Turin, Italy},
abstract = {Deep machine learning models, e.g., image classifier, are increasingly deployed in the wild to provide services to users. Adversaries are shown capable of stealing the knowledge of these models by sending inference queries and then training substitute models based on query results. The availability and quality of adversarial query inputs are undoubtedly crucial in the stealing process. The recent prior art demonstrates the feasibility of replacing real data by exploring the synthetic adversarial queries, so called data-free attacks, under strong adversarial assumptions, i.e., the deployed classier returns not only class labels but also class probabilities. In this paper, we consider a general adversarial model and propose an effective data-free stealing algorithm, Tandem-GAN, which not only explores synthetic queries but also explicitly exploits the high quality ones. The core of TandemGAN is composed of (i) substitute model which imitates the target model through synthetic queries and their inferred labels; and (ii) a tandem generator consisting of two networks, Gx and Ge, which first explores the synthetic data space via Gx and then exploits high-quality examples via Ge to maximize the knowledge transfer from the target to the substitute model. Our results on four datasets show that the accuracy of our trained substitute model ranges between 96-67% of the target model and outperforms the existing state-of-the-art data-free model stealing approach by up to 2.5X.},
keywords = {eupilot, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Valentina Cesare, Ugo Becciani, Alberto Vecchiato, Mario Gilberto Lattanzi, Fabio Pitari, Marco Aldinucci, Beatrice Bucciarelli
The MPI + CUDA Gaia AVU–GSR Parallel Solver Toward Next-generation Exascale Infrastructures Journal Article
In: Publications of the Astronomical Society of the Pacific, vol. 135, no. 1049, 2023.
Abstract | Links | BibTeX | Tags: HPC
@article{23:GAIAMPI_PASP,
title = {The MPI + CUDA Gaia AVU–GSR Parallel Solver Toward Next-generation Exascale Infrastructures},
author = {Valentina Cesare and Ugo Becciani and Alberto Vecchiato and Mario Gilberto Lattanzi and Fabio Pitari and Marco Aldinucci and Beatrice Bucciarelli},
url = {https://iopscience.iop.org/article/10.1088/1538-3873/acdf1e/pdf},
doi = {10.1088/1538-3873/acdf1e},
year = {2023},
date = {2023-08-01},
journal = {Publications of the Astronomical Society of the Pacific},
volume = {135},
number = {1049},
abstract = {We ported to the GPU with CUDA the Astrometric Verification Unit–Global Sphere Reconstruction (AVU–GSR) Parallel Solver developed for the ESA Gaia mission, by optimizing a previous OpenACC porting of this application. The code aims to find, with a [10, 100] μarcsec precision, the astrometric parameters of about 10^8 stars, the attitude and instrumental settings of the Gaia satellite, and the global parameter γ of the parametrized Post-Newtonian formalism, by solving a system of linear equations, A × x = b, with the LSQR iterative algorithm. The coefficient matrix A of the final Gaia data set is large, with ∼1011 × 108 elements, and sparse, reaching a size of ∼10–100 TB, typical for the Big Data analysis, which requires an efficient parallelization to obtain scientific results in reasonable timescales. The speedup of the CUDA code over the original AVU–GSR solver, parallelized on the CPU with MPI + OpenMP, increases with the system size and the number of resources, reaching a maximum of ∼14×, >9× over the OpenACC application. This result is obtained by comparing the two codes on the CINECA cluster Marconi100, with 4 V100 GPUs per node. After verifying the agreement between the solutions of a set of systems with different sizes computed with the CUDA and the OpenMP codes and that the solutions showed the required precision, the CUDA code was put in production on Marconi100, essential for an optimal AVU–GSR pipeline and the successive Gaia Data Releases. This analysis represents a first step to understand the (pre-)Exascale behavior of a class of applications that follow the same structure of this code. In the next months, we plan to run this code on the pre-Exascale platform Leonardo of CINECA, with 4 next-generation A200 GPUs per node, toward a porting on this infrastructure, where we expect to obtain even higher performances.},
key = {icsc, eupex},
keywords = {HPC},
pubstate = {published},
tppubtype = {article}
}
Gianluca Mittone, Walter Riviera, Iacopo Colonnelli, Robert Birke, Marco Aldinucci
Model-Agnostic Federated Learning Proceedings Article
In: Euro-Par 2023: Parallel Processing, pp. 383–396, Springer, Limassol, Cyprus, 2023.
Abstract | Links | BibTeX | Tags: ai, confidential, eupilot, icsc, riscv
@inproceedings{23:mittone:mafl,
title = {Model-Agnostic Federated Learning},
author = {Gianluca Mittone and Walter Riviera and Iacopo Colonnelli and Robert Birke and Marco Aldinucci},
url = {https://doi.org/10.1007/978-3-031-39698-4_26},
doi = {10.1007/978-3-031-39698-4_26},
year = {2023},
date = {2023-08-01},
booktitle = {Euro-Par 2023: Parallel Processing},
volume = {14100},
pages = {383–396},
publisher = {Springer},
address = {Limassol, Cyprus},
institution = {Computer Science Department, University of Torino},
abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. We test MAFL from multiple points of view, assessing its correctness, flexibility and scaling properties up to 64 nodes. We optimised the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},
keywords = {ai, confidential, eupilot, icsc, riscv},
pubstate = {published},
tppubtype = {inproceedings}
}
Zilong Zhao, Robert Birke, Lydia Y. Chen
GDTS: GAN-based Distributed Tabular Synthesizer Proceedings Article
In: 16th IEEE International Conference on Cloud Computing (CLOUD), IEEE, Chicago, USA, 2023.
Abstract | Links | BibTeX | Tags: ai
@inproceedings{23:cloud:gdts,
title = {GDTS: GAN-based Distributed Tabular Synthesizer},
author = {Zilong Zhao and Robert Birke and Lydia Y. Chen},
url = {https://iris.unito.it/retrieve/8bc610de-3ccd-4a0a-b97f-ee329e487b76/GDTS_IEEE_CLOUD_preprint.pdf},
doi = {10.1109/CLOUD60044.2023.00078},
year = {2023},
date = {2023-07-01},
booktitle = {16th IEEE International Conference on Cloud Computing (CLOUD)},
publisher = {IEEE},
address = {Chicago, USA},
abstract = {Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data. While learning image GANs on Federated Learning (FL) and Multi-Discriminator (MD) systems has just been demonstrated, it is unknown if tabular GANs can be learned from decentralized data sources. Different from image GANs, state-of-the-art tabular GANs require prior knowledge on the data distribution of each (discrete and continuous) column to agree on a common encoding – risking privacy guarantees. In this paper, we propose GDTS, a distributed framework for GAN-based tabular synthesizer. GDTS provides different system architectures to match the two training paradigms termed GDTS FL and GDTS MD. Key to enable learning on distributed data is the proposed novel privacy-preserving multi-source feature encoding to capture the global data properties. In addition GDTS encompasses a weighting strategy based on table similarity to counter the detrimental effects of non-IID data and a validation pipeline to easily assess and compare the performance of different paradigms and hyper parameters. We evaluate the effectiveness of GDTS in terms of synthetic data quality, and overall training scalability. Experiments show that GDTS FL achieves better statistical similarity and machine learning utility between generated and original data compared to GDTS MD.},
keywords = {ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Iacopo Colonnelli, Robert Birke, Marco Aldinucci
Experimenting with PyTorch on RISC-V Proceedings Article
In: RISC-V Summit Europe 2023, Barcelona, Spain, 2023, (Poster).
Abstract | Links | BibTeX | Tags: eupilot, icsc, riscv
@inproceedings{23:risc-v-summit,
title = {Experimenting with PyTorch on RISC-V},
author = {Iacopo Colonnelli and Robert Birke and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/429bf344-9090-42c3-809c-1b8ac320a930/2023-06-08-Iacopo-COLONNELLI-abstract.pdf},
year = {2023},
date = {2023-06-01},
booktitle = {RISC-V Summit Europe 2023},
address = {Barcelona, Spain},
abstract = {RISC-V is an emerging instruction set architecture. Its modular and extensible open-source royalty-free design is increasingly attracting interest from both research and industry. Nowadays, different RISC-V-based boards can be bought off the shelf. However, software availability is equivalently vital in guaranteeing the RISC-V ecosystem's success. Here we contribute with the first publicly available port of PyTorch. PyTorch is one of the most popular Deep Learning libraries available today. As such, it is a crucial enabler in running state-of-the-art AI applications on RISC-V-based systems and a first step towards a fully democratic end-to-end codesign process.},
note = {Poster},
keywords = {eupilot, icsc, riscv},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, Dingqi Yang
A Survey on Hypergraph Representation Learning Journal Article
In: ACM Comput. Surv., 2023, ISSN: 0360-0300.
Abstract | Links | BibTeX | Tags: analytics
@article{Antelmi_CSUR_23,
title = {A Survey on Hypergraph Representation Learning},
author = {Alessia Antelmi and Gennaro Cordasco and Mirko Polato and Vittorio Scarano and Carmine Spagnuolo and Dingqi Yang},
url = {https://doi.org/10.1145/3605776},
doi = {10.1145/3605776},
issn = {0360-0300},
year = {2023},
date = {2023-06-01},
journal = {ACM Comput. Surv.},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects - most commonly nodes - of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.},
keywords = {analytics},
pubstate = {published},
tppubtype = {article}
}
Marco Aldinucci, Robert Birke, Antonio Brogi, Emanuele Carlini, Massimo Coppola, Marco Danelutto, Patrizio Dazzi, Luca Ferrucci, Forti Stefano, Hanna Kavalionak, Gabriele Mencagli, Matteo Mordacchin, Marcelo Pasin, Federica Paganelli, Massimo Torquati
A Proposal for a Continuum-aware Programming Model: From Workflows to Services Autonomously Interacting in the Compute Continuum Proceedings Article
In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, Turin, Italy, 2023.
Abstract | Links | BibTeX | Tags: icsc
@inproceedings{23:aldinucci:continuum,
title = {A Proposal for a Continuum-aware Programming Model: From Workflows to Services Autonomously Interacting in the Compute Continuum},
author = {Marco Aldinucci and Robert Birke and Antonio Brogi and Emanuele Carlini and Massimo Coppola and Marco Danelutto and Patrizio Dazzi and Luca Ferrucci and Forti Stefano and Hanna Kavalionak and Gabriele Mencagli and Matteo Mordacchin and Marcelo Pasin and Federica Paganelli and Massimo Torquati},
url = {https://iris.unito.it/retrieve/2ae13a33-5814-43da-8ea6-2d3e8b122384/Continuum-aware-PM.pdf},
doi = {10.1109/COMPSAC57700.2023.00287},
year = {2023},
date = {2023-06-01},
booktitle = {2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)},
publisher = {IEEE},
address = {Turin, Italy},
abstract = {This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A meta-model introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.},
keywords = {icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Jani Valtari, Anna Kulmala, Sandro Schönborn, David Khozaya, Robert Birke, Reikko Jyrki
Real-life Pilot of Virtual Protection and Control - Experiences and Performance Analysis Proceedings Article
In: 27th International Conference on Electricity Distribution (CIRED), Rome, Italy, 2023.
Abstract | Links | BibTeX | Tags: RT
@inproceedings{23:valtari:pilot,
title = {Real-life Pilot of Virtual Protection and Control - Experiences and Performance Analysis},
author = {Jani Valtari and Anna Kulmala and Sandro Schönborn and David Khozaya and Robert Birke and Reikko Jyrki},
url = {https://iris.unito.it/retrieve/5de5fb00-02bf-4ba8-a4db-5876415d5105/virtualization_full_paper_cired2023_submitted.pdf},
doi = {10.1049/icp.2023.1219},
year = {2023},
date = {2023-06-01},
booktitle = {27th International Conference on Electricity Distribution (CIRED)},
address = {Rome, Italy},
abstract = {Virtualized protection and control (VPC) is seen as a promising evolution for the centralized protection and control (CPC) concept. Centralization of protection functions consolidates the functions of multiple traditional relays into one device. This consolidation reduces communications network complexity and offers effective ways to manage protection applications of the substation. Making the CPC available as a VPC software image instead of a dedicated device creates yet another degree of freedom. The solution becomes hardware independent, bringing more flexibility and scalability to the solution. ABB and Caruna together wanted to explore these possibilities in a real-life substation pilot. This paper describes the piloted VPC environment and the results from the piloting period. The results show that virtualization technology is suitable for time critical protection and control applications, with real-time performance comparable to existing non- virtualized solutions.},
keywords = {RT},
pubstate = {published},
tppubtype = {inproceedings}
}
Sandro Schönborn, Robert Birke, David Kozhaya, Thanikesavan Sivanthi
Real-Time Performance of Virtualised Protection and Control Software Proceedings Article
In: 27th International Conference on Electricity Distribution (CIRED), Rome, Italy, 2023.
Abstract | Links | BibTeX | Tags: RT
@inproceedings{23:schoenborn:vipac,
title = {Real-Time Performance of Virtualised Protection and Control Software},
author = {Sandro Schönborn and Robert Birke and David Kozhaya and Thanikesavan Sivanthi},
url = {https://iris.unito.it/retrieve/eb610327-6e38-4f5e-8673-e62f2d956821/10702-Scho%cc%88nborn.pdf},
doi = {10.1049/icp.2023.1028},
year = {2023},
date = {2023-06-01},
booktitle = {27th International Conference on Electricity Distribution (CIRED)},
address = {Rome, Italy},
abstract = {Substation automation is ever challenged by the integration of distributed energy resources which imposes higher deployment flexibility and adaptability for protection and control. Although virtualization helps to run software applications independent of the underlying platform in IT infrastructures and cloud computing, it is still not commonly used in the field of substation automation. This is mainly due to the real-time performance demands of substation automation protection and control applications. In this article, we present an approach for running substation automation protection and control software in virtual environments. We contrast the real-time performance of different virtualization technologies under different workloads and focus on the performance evaluation of protection and control software in container- based solutions running on Linux with PREEMPT RT. We also present additional results for performance achieved in virtual machines. Our results clearly demonstrate that it is possible to run substation automation protection and control software in virtual environments while still providing the necessary performance. This paves the way for the deployment of substation protection and control software in virtualisation environments.},
keywords = {RT},
pubstate = {published},
tppubtype = {inproceedings}
}
Jesus Carretero, Javier Garcia-Blas, Marco Aldinucci, Jean Baptiste Besnard Besnard, Jean-Thomas Acquaviva, André Brinkmann, Marc-André Vef, Emmanuel Jeannot, Alberto Miranda, Ramon Nou, Morris Riedel, Massimo Torquati, Felix Wolf
Adaptive multi-tier intelligent data manager for Exascale Proceedings Article
In: 20th ACM International Conference on Computing Frontiers (CF '23), ACM, Bologna, Italy, 2023.
Abstract | Links | BibTeX | Tags: admire
@inproceedings{23:admire:cf,
title = {Adaptive multi-tier intelligent data manager for Exascale},
author = {Jesus Carretero and Javier Garcia-Blas and Marco Aldinucci and Jean Baptiste Besnard Besnard and Jean-Thomas Acquaviva and André Brinkmann and Marc-André Vef and Emmanuel Jeannot and Alberto Miranda and Ramon Nou and Morris Riedel and Massimo Torquati and Felix Wolf},
url = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592174},
doi = {10.1145/3587135.3592174},
year = {2023},
date = {2023-05-01},
booktitle = {20th ACM International Conference on Computing Frontiers (CF '23)},
publisher = {ACM},
address = {Bologna, Italy},
abstract = {The main objective of the ADMIRE project1 is the creation of an active I/O stack that dynamically adjusts computation and storage requirements through intelligent global coordination, the elasticity of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy, while offering quality-of-service (QoS), energy efficiency, and resilience for accessing extremely large data sets in very heterogeneous computing and storage environments. We have developed a framework prototype that is able to dynamically adjust computation and storage requirements through intelligent global coordination, separated control, and data paths, the malleability of computation and I/O, the scheduling of storage resources along all levels of the storage hierarchy, and scalable monitoring techniques. The leading idea in ADMIRE is to co-design applications with ad-hoc storage systems that can be deployed with the application and adapt their computing and I/O behaviour on runtime, using malleability techniques, to increase the performance of applications and the throughput of the applications.},
keywords = {admire},
pubstate = {published},
tppubtype = {inproceedings}
}
Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medić, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning Proceedings Article
In: 20th ACM International Conference on Computing Frontiers (CF '23), ACM, Bologna, Italy, 2023, ISBN: 979-8-4007-0140-5/23/05, (https://arxiv.org/abs/2302.07946).
Abstract | Links | BibTeX | Tags: ai, confidential, eupilot, HPC, icsc, riscv
@inproceedings{23:mittone:fl-riscv,
title = {Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning},
author = {Gianluca Mittone and Nicolò Tonci and Robert Birke and Iacopo Colonnelli and Doriana Medić and Andrea Bartolini and Roberto Esposito and Emanuele Parisi and Francesco Beneventi and Mirko Polato and Massimo Torquati and Luca Benini and Marco Aldinucci},
url = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592211},
doi = {10.1145/3587135.3592211},
isbn = {979-8-4007-0140-5/23/05},
year = {2023},
date = {2023-05-01},
booktitle = {20th ACM International Conference on Computing Frontiers (CF '23)},
publisher = {ACM},
address = {Bologna, Italy},
institution = {Computer Science Department, University of Torino},
abstract = {Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on two emerging architectures (ARM-v8, RISC-V) and the x86-64 platform. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},
note = {https://arxiv.org/abs/2302.07946},
keywords = {ai, confidential, eupilot, HPC, icsc, riscv},
pubstate = {published},
tppubtype = {inproceedings}
}
Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lio
A Federated Learning Benchmark for Drug-Target Interaction Proceedings Article
In: Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion), ACM, Austin, Texas, 2023, ISBN: 978-1-4503-9419-2/23/04, (https://arxiv.org/abs/2302.07684).
Abstract | Links | BibTeX | Tags: ai, confidential, eupilot, icsc
@inproceedings{23:mittone:dti,
title = {A Federated Learning Benchmark for Drug-Target Interaction},
author = {Gianluca Mittone and Filip Svoboda and Marco Aldinucci and Nicholas D. Lane and Pietro Lio},
url = {https://hdl.handle.net/2318/1898472},
doi = {10.1145/3543873.3587687},
isbn = {978-1-4503-9419-2/23/04},
year = {2023},
date = {2023-04-01},
booktitle = {Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion)},
publisher = {ACM},
address = {Austin, Texas},
institution = {Computer Science Department, University of Torino},
abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which we argue to be reconcilable with the industry's constraints, as it does not require sharing of any information that would reveal the entities' data or any other high-level summary of it. When used on a representative GraphDTA model and the KIBA dataset it achieves up to 15 percent improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data, and the cost of adding more clients.},
note = {https://arxiv.org/abs/2302.07684},
keywords = {ai, confidential, eupilot, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Adriano Marques Garcia, Dalvan Griebler, Claudio Schepke, André Sacilotto Santos, José Daniel García, Javier Fernández Muñoz, Luiz Gustavo Fernandes
A Latency, Throughput, and Programmability Perspective of GrPPI for Streaming on Multi-cores Proceedings Article
In: 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 164-168, IEEE, Naples, Italy, 2023.
Abstract | Links | BibTeX | Tags: admire
@inproceedings{GARCIA:PDP:23,
title = {A Latency, Throughput, and Programmability Perspective of GrPPI for Streaming on Multi-cores},
author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and André Sacilotto Santos and José Daniel García and Javier Fernández Muñoz and Luiz Gustavo Fernandes},
url = {https://iris.unito.it/retrieve/9165d2ef-7140-4645-87cc-269050341c1d/PDP_2023_SPbench_with_GrPPI.pdf},
doi = {10.1109/PDP59025.2023.00033},
year = {2023},
date = {2023-03-01},
booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
pages = {164-168},
publisher = {IEEE},
address = {Naples, Italy},
series = {PDP'23},
abstract = {Several solutions aim to simplify the burdening task of parallel programming. The GrPPI library is one of them. It allows users to implement parallel code for multiple backends through a unified, abstract, and generic layer while promising minimal overhead on performance. An outspread evaluation of GrPPI regarding stream parallelism with representative metrics for this domain, such as throughput and latency, was not yet done. In this work, we evaluate GrPPI focused on stream processing. We evaluate performance, memory usage, and programming effort and compare them against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is competitive with handwritten code in some cases, in other cases, the infeasibility of fine-tuning GrPPI is a crucial drawback. Despite this, programmability experiments estimate that GrPPI has the potential to reduce by about three times the development time of parallel applications.},
keywords = {admire},
pubstate = {published},
tppubtype = {inproceedings}
}
Alberto Mulone, Sherine Awad, Davide Chiarugi, Marco Aldinucci
Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment Proceedings Article
In: Shahriar, Hossain, Teranishi, Yuuichi, Cuzzocrea, Alfredo, Sharmin, Moushumi, Towey, Dave, Majumder, A. K. M. Jahangir Alam, Kashiwazaki, Hiroki, Yang, Ji-Jiang, Takemoto, Michiharu, Sakib, Nazmus, Banno, Ryohei, Ahamed, Sheikh Iqbal (Ed.): 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, pp. 1858–1863, IEEE, Torino, Italy, 2023.
Abstract | Links | BibTeX | Tags: across, icsc, streamflow
@inproceedings{23:mulone:wide:vcp,
title = {Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment},
author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},
editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},
url = {https://iris.unito.it/bitstream/2318/1919364/1/paper.pdf},
doi = {10.1109/COMPSAC57700.2023.00288},
year = {2023},
date = {2023-01-01},
booktitle = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},
pages = {1858–1863},
publisher = {IEEE},
address = {Torino, Italy},
abstract = {In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.},
howpublished = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},
keywords = {across, icsc, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Iacopo Colonnelli, Bruno Casella, Gianluca Mittone, Yasir Arfat, Barbara Cantalupo, Roberto Esposito, Alberto Riccardo Martinelli, Doriana Medić, Marco Aldinucci
Federated Learning meets HPC and cloud Proceedings Article
In: Bufano, Filomena, Riggi, Simone, Sciacca, Eva, Schillirò, Francesco (Ed.): Astrophysics and Space Science Proceedings, pp. 193–199, Springer, Catania, Italy, 2023, ISBN: 978-3-031-34167-0, (Keynote talk).
Abstract | Links | BibTeX | Tags: across, eupilot, streamflow
@inproceedings{22:ml4astro,
title = {Federated Learning meets HPC and cloud},
author = {Iacopo Colonnelli and Bruno Casella and Gianluca Mittone and Yasir Arfat and Barbara Cantalupo and Roberto Esposito and Alberto Riccardo Martinelli and Doriana Medić and Marco Aldinucci},
editor = {Filomena Bufano and Simone Riggi and Eva Sciacca and Francesco Schillirò},
url = {https://iris.unito.it/retrieve/3ac66baa-9d9a-4e9f-94a5-13700694d8aa/ML4Astro.pdf},
doi = {10.1007/978-3-031-34167-0_39},
isbn = {978-3-031-34167-0},
year = {2023},
date = {2023-01-01},
booktitle = {Astrophysics and Space Science Proceedings},
volume = {60},
pages = {193–199},
publisher = {Springer},
address = {Catania, Italy},
abstract = {HPC and AI are fated to meet for several reasons. This article will discuss some of them and argue why this will happen through the set of methods and technologies that underpin cloud computing. As a paradigmatic example, we present a new federated learning system that collaboratively trains a deep learning model in different supercomputing centers. The system is based on the StreamFlow workflow manager designed for hybrid cloud-HPC infrastructures.},
howpublished = {Machine Learning for Astrophysics (ML4ASTRO)},
note = {Keynote talk},
keywords = {across, eupilot, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Luca La Cava, Arianna Pera
Tell Me Who You Are and I Will Predict Your Vulnerability to Political Persuasion Techniques Proceedings Article
In: The 12th International Conference on Complex Networks and their Applications-Book of Abstracts, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_CNA1_2023,
title = {Tell Me Who You Are and I Will Predict Your Vulnerability to Political Persuasion Techniques},
author = {Alessia Antelmi and Luca La Cava and Arianna Pera},
url = {https://iris.unito.it/bitstream/2318/1949370/1/_CNA__23__Personality_vs_propaganda.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},
abstract = {Given the evolving role of social media in political communication and the strategic use of these platforms by politicians to shape public opinion, research has commonly focused on investigating computational propaganda as a means for automated information diffusion. Focusing on a less explored yet promising line, we aim to assess political persuasion in digital contexts by introducing a computational framework that combines Natural Language Processing and Network Science methods to investigate the linkage between persuasion techniques on social media and personality traits of online political audiences. Our final goal is to enhance public awareness of political tactics and encourage critical thinking in response to the online spread of political information.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Luca La Cava, Arianna Pera
Finding Hidden Swingers in the 2022 Italian Elections Twitter Discourse Proceedings Article
In: The 12th International Conference on Complex Networks and their Applications-Book of Abstracts, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_CNA_2023,
title = {Finding Hidden Swingers in the 2022 Italian Elections Twitter Discourse},
author = {Alessia Antelmi and Luca La Cava and Arianna Pera},
url = {https://iris.unito.it/bitstream/2318/1949354/1/_CNA__23__TweetYourMind.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},
abstract = {The volume of the Italian online political discourse on social media has recently increased, but the coverage level does not compare with other Countries such as the US. Nonetheless, researchers focused on studying polarization and homophily with respect to political debates or investigating the role of populism in online engagement. In this research landscape, the analysis of political preference shifts through social media remains to be explored. We aim to bridge this gap by examining the Twitter discourse during the 2022 Italian general elections, with a specific emphasis on political "swingers". In particular, our findings indicate a stable political discourse in Italy, yet they also uncover a growing presence of political swingers willing to shift their support to significantly different factions.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Massimo Torquati, Daniele Gregori, Francesco Polzella, Gianmarco Spinatelli, Marco Aldinucci
The SWH-Analytics Framework Proceedings Article
In: Bena, Nicola, Martino, Beniamino Di, Maratea, Antonio, Sperduti, Alessandro, Nardo, Emanuel Di, Ciaramella, Angelo, Montella, Raffaele, Ardagna, Claudio A. (Ed.): Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023, CEUR-WS.org, 2023.
Abstract | Links | BibTeX | Tags: admire, analytics, icsc
@inproceedings{Antelmi_ITADATA_2023,
title = {The SWH-Analytics Framework},
author = {Alessia Antelmi and Massimo Torquati and Daniele Gregori and Francesco Polzella and Gianmarco Spinatelli and Marco Aldinucci},
editor = {Nicola Bena and Beniamino Di Martino and Antonio Maratea and Alessandro Sperduti and Emanuel Di Nardo and Angelo Ciaramella and Raffaele Montella and Claudio A. Ardagna},
url = {https://ceur-ws.org/Vol-3606/paper76.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023},
volume = {3606},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {The Software Heritage (SWH) dataset serves as a vast repository for open-source code, with the ambitious goal of preserving all publicly available open-source projects. Despite being designed to effectively archive project files, its size of nearly 1 petabyte presents challenges in efficiently supporting Big Data MapReduce or AI systems. To address this disparity and enable seamless custom analytics on the SWH dataset, we present the SWH-Analytics (SWHA) architecture. This development environment quickly and transparently runs custom analytic applications on open-source software data preserved over time by SWH.},
keywords = {admire, analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Iacopo Colonnelli
Workflow Models for Heterogeneous Distributed Systems Proceedings Article
In: Bena, Nicola, Martino, Beniamino Di, Maratea, Antonio, Sperduti, Alessandro, Nardo, Emanuel Di, Ciaramella, Angelo, Montella, Raffaele, Ardagna, Claudio A. (Ed.): Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023, CEUR-WS.org, 2023.
Abstract | Links | BibTeX | Tags: across, eupex, icsc, jupyter-workflow, streamflow
@inproceedings{23:colonnelli:itadata,
title = {Workflow Models for Heterogeneous Distributed Systems},
author = {Iacopo Colonnelli},
editor = {Nicola Bena and Beniamino Di Martino and Antonio Maratea and Alessandro Sperduti and Emanuel Di Nardo and Angelo Ciaramella and Raffaele Montella and Claudio A. Ardagna},
url = {https://ceur-ws.org/Vol-3606/invited77.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023},
volume = {3606},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {This article introduces a novel hybrid workflow abstraction that injects topology awareness directly into the definition of a distributed workflow model. In particular, the article briefly discusses the advantages brought by this approach to the design and orchestration of large-scale data-oriented workflows, the current level of support from state-of-the-art workflow systems, and some future research directions.},
keywords = {across, eupex, icsc, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruno Casella, Lorenzo Paletto
Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting Proceedings Article
In: Proceedings of the 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-5 May 2023, Dubai, 2023, (https://ieeexplore.ieee.org/document/10174989).
Abstract | Links | BibTeX | Tags: epi, icsc
@inproceedings{23:casella:onchain,
title = {Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting},
author = {Bruno Casella and Lorenzo Paletto},
url = {https://iris.unito.it/bitstream/2318/1902652/1/6.%20ICBC23%20-%20PREDICTING%20BTC.pdf},
doi = {https://doi.org/10.1109/ICBC56567.2023.10174989},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-5 May 2023, Dubai},
abstract = {Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network's activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies' future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.},
note = {https://ieeexplore.ieee.org/document/10174989},
keywords = {epi, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Bruno Casella, Samuele Fonio
Architecture-Based FedAvg for Vertical Federated Learning Proceedings Article
In: Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy, 4 December 2023, 2023, (https://iris.unito.it/bitstream/2318/1949730/1/HALF_HVL_for_DML_ICC23___Taormina-2.pdf).
Abstract | Links | BibTeX | Tags: ai, epi, icsc
@inproceedings{23:casella:architecturalfedavg,
title = {Architecture-Based FedAvg for Vertical Federated Learning},
author = {Bruno Casella and Samuele Fonio},
url = {https://iris.unito.it/retrieve/173d9960-8531-419d-9bd5-5acce6694c4e/Aggregation%20Based%20VFL.pdf},
doi = {10.1145/3603166.3632559},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy, 4 December 2023},
abstract = {Federated Learning (FL) has emerged as a promising solution to address privacy concerns by collaboratively training Deep Learning (DL) models across distributed parties. This work proposes an architecture-based aggregation strategy in Vertical FL, where parties hold data with different attributes but shared instances. Our approach leverages the identical architectural parts, i.e. neural network layers, of different models to selectively aggregate weights, which is particularly relevant when collaborating with institutions holding different types of datasets, i.e., image, text, or tabular datasets. In a scenario where two entities train DL models, such as a Convolutional Neural Network (CNN) and a Multi-Layer Perceptron (MLP), our strategy computes the average only for architecturally identical segments. This preserves data-specific features learned from demographic and clinical data. We tested our approach on two clinical datasets, i.e., the COVID-CXR dataset and the ADNI study. Results show that our method achieves comparable results with the centralized scenario, in which all the data are collected in a single data lake, and benefits from FL generalizability. In particular, compared to the non-federated models, our proposed proof-of-concept model exhibits a slight performance loss on the COVID-CXR dataset (less than 8%), but outperforms ADNI models by up to 12%. Moreover, communication costs between training rounds are minimized by exchanging only the dense layer parameters.},
note = {https://iris.unito.it/bitstream/2318/1949730/1/HALF_HVL_for_DML_ICC23___Taormina-2.pdf},
keywords = {ai, epi, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato
Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning Proceedings Article
In: Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France, 2 October 2023, 2023, (https://ieeexplore.ieee.org/document/10350429).
Abstract | Links | BibTeX | Tags: ai
@inproceedings{23:casella:ERGANs,
title = {Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning},
author = {Matteo Pennisi and Federica Proietto Salanitri and Giovanni Bellitto and Bruno Casella and Marco Aldinucci and Simone Palazzo and Concetto Spampinato},
url = {https://openaccess.thecvf.com/content/ICCV2023W/VCL/papers/Pennisi_Experience_Replay_as_an_Effective_Strategy_for_Optimizing_Decentralized_Federated_ICCVW_2023_paper.pdf},
doi = {10.1109/ICCVW60793.2023.00362},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France, 2 October 2023},
abstract = {Federated and continual learning are training paradigms addressing data distribution shift in space and time. More specifically, federated learning tackles non-i.i.d data in space as information is distributed in multiple nodes, while continual learning faces with temporal aspect of training as it deals with continuous streams of data. Distribution shifts over space and time is what it happens in real federated learning scenarios that show multiple challenges. First, the federated model needs to learn sequentially while retaining knowledge from the past training rounds. Second, the model has also to deal with concept drift from the distributed data distributions. To address these complexities, we attempt to combine continual and federated learning strategies by proposing a solution inspired by experience replay and generative adversarial concepts for supporting decentralized distributed training. In particular, our approach relies on using limited memory buffers of synthetic privacy-preserving samples and interleaving training on local data and on buffer data. By translating the CL formulation into the task of integrating distributed knowledge with local knowledge, our method enables models to effectively integrate learned representation from local nodes, providing models the capability to generalize across multiple datasets.We test our integrated strategy on two realistic medical image analysis tasks — tuberculosis and melanoma classification — using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation.},
note = {https://ieeexplore.ieee.org/document/10350429},
keywords = {ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Giorgio Audrito, Alberto Riccardo Martinelli, Gianluca Torta
Parallelising an Aggregate Programming Framework with Message-Passing Interface Proceedings Article
In: 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), pp. 140–145, 2023.
@inproceedings{23:acsos:fcppmpi,
title = {Parallelising an Aggregate Programming Framework with Message-Passing Interface},
author = {Giorgio Audrito and Alberto Riccardo Martinelli and Gianluca Torta},
doi = {10.1109/ACSOS-C58168.2023.00054},
year = {2023},
date = {2023-01-01},
booktitle = {2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)},
pages = {140–145},
keywords = {HPC},
pubstate = {published},
tppubtype = {inproceedings}
}
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis Journal Article
In: Computer Vision and Image Understanding, vol. 238, pp. 103882, 2023.
Abstract | Links | BibTeX | Tags: ai
@article{23:casella:FedER,
title = {FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis},
author = {Matteo Pennisi and Federica Proietto Salanitri and Giovanni Bellitto and Bruno Casella and Marco Aldinucci and Simone Palazzo and Concetto Spampinato},
url = {https://www.sciencedirect.com/science/article/pii/S107731422300262X?via%3Dihub},
doi = {10.1016/j.cviu.2023.103882},
year = {2023},
date = {2023-01-01},
journal = {Computer Vision and Image Understanding},
volume = {238},
pages = {103882},
institution = {Computer Science Department, University of Torino},
abstract = {In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. Federated learning (FL) aims at sidestepping this limitation by bringing AI-based solutions to data owners and only sharing local AI models, or parts thereof, that need then to be aggregated. However, most of the existing federated learning solutions are still at their infancy and show several shortcomings, from the lack of a reliable and effective aggregation scheme able to retain the knowledge learned locally to weak privacy preservation as real data may be reconstructed from model updates. Furthermore, the majority of these approaches, especially those dealing with medical data, relies on a centralized distributed learning strategy that poses robustness, scalability and trust issues. In this paper we present a federated and decentralized learning strategy, FedER, that, exploiting experience replay and generative adversarial concepts, effectively integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy. FedER is tested on two tasks — tuberculosis and melanoma classification — using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation. Code is available at https://github.com/perceivelab/FedER},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Bruno Casella, Walter Riviera, Marco Aldinucci, Gloria Menegaz
MERGE: A model for multi-input biomedical federated learning Journal Article
In: Patterns, pp. 100856, 2023, ISSN: 2666-3899.
Abstract | Links | BibTeX | Tags: ai, epi, icsc
@article{23:fl:patterns,
title = {MERGE: A model for multi-input biomedical federated learning},
author = {Bruno Casella and Walter Riviera and Marco Aldinucci and Gloria Menegaz},
url = {https://www.sciencedirect.com/science/article/pii/S2666389923002404},
doi = {10.1016/j.patter.2023.100856},
issn = {2666-3899},
year = {2023},
date = {2023-01-01},
journal = {Patterns},
pages = {100856},
abstract = {Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic images. Imaging, however, is not the only source of information. Tabular data, such as personal and genomic data and blood test results, are routinely collected but rarely considered in DL pipelines. Nevertheless, DL requires large datasets that often must be pooled from different institutions, raising non-trivial privacy concerns. Federated learning (FL) is a cooperative learning paradigm that aims to address these issues by moving models instead of data across different institutions. Here, we present a federated multi-input architecture using images and tabular data as a methodology to enhance model performance while preserving data privacy. We evaluated it on two showcases: the prognosis of COVID-19 and patients' stratification in Alzheimer's disease, providing evidence of enhanced accuracy and F1 scores against single-input models and improved generalizability against non-federated models.},
keywords = {ai, epi, icsc},
pubstate = {published},
tppubtype = {article}
}
Javier Garcia-Blas, Genaro Sanchez-Gallegos, Cosmin Petre, Alberto Riccardo Martinelli, Marco Aldinucci, Jesus Carretero
Hercules: Scalable and Network Portable In-Memory Ad-Hoc File System for Data-Centric and High-Performance Applications Proceedings Article
In: Cano, José, Dikaiakos, Marios D., Papadopoulos, George A., Pericàs, Miquel, Sakellariou, Rizos (Ed.): Euro-Par 2023: Parallel Processing, pp. 679–693, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-39698-4.
Abstract | BibTeX | Tags: admire, HPC
@inproceedings{10.1007/978-3-031-39698-4_46,
title = {Hercules: Scalable and Network Portable In-Memory Ad-Hoc File System for Data-Centric and High-Performance Applications},
author = {Javier Garcia-Blas and Genaro Sanchez-Gallegos and Cosmin Petre and Alberto Riccardo Martinelli and Marco Aldinucci and Jesus Carretero},
editor = {José Cano and Marios D. Dikaiakos and George A. Papadopoulos and Miquel Pericàs and Rizos Sakellariou},
isbn = {978-3-031-39698-4},
year = {2023},
date = {2023-01-01},
booktitle = {Euro-Par 2023: Parallel Processing},
pages = {679–693},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The growing demands for data processing by new data-intensive applications are putting pressure on the performance and capacity of HPC storage systems. The advancement in storage technologies, such as NVMe and persistent memory, are aimed at meeting these demands. However, relying solely on ultra-fast storage devices is not cost-effective, leading to the need for multi-tier storage hierarchies to move data based on its usage. To address this issue, ad-hoc file systems have been proposed as a solution. They utilise the available storage of compute nodes, such as memory and persistent storage, to create a temporary file system that adapts to the application behaviour in the HPC environment. This work presents the design, implementation, and evaluation of a distributed ad-hoc in-memory storage system (Hercules), highlighting the new communication model included in Hercules. This communication model takes advantage of the Unified Communication X framework (UCX). This solution leverages the capabilities of RDMA protocols, including Infiniband, Onmipath, shared memory, and zero-copy transfers. The preliminary evaluation results show excellent network utilisation compared with other existing technologies.},
keywords = {admire, HPC},
pubstate = {published},
tppubtype = {inproceedings}
}
Marco Aldinucci Mirko Polato Roberto Esposito
Boosting Methods for Federated Learning Proceedings Article
In: Calvanese, Diego, Diamantini, Claudia, Ferro, Nicola, Marchesin, Stefano, Silvello, Gianmaria, Tanca, Letizia (Ed.): Proc. of the 31th Italian Symposium on Advanced Database Systems,SEBD 2023, pp. 439–448, CEUR-WS.org, 2023.
Abstract | Links | BibTeX | Tags: eupilot
@inproceedings{DBLP:conf/sebd/Esposito23,
title = {Boosting Methods for Federated Learning},
author = {Marco Aldinucci Mirko Polato Roberto Esposito},
editor = {Diego Calvanese and Claudia Diamantini and Nicola Ferro and Stefano Marchesin and Gianmaria Silvello and Letizia Tanca},
url = {https://ceur-ws.org/Vol-3478/paper48.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {Proc. of the 31th Italian Symposium on Advanced Database Systems,SEBD 2023},
pages = {439–448},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {Federated Learning (FL) has been proposed to develop better AI systems without compromising the privacy of final users and the legitimate interests of private companies. Initially deployed by Google to predict text input on mobile devices, FL has been deployed in many other industries. Since its introduction, Federated Learning mainly exploited the inner working of neural networks and other gradient descent-based algorithms by either exchanging the weights of the model or the gradients computed during learning. While this approach has been very successful, it rules out applying FL in contexts where other models are preferred, e.g., easier to interpret or known to work better. This paper proposes to leverage distributed versions of the AdaBoost algorithm to acquire strong federated models. In contrast with previous approaches, our proposal does not put any constraint on the client-side learning models and does not rely on inner workings of the learning algorithms used in the clients. We perform a large set of experiments on ten UCI datasets, comparing the algorithms in six non-iidness settings. Results show that the approach is effective, in the case of an IID setting, results are often near to the theoretical optimum (i.e., the performances of AdaBoost on the complete dataset). In case of non-IID settings, results very much depend on the severity of the non-IIDness.},
keywords = {eupilot},
pubstate = {published},
tppubtype = {inproceedings}
}
Pedro Ângelo, Viviana Bono, Mariangiola Dezani-Ciancaglini, Mário Florido
Gradual Guarantee for FJ with lambda-Expressions Proceedings Article
In: Tomb, Aaron (Ed.): Proceedings of the 25th ACM International Workshop on Formal Techniques for Java-like Programs, FTfJP 2023, Seattle, WA, USA, 18 July 2023, pp. 32–38, ACM, 2023.
Links | BibTeX | Tags: admire, icsc
@inproceedings{DBLP:conf/ftfjp/AngeloBDF23,
title = {Gradual Guarantee for FJ with lambda-Expressions},
author = {Pedro Ângelo and Viviana Bono and Mariangiola Dezani-Ciancaglini and Mário Florido},
editor = {Aaron Tomb},
url = {https://doi.org/10.1145/3605156.3606453},
doi = {10.1145/3605156.3606453},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 25th ACM International Workshop on Formal Techniques for Java-like Programs, FTfJP 2023, Seattle, WA, USA, 18 July 2023},
pages = {32–38},
publisher = {ACM},
keywords = {admire, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
William Fornaciari, Federico Reghenzani, Federico Terraneo, Davide Baroffio, Cecilia Metra, Martin Omana, Josie E. Rodriguez Condia, Matteo Sonza Reorda, Robert Birke, Iacopo Colonnelli, Gianluca Mittone, Marco Aldinucci, Gabriele Mencagli, Francesco Iannone, Filippo Palombi, Giuseppe Zummo, Daniele Cesarini, Federico Tesser
RISC-V-based Platforms for HPC: Analyzing Non-functional Properties for Future HPC and Big-Data Clusters Proceedings Article
In: Embedded Computer Systems: Architectures, Modeling, and Simulation - 23rd International Conference, SAMOS 2023, Samos, Greece, 2023, (icsc).
Abstract | Links | BibTeX | Tags: icsc, riscv
@inproceedings{23:SAMOS,
title = {RISC-V-based Platforms for HPC: Analyzing Non-functional Properties for Future HPC and Big-Data Clusters},
author = {William Fornaciari and Federico Reghenzani and Federico Terraneo and Davide Baroffio and Cecilia Metra and Martin Omana and Josie E. Rodriguez Condia and Matteo Sonza Reorda and Robert Birke and Iacopo Colonnelli and Gianluca Mittone and Marco Aldinucci and Gabriele Mencagli and Francesco Iannone and Filippo Palombi and Giuseppe Zummo and Daniele Cesarini and Federico Tesser},
url = {https://iris.unito.it/retrieve/b627eab0-3aa1-4fd7-8685-f47c62c792b3/SAMOS_2023_CN_HPC_FL1.pdf},
doi = {10.1007/978-3-031-46077-7_26},
year = {2023},
date = {2023-01-01},
booktitle = {Embedded Computer Systems: Architectures, Modeling, and Simulation - 23rd International Conference, SAMOS 2023},
address = {Samos, Greece},
abstract = {High-PerformanceComputing(HPC)haveevolvedtobeused to perform simulations of systems where physical experimentation is pro- hibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activ- ities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches.},
note = {icsc},
keywords = {icsc, riscv},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi, Daniele De Vinco, Gennaro Cordasco, Carmine Spagnuolo
Towards Unraveling Developers Communities in Stack Overflow and Reddit Proceedings Article
In: International Conference on Computational Social Science 2023, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_IC2S2_2023,
title = {Towards Unraveling Developers Communities in Stack Overflow and Reddit},
author = {Alessia Antelmi and Daniele De Vinco and Gennaro Cordasco and Carmine Spagnuolo},
url = {https://openreview.net/forum?id=WP5ZaAFP19},
year = {2023},
date = {2023-01-01},
booktitle = {International Conference on Computational Social Science 2023},
abstract = {This work investigates the developers' behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users' posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these QA platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Alessia Antelmi
Engagement in Open Data Workshops: The dark side of remote settings Proceedings Article
In: Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference, Springer International Publishing, Cham, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_TEL4FC_2023,
title = {Engagement in Open Data Workshops: The dark side of remote settings},
author = {Alessia Antelmi},
url = {https://link.springer.com/chapter/10.1007/978-3-031-42134-1_33},
year = {2023},
date = {2023-01-01},
booktitle = {Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The increasing availability of Open Data gives birth to a fertile field for interested stakeholders to create value out of them; however, limited technical expertise and poor awareness are crucial barriers to their exploitation. Because of these reasons, there is an urge for learners to acquire data and information literacy competencies, which are essential for 21st-century skills, and become familiar with available Open Data sources and their potential uses. To promote the dialogue around activities to boost recognition of Open Data and improve users' skills to work with them, we proposed a series of workshops to introduce Italian high school learners to searching for, authoring, and building effective communication based on Open Data. This article describes an ongoing activity and details its organization, reports preliminary results on learners' engagement, and discusses both challenges of the remote setting as well as promising learning outcomes.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Doriana Medić, Marco Aldinucci
Towards formal model for location aware workflows Proceedings Article
In: Shahriar, Hossain, Teranishi, Yuuichi, Cuzzocrea, Alfredo, Sharmin, Moushumi, Towey, Dave, Majumder, A. K. M. Jahangir Alam, Kashiwazaki, Hiroki, Yang, Ji-Jiang, Takemoto, Michiharu, Sakib, Nazmus, Banno, Ryohei, Ahamed, Sheikh Iqbal (Ed.): 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, pp. 1864–1869, IEEE, Torino, Italy, 2023.
Abstract | Links | BibTeX | Tags: eupex, icsc, semantics
@inproceedings{23:medic:formal-model,
title = {Towards formal model for location aware workflows},
author = {Doriana Medić and Marco Aldinucci},
editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},
url = {https://iris.unito.it/retrieve/1f9f959c-cd88-4d9c-90ea-54f1c86a15bc/6210-medic.pdf},
doi = {10.1109/COMPSAC57700.2023.00289},
year = {2023},
date = {2023-01-01},
booktitle = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},
pages = {1864–1869},
publisher = {IEEE},
address = {Torino, Italy},
abstract = {Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack. This paper investigates the capability of process algebra to model a location aware workflow system. Distributed π-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system. The intention of this paper is to highlight the fact that due to its flexibility, π-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.},
keywords = {eupex, icsc, semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
Ovidio Filippo, Francesco Bruno, Tineke H. Pinxterhuis, Mariusz Gasior, Leor Perl, Luca Gaido, Domenico Tuttolomondo, Antonio Greco, Roberto Verardi, Gianluca Lo Martire, Mario Iannaccone, Attilio Leone, Gaetano Liccardo, Serena Caglioni, Rocio González Ferreiro, Giulio Rodinò, Giuseppe Musumeci, Giuseppe Patti, Irene Borzillo, Giuseppe Tarantini, Wojciech Wańha, Bruno Casella, Eline H Ploumen, Lukasz Pyka, Ran Kornowski, Andrea Gagnor, Raffaele Piccolo, Sergio Raposeiras Roubin, Davide Capodanno, Paolo Zocca, Federico Conrotto, Gaetano M De Ferrari, Clemens Birgelen, Fabrizio D'Ascenzo
In: Catheterization and Cardiovascular Interventions, 2023.
Abstract | Links | BibTeX | Tags: ai, cardio
@article{23:casella:ultra,
title = {Predictors of target lesion failure after treatment of left main, bifurcation, or chronic total occlusion lesions with ultrathin-strut drug-eluting coronary stents in the ULTRA registry},
author = {Ovidio Filippo and Francesco Bruno and Tineke H. Pinxterhuis and Mariusz Gasior and Leor Perl and Luca Gaido and Domenico Tuttolomondo and Antonio Greco and Roberto Verardi and Gianluca Lo Martire and Mario Iannaccone and Attilio Leone and Gaetano Liccardo and Serena Caglioni and Rocio González Ferreiro and Giulio Rodinò and Giuseppe Musumeci and Giuseppe Patti and Irene Borzillo and Giuseppe Tarantini and Wojciech Wańha and Bruno Casella and Eline H Ploumen and Lukasz Pyka and Ran Kornowski and Andrea Gagnor and Raffaele Piccolo and Sergio Raposeiras Roubin and Davide Capodanno and Paolo Zocca and Federico Conrotto and Gaetano M De Ferrari and Clemens Birgelen and Fabrizio D'Ascenzo},
url = {https://onlinelibrary.wiley.com/doi/full/10.1002/ccd.30696},
doi = {10.1002/ccd.30696},
year = {2023},
date = {2023-01-01},
journal = {Catheterization and Cardiovascular Interventions},
abstract = {Background: Data about the long-term performance of new-generation ultrathin-strut drug-eluting stents (DES) in challenging coronary lesions, such as left main (LM), bifurcation, and chronic total occlusion (CTO) lesions are scant. Methods: The international multicenter retrospective observational ULTRA study included consecutive patients treated from September 2016 to August 2021 with ultrathin-strut (<70µm) DES in challenging de novo lesions. Primary endpoint was target lesion failure (TLF): composite of cardiac death, target-lesion revascularization (TLR), target-vessel myocardial infarction (TVMI), or definite stent thrombosis (ST). Secondary endpoints included all-cause death, acute myocardial infarction (AMI), target vessel revascularization, and TLF components. TLF predictors were assessed with Cox multivariable analysis. Results: Of 1801 patients (age: 66.6$±$11.2 years; male: 1410 [78.3%]), 170 (9.4%) experienced TLF during follow-up of 3.1$±$1.4 years. In patients with LM, CTO, and bifurcation lesions, TLF rates were 13.5%, 9.9%, and 8.9%, respectively. Overall, 160 (8.9%) patients died (74 [4.1%] from cardiac causes). AMI and TVMI rates were 6.0% and 3.2%, respectively. ST occurred in 11 (1.1%) patients while 77 (4.3%) underwent TLR. Multivariable analysis identified the following predictors of TLF: age, STEMI with cardiogenic shock, impaired left ventricular ejection fraction, diabetes, and renal dysfunction. Among the procedural variables, total stent length increased TLF risk (HR: 1.01, 95% CI: 1-1.02 per mm increase), while intracoronary imaging reduced the risk substantially (HR: 0.35, 95% CI: 0.12-0.82). Conclusions: Ultrathin-strut DES showed high efficacy and satisfactory safety, even in patients with challenging coronary lesions. Yet, despite using contemporary gold-standard DES, the association persisted between established patient- and procedure-related features of risk and impaired 3-year clinical outcome.},
keywords = {ai, cardio},
pubstate = {published},
tppubtype = {article}
}
Bruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios Technical Report
Computer Science Department, University of Torino 2023.
Abstract | Links | BibTeX | Tags: confidential, epi, icsc
@techreport{23:casella:normalization,
title = {Experimenting with Normalization Layers in Federated Learning on non-IID scenarios},
author = {Bruno Casella and Roberto Esposito and Antonio Sciarappa and Carlo Cavazzoni and Marco Aldinucci},
url = {https://arxiv.org/pdf/2303.10630.pdf},
year = {2023},
date = {2023-01-01},
institution = {Computer Science Department, University of Torino},
abstract = {Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacy-preserving pooling of datasets employing collaborative training from different institutions by iteratively globally aggregating locally trained models. One critical performance challenge of FL is operating on datasets not independently and identically distributed (non-IID) among the federation participants. Even though this fragility cannot be eliminated, it can be debunked by a suitable optimization of two hyperparameters: layer normalization methods and collaboration frequency selection. In this work, we benchmark five different normalization layers for training Neural Networks (NNs), two families of non-IID data skew, and two datasets. Results show that Batch Normalization, widely employed for centralized DL, is not the best choice for FL, whereas Group and Layer Normalization consistently outperform Batch Normalization. Similarly, frequent model aggregation decreases convergence speed and mode quality.},
keywords = {confidential, epi, icsc},
pubstate = {published},
tppubtype = {techreport}
}
Yasir Arfat, Gianluca Mittone, Iacopo Colonnelli, Fabrizio D'Ascenzo, Roberto Esposito, Marco Aldinucci
Pooling critical datasets with Federated Learning Proceedings Article
In: 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023, pp. 329–337, IEEE, Napoli, Italy, 2023.
Abstract | Links | BibTeX | Tags: admire, ai, cardio, confidential, hpc4ai
@inproceedings{23:praise-fl:pdp,
title = {Pooling critical datasets with Federated Learning},
author = {Yasir Arfat and Gianluca Mittone and Iacopo Colonnelli and Fabrizio D'Ascenzo and Roberto Esposito and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/491e22ec-3db5-4989-a063-085a199edd20/23_pdp_fl.pdf},
doi = {10.1109/PDP59025.2023.00057},
year = {2023},
date = {2023-01-01},
booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023},
pages = {329–337},
publisher = {IEEE},
address = {Napoli, Italy},
abstract = {Federated Learning (FL) is becoming popular in different industrial sectors where data access is critical for security, privacy and the economic value of data itself. Unlike traditional machine learning, where all the data must be globally gathered for analysis, FL makes it possible to extract knowledge from data distributed across different organizations that can be coupled with different Machine Learning paradigms. In this work, we replicate, using Federated Learning, the analysis of a pooled dataset (with AdaBoost) that has been used to define the PRAISE score, which is today among the most accurate scores to evaluate the risk of a second acute myocardial infarction. We show that thanks to the extended-OpenFL framework, which implements AdaBoost.F, we can train a federated PRAISE model that exhibits comparable accuracy and recall as the centralised model. We achieved F1 and F2 scores which are consistently comparable to the PRAISE score study of a 16- parties federation but within an order of magnitude less time.},
keywords = {admire, ai, cardio, confidential, hpc4ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Sandro Gepiro Contaldo, Luca Alessandri, Iacopo Colonnelli, Marco Beccuti, Marco Aldinucci
Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow Book Chapter
In: Calogero, Raffaele Adolfo, Benes, Vladimir (Ed.): Single Cell Transcriptomics: Methods and Protocols, pp. 337–345, Springer US, New York, NY, 2023, ISBN: 978-1-0716-2756-3.
Abstract | Links | BibTeX | Tags: streamflow
@inbook{Contaldo2023,
title = {Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow},
author = {Sandro Gepiro Contaldo and Luca Alessandri and Iacopo Colonnelli and Marco Beccuti and Marco Aldinucci},
editor = {Raffaele Adolfo Calogero and Vladimir Benes},
url = {https://datacloud.di.unito.it/index.php/s/KMfKo4m7GTGdZmF},
doi = {10.1007/978-1-0716-2756-3_17},
isbn = {978-1-0716-2756-3},
year = {2023},
date = {2023-01-01},
booktitle = {Single Cell Transcriptomics: Methods and Protocols},
pages = {337–345},
publisher = {Springer US},
address = {New York, NY},
abstract = {The idea behind novel single-cell RNA sequencing (scRNA-seq) pipelines is to isolate single cells through microfluidic approaches and generate sequencing libraries in which the transcripts are tagged to track their cell of origin. Modern scRNA-seq platforms are capable of analyzing up to many thousands of cells in each run. Then, combined with massive high-throughput sequencing producing billions of reads, scRNA-seq allows the assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution.},
keywords = {streamflow},
pubstate = {published},
tppubtype = {inbook}
}
Amirmasoud Ghiassi, Robert Birke, Lydia Chen
Robust Learning via Golden Symmetric Loss of (un)Trusted Labels Proceedings Article
In: SDM '23: SIAM International Conference on Data Mining, pp. 568–576, 2023.
Abstract | Links | BibTeX | Tags: textarossa
@inproceedings{sdm-ghiassi23,
title = {Robust Learning via Golden Symmetric Loss of (un)Trusted Labels},
author = {Amirmasoud Ghiassi and Robert Birke and Lydia Chen},
url = {https://datacloud.di.unito.it/index.php/s/b6z3moNLxnNiCxz},
doi = {10.1137/1.9781611977653.ch64},
year = {2023},
date = {2023-01-01},
booktitle = {SDM '23: SIAM International Conference on Data Mining},
pages = {568–576},
abstract = {Learning robust deep models against noisy labels becomes ever critical when today's data is commonly collected from open platforms and subject to adversarial corruption. The information on the label corruption process, i.e., corruption matrix, can greatly enhance the robustness of deep models but still fall behind in combating hard classes. In this paper, we propose to construct a golden symmetric loss (GSL) based on the estimated corruption matrix as to avoid overfitting to noisy labels and learn effectively from hard classes. GSL is the weighted sum of the corrected regular cross entropy and reverse cross entropy. By leveraging a small fraction of trusted clean data, we estimate the corruption matrix and use it to correct the loss as well as to determine the weights of GSL. We theoretically prove the robustness of the proposed loss function in the presence of dirty labels. We provide a heuristics to adaptively tune the loss weights of GSL according to the noise rate and diversity measured from the dataset. We evaluate our proposed golden symmetric loss on both vision and natural language deep models subject to different types of label noise patterns. Empirical results show that GSL can significantly outperform the existing robust training methods on different noise patterns, showing accuracy improvement up to 18% on CIFAR-100 and 1% on real world noisy dataset of Clothing1M.},
keywords = {textarossa},
pubstate = {published},
tppubtype = {inproceedings}
}
Adriano Marques Garcia, Dalvan Griebler, Claudio Schepke, Luiz Gustavo Fernandes
Micro-batch and data frequency for stream processing on multi-cores Journal Article
In: The Journal of Supercomputing, vol. 79, no. 8, pp. 9206-9244, 2023, ISBN: 1573-0484.
Abstract | Links | BibTeX | Tags: parallel
@article{GARCIA:JSuper:23,
title = {Micro-batch and data frequency for stream processing on multi-cores},
author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes},
url = {https://iris.unito.it/retrieve/9328dbca-98ae-4ac5-b856-57c72db4444a/s11227-022-05024-y_preprint.pdf},
doi = {10.1007/s11227-022-05024-y},
isbn = {1573-0484},
year = {2023},
date = {2023-01-01},
journal = {The Journal of Supercomputing},
volume = {79},
number = {8},
pages = {9206-9244},
publisher = {Springer},
abstract = {Latency or throughput is often critical performance metrics in stream processing. Applications’ performance can fluctuate depending on the input stream. This unpredictability is due to the variety in data arrival frequency and size, complexity, and other factors. Researchers are constantly investigating new ways to mitigate the impact of these variations on performance with self-adaptive techniques involving elasticity or micro-batching. However, there is a lack of benchmarks capable of creating test scenarios to further evaluate these techniques. This work extends and improves the SPBench benchmarking framework to support dynamic micro-batching and data stream frequency management. We also propose a set of algorithms that generates the most commonly used frequency patterns for benchmarking stream processing in related work. It allows the creation of a wide variety of test scenarios. To validate our solution, we use SPBench to create custom benchmarks and evaluate the impact of micro-batching and data stream frequency on the performance of Intel TBB and FastFlow. These are two libraries that leverage stream parallelism for multi-core architectures. Our results demonstrated that our test cases did not benefit from micro-batches on multi-cores. For different data stream frequency configurations, TBB ensured the lowest latency, while FastFlow assured higher throughput in shorter pipelines.},
keywords = {parallel},
pubstate = {published},
tppubtype = {article}
}
2022
Yujin Zhu, Zilong Zhao, Robert Birke, Lydia Y. Chen
Permutation-Invariant Tabular Data Synthesis Proceedings Article
In: Tsumoto, Shusaku, Ohsawa, Yukio, Chen, Lei, Poel, Dirk Van, Hu, Xiaohua, Motomura, Yoichi, Takagi, Takuya, Wu, Lingfei, Xie, Ying, Abe, Akihiro, Raghavan, Vijay (Ed.): IEEE International Conference on Big Data (Big Data), pp. 5855–5864, IEEE, 2022.
Abstract | Links | BibTeX | Tags: analytics
@inproceedings{bigdata-zhu22,
title = {Permutation-Invariant Tabular Data Synthesis},
author = {Yujin Zhu and Zilong Zhao and Robert Birke and Lydia Y. Chen},
editor = {Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and Dirk Van Poel and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan},
url = {https://datacloud.di.unito.it/index.php/s/b6z3moNLxnNiCxz},
doi = {10.1109/BigData55660.2022.10020639},
year = {2022},
date = {2022-12-01},
booktitle = {IEEE International Conference on Big Data (Big Data)},
pages = {5855–5864},
publisher = {IEEE},
abstract = {Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first c onduct a n e xtensive e mpirical s tudy to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find t he s uitable c olumn o rder o f i nput d ata f or CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.},
keywords = {analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Emilio Sulis, Ilaria Angela Amantea, Marco Aldinucci, Guido Boella, Renata Marinello, Marco Grosso, Paolo Platter, Serena Ambrosini
An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective Journal Article
In: Journal of Ambient Intelligence and Humanized Computing, 2022, ISBN: 1868-5145.
Abstract | Links | BibTeX | Tags: ai
@article{Sulis2022,
title = {An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective},
author = {Emilio Sulis and Ilaria Angela Amantea and Marco Aldinucci and Guido Boella and Renata Marinello and Marco Grosso and Paolo Platter and Serena Ambrosini},
url = {https://iris.unito.it/retrieve/c7eaab0b-f78b-4af0-8c17-fa5479d776e6/jaihc2021-preprint.pdf},
doi = {10.1007/s12652-022-04388-6},
isbn = {1868-5145},
year = {2022},
date = {2022-09-21},
journal = {Journal of Ambient Intelligence and Humanized Computing},
abstract = {The growing number of next-generation applications offers a relevant opportunity for healthcare services, generating an urgent need for architectures for systems integration. Moreover, the huge amount of stored information related to events can be explored by adopting a process-oriented perspective. This paper discusses an Ambient Assisted Living healthcare architecture to manage hospital home-care services. The proposed solution relies on adopting an event manager to integrate sources ranging from personal devices to web-based applications. Data are processed on a federated cloud platform offering computing infrastructure and storage resources to improve scientific research. In a second step, a business process analysis of telehealth and telemedicine applications is considered. An initial study explored the business process flow to capture the main sequences of tasks, activities, events. This step paves the way for the integration of process mining techniques to compliance monitoring in an AAL architecture framework.},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Iacopo Colonnelli, Marco Aldinucci
Hybrid Workflows For Large - Scale Scientific Applications Proceedings Article
In: Sixth EAGE High Performance Computing Workshop, pp. 1–5, European Association of Geoscientists & Engineers , Milano, Italy, 2022, ISSN: 2214-4609.
Abstract | Links | BibTeX | Tags: across, eupex
@inproceedings{22:eage-hpc-workshop,
title = {Hybrid Workflows For Large - Scale Scientific Applications},
author = {Iacopo Colonnelli and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/d79ddabb-f9d7-4a55-9f84-1528b1533ba3/Extended_Abstract.pdf},
doi = {10.3997/2214-4609.2022615029},
issn = {2214-4609},
year = {2022},
date = {2022-09-01},
booktitle = {Sixth EAGE High Performance Computing Workshop},
pages = {1–5},
publisher = {European Association of Geoscientists & Engineers },
address = {Milano, Italy},
abstract = {Large-scale scientific applications are facing an irrevrsible transition from monolithic, high-performance oriented codes to modular and polyglot deployments of specialised (micro-)services. The reasons behind this transition are many: coupling of standard solvers with Deep Learning techniques, offloading of data analysis and visualisation to Cloud, and the advent of specialised hardware accelerators. Topology-aware Workflow Management Systems (WMSs) play a crucial role. In particular, topology-awareness allows an explicit mapping of workflow steps onto heterogeneous locations, allowing automated executions on top of hybrid architectures (e.g., cloud+HPC or classical+quantum). Plus, topology-aware WMSs can offer nonfunctional requirements OOTB, e.g. components' life-cycle orchestration, secure and efficient data transfers, fault tolerance, and cross-cluster execution of urgent workloads. Augmenting interactive Jupyter Notebooks with distributed workflow capabilities allows domain experts to prototype and scale applications using the same technological stack, while relying on a feature-rich and user-friendly web interface. This abstract will showcase how these general methodologies can be applied to a typical geoscience simulation pipeline based on the Full Wavefront Inversion (FWI) technique. In particular, a prototypical Jupyter Notebook will be executed interactively on Cloud. Preliminary data analyses and post-processing will be executed locally, while the computationally demanding optimisation loop will be scheduled on a remote HPC cluster.},
keywords = {across, eupex},
pubstate = {published},
tppubtype = {inproceedings}
}
Christopher Stewart, Nathaniel Morris, Lydia Y. Chen, Robert Birke
Performance Modeling for Short-Term Cache Allocation Proceedings Article
In: Proceedings of the 51st International Conference on Parallel Processing (ICPP), pp. 31:1–31:11, ACM, 2022.
Abstract | Links | BibTeX | Tags: parallel
@inproceedings{icpp-stewart22,
title = {Performance Modeling for Short-Term Cache Allocation},
author = {Christopher Stewart and Nathaniel Morris and Lydia Y. Chen and Robert Birke},
url = {https://doi.org/10.1145/3545008.3545094},
doi = {10.1145/3545008.3545094},
year = {2022},
date = {2022-08-01},
booktitle = {Proceedings of the 51st International Conference on Parallel Processing (ICPP)},
pages = {31:1–31:11},
publisher = {ACM},
abstract = {Short-term cache allocation grants and then revokes access to processor cache lines dynamically. For online services, short-term allocation can speed up targeted query executions and free up cache lines reserved, but normally not needed, for performance. However, in collocated settings, short-term allocation can increase cache contention, slowing down collocated query executions. To offset slowdowns, collocated services may request short-term allocation more often, making the problem worse. Short-term allocation policies manage which queries receive cache allocations and when. In collocated settings, these policies should balance targeted query speedups against slowdowns caused by recurring cache contention. We present a model-driven approach that (1) predicts response time under a given policy, (2) explores competing policies and (3) chooses policies that yield low response time for all collocated services. Our approach profiles cache usage offline, characterizes the effects of cache allocation policies using deep learning techniques and devises novel performance models for short-term allocation with online services. We tested our approach using data processing, cloud, and high-performance computing benchmarks collocated on Intel processors equipped with Cache Allocation Technology. Our models predicted median response time with 11% absolute percent error. Short-term allocation policies found using our approach out performed state-of-the-art shared cache allocation policies by 1.2-2.3X.},
keywords = {parallel},
pubstate = {published},
tppubtype = {inproceedings}
}
Mirko Polato, Roberto Esposito, Marco Aldinucci
Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent Proceedings Article
In: Intl. Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE, Padua, Italy, 2022.
Abstract | Links | BibTeX | Tags: eupilot, hpc4ai
@inproceedings{22:fl:ijcnn,
title = {Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent},
author = {Mirko Polato and Roberto Esposito and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/03a7b692-aecc-43db-a792-874c553d9ebe/ijcnn22-internal.pdf},
doi = {10.1109/IJCNN55064.2022.9892284},
year = {2022},
date = {2022-07-01},
booktitle = {Intl. Joint Conference on Neural Networks (IJCNN)},
pages = {1–10},
publisher = {IEEE},
address = {Padua, Italy},
abstract = {Federated Learning has been proposed to develop better AI systems without compromising the privacy of final users and the legitimate interests of private companies. Initially deployed by Google to predict text input on mobile devices, FL has been deployed in many other industries. Since its introduction, Federated Learning mainly exploited the inner working of neural networks and other gradient descent-based algorithms by either exchanging the weights of the model or the gradients computed during learning. While this approach has been very successful, it rules out applying FL in contexts where other models are preferred, e.g., easier to interpret or known to work better. This paper proposes FL algorithms that build federated models without relying on gradient descent-based methods. Specifically, we leverage distributed versions of the AdaBoost algorithm to acquire strong federated models. In contrast with previous approaches, our proposal does not put any constraint on the client-side learning models. We perform a large set of experiments on ten UCI datasets, comparing the algorithms in six non-iidness settings.},
keywords = {eupilot, hpc4ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Adriano Marques Garcia, Dalvan Griebler, Claudio Schepke, Luiz Gustavo Fernandes
Evaluating Micro-batch and Data Frequency for Stream Processing Applications on Multi-cores Proceedings Article
In: 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 10-17, IEEE, Valladolid, Spain, 2022.
Abstract | Links | BibTeX | Tags: parallel
@inproceedings{GARCIA:PDP:22,
title = {Evaluating Micro-batch and Data Frequency for Stream Processing Applications on Multi-cores},
author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes},
url = {https://iris.unito.it/retrieve/f6d113e5-789b-4f8b-924d-8ca3d38e8d62/PDP_2022__SPBench_with_Batch_and_Data_Frequency_.pdf},
doi = {10.1109/PDP55904.2022.00011},
year = {2022},
date = {2022-04-01},
booktitle = {30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
pages = {10-17},
publisher = {IEEE},
address = {Valladolid, Spain},
series = {PDP'22},
abstract = {In stream processing, data arrives constantly and is often unpredictable. It can show large fluctuations in arrival frequency, size, complexity, and other factors. These fluctuations can strongly impact application latency and throughput, which are critical factors in this domain. Therefore, there is a significant amount of research on self-adaptive techniques involving elasticity or micro-batching as a way to mitigate this impact. However, there is a lack of benchmarks and tools for helping researchers to investigate micro-batching and data stream frequency implications. In this paper, we extend a benchmarking framework to support dynamic micro-batching and data stream frequency management. We used it to create custom benchmarks and compare latency and throughput aspects from two different parallel libraries. We validate our solution through an extensive analysis of the impact of micro-batching and data stream frequency on stream processing applications using Intel TBB and FastFlow, which are two libraries that leverage stream parallelism on multi-core architectures. Our results demonstrated up to 33% throughput gain over latency using micro-batches. Additionally, while TBB ensures lower latency, FastFlow ensures higher throughput in the parallel applications for different data stream frequency configurations.},
keywords = {parallel},
pubstate = {published},
tppubtype = {inproceedings}
}
Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
LABNET: A Collaborative Method for DNN Training and Label Aggregation Proceedings Article
In: Rocha, Ana Paula, Steels, Luc, Herik, H. Jaap (Ed.): 14th International Conference on Agents and Artificial Intelligence (ICAART), pp. 56–66, SCITEPRESS, 2022.
Abstract | Links | BibTeX | Tags:
@inproceedings{ghiassi/iccart22,
title = {LABNET: A Collaborative Method for DNN Training and Label Aggregation},
author = {Amirmasoud Ghiassi and Robert Birke and Lydia Y. Chen},
editor = {Ana Paula Rocha and Luc Steels and H. Jaap Herik},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010770400003116},
doi = {10.5220/0010770400003116},
year = {2022},
date = {2022-02-01},
booktitle = {14th International Conference on Agents and Artificial Intelligence (ICAART)},
pages = {56–66},
publisher = {SCITEPRESS},
abstract = {Today, to label the massive datasets needed to train Deep Neural Networks (DNNs), cheap and error-prone methods such as crowdsourcing are used. Label aggregation methods aim to infer the true labels from noisy labels annotated by crowdsourcing workers via labels statistics features. Aggregated labels are the main data source to train deep neural networks, and their accuracy directly affects the deep neural network performance. In this paper, we argue that training DNN and aggregating labels are not two separate tasks. Incorporation between DNN training and label aggregation connects data features, noisy labels, and aggregated labels. Since each image contains valuable knowledge about its label, the data features help aggregation methods enhance their performance. We propose LABNET an iterative two-step method. Step one: the label aggregation algorithm provides labels to train the DNN. Step two: the DNN shares a representation of the data features with the label aggregation algorithm. These steps are repeated until the converging label aggregation error rate. To evaluate LABNET we conduct an extensive empirical comparison on CIFAR-10 and CIFAR-100 under different noise and worker statistics. Our evaluation results show that LABNET achieves the highest mean accuracy with an increase of at least 8% to 0.6% and lowest error rate with a reduction of 7.5% to 0.25% against existing aggregation and training methods in most cases.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ismail Irmakci, Michael B. Wallace, Candice W. Bolan, Megan Engels, Sanne Hoogenboom, Marco Aldinucci, Ulas Bagci, Daniela Giordano, Concetto Spampinato
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images Proceedings Article
In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022, pp. 475–479, IEEE, 2022.
@inproceedings{DBLP:conf/embc/SalanitriBPIWBE22,
title = {Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images},
author = {Federica Proietto Salanitri and Giovanni Bellitto and Simone Palazzo and Ismail Irmakci and Michael B. Wallace and Candice W. Bolan and Megan Engels and Sanne Hoogenboom and Marco Aldinucci and Ulas Bagci and Daniela Giordano and Concetto Spampinato},
url = {https://doi.org/10.1109/EMBC48229.2022.9871547},
doi = {10.1109/EMBC48229.2022.9871547},
year = {2022},
date = {2022-01-01},
booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022},
pages = {475–479},
publisher = {IEEE},
keywords = {hpc4ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Valentina Cesare, Ugo Becciani, Alberto Vecchiato, Mario Gilberto Lattanzi, Fabio Pitari, Mario Raciti, Giuseppe Tudisco, Marco Aldinucci, Beatrice Bucciarelli
The Gaia AVU-GSR parallel solver: Preliminary studies of a LSQR-based application in perspective of exascale systems Journal Article
In: Astronomy and Computing, pp. 100660, 2022, ISSN: 2213-1337.
Abstract | Links | BibTeX | Tags: eupex
@article{CESARE2022100660,
title = {The Gaia AVU-GSR parallel solver: Preliminary studies of a LSQR-based application in perspective of exascale systems},
author = {Valentina Cesare and Ugo Becciani and Alberto Vecchiato and Mario Gilberto Lattanzi and Fabio Pitari and Mario Raciti and Giuseppe Tudisco and Marco Aldinucci and Beatrice Bucciarelli},
url = {https://openaccess.inaf.it/handle/20.500.12386/32451},
doi = {10.1016/j.ascom.2022.100660},
issn = {2213-1337},
year = {2022},
date = {2022-01-01},
journal = {Astronomy and Computing},
pages = {100660},
abstract = {The Gaia Astrometric Verification Unit–Global Sphere Reconstruction (AVU–GSR) Parallel Solver aims to find the astrometric parameters for circa 10^8 stars in the Milky Way, the attitude and the instrumental specifications of the Gaia satellite, and the global parameter γ of the post Newtonian formalism. The code iteratively solves a system of linear equations, A×x=b, where the coefficient matrix A is large (circa 10^11×10^8 elements) and sparse. To solve this system of equations, the code exploits a hybrid implementation of the iterative PC-LSQR algorithm, where the computation related to different horizontal portions of the coefficient matrix is assigned to separate MPI processes. In the original code, each matrix portion is further parallelized over the OpenMP threads. To further improve the code performance, we ported the application to the GPU, replacing the OpenMP parallelization language with OpenACC. In this port, ∼95% of the data is copied from the host to the device at the beginning of the entire cycle of iterations, making the code compute bound rather than data-transfer bound. The OpenACC code presents a speedup of circa 1.5 over the OpenMP version but further optimizations are in progress to obtain higher gains. The code runs on multiple GPUs and it was tested on the CINECA supercomputer Marconi100, in anticipation of a port to the pre-exascale system Leonardo, that will be installed at CINECA in 2022.},
keywords = {eupex},
pubstate = {published},
tppubtype = {article}
}
Giovanni Agosta, Marco Aldinucci, Carlos Alvarez, Roberto Ammendola, Yasir Arfat, Olivier Beaumont, Massimo Bernaschi, Andrea Biagioni, Tommaso Boccali, Berenger Bramas, Carlo Brandolese, Barbara Cantalupo, Mauro Carrozzo, Daniele Cattaneo, Alessandro Celestini, Massimo Celino, Iacopo Colonnelli, Paolo Cretaro, Pasqua D'Ambra, Marco Danelutto, Roberto Esposito, Lionel Eyraud-Dubois, Antonio Filgueras, William Fornaciari, Ottorino Frezza, Andrea Galimberti, Francesco Giacomini, Brice Goglin, Daniele Gregori, Abdou Guermouche, Francesco Iannone, Michal Kulczewski, Francesca Lo Cicero, Alessandro Lonardo, Alberto R. Martinelli, Michele Martinelli, Xavier Martorell, Giuseppe Massari, Simone Montangero, Gianluca Mittone, Raymond Namyst, Ariel Oleksiak, Paolo Palazzari, Pier Stanislao Paolucci, Federico Reghenzani, Cristian Rossi, Sergio Saponara, Francesco Simula, Federico Terraneo, Samuel Thibault, Massimo Torquati, Matteo Turisini, Piero Vicini, Miquel Vidal, Davide Zoni, Giuseppe Zummo
Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach Journal Article
In: Microprocessors and Microsystems, vol. 95, pp. 104679, 2022, ISSN: 0141-9331.
Abstract | Links | BibTeX | Tags: textarossa
@article{textarossa2022micpro:,
title = {Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach},
author = {Giovanni Agosta and Marco Aldinucci and Carlos Alvarez and Roberto Ammendola and Yasir Arfat and Olivier Beaumont and Massimo Bernaschi and Andrea Biagioni and Tommaso Boccali and Berenger Bramas and Carlo Brandolese and Barbara Cantalupo and Mauro Carrozzo and Daniele Cattaneo and Alessandro Celestini and Massimo Celino and Iacopo Colonnelli and Paolo Cretaro and Pasqua D'Ambra and Marco Danelutto and Roberto Esposito and Lionel Eyraud-Dubois and Antonio Filgueras and William Fornaciari and Ottorino Frezza and Andrea Galimberti and Francesco Giacomini and Brice Goglin and Daniele Gregori and Abdou Guermouche and Francesco Iannone and Michal Kulczewski and Francesca Lo Cicero and Alessandro Lonardo and Alberto R. Martinelli and Michele Martinelli and Xavier Martorell and Giuseppe Massari and Simone Montangero and Gianluca Mittone and Raymond Namyst and Ariel Oleksiak and Paolo Palazzari and Pier Stanislao Paolucci and Federico Reghenzani and Cristian Rossi and Sergio Saponara and Francesco Simula and Federico Terraneo and Samuel Thibault and Massimo Torquati and Matteo Turisini and Piero Vicini and Miquel Vidal and Davide Zoni and Giuseppe Zummo},
doi = {10.1016/j.micpro.2022.104679},
issn = {0141-9331},
year = {2022},
date = {2022-01-01},
journal = {Microprocessors and Microsystems},
volume = {95},
pages = {104679},
abstract = {In the near future, Exascale systems will need to bridge three technology gaps to achieve high performance while remaining under tight power constraints: energy efficiency and thermal control; extreme computation efficiency via HW acceleration and new arithmetic; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA addresses these gaps through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models, and tools derived from European research.},
keywords = {textarossa},
pubstate = {published},
tppubtype = {article}
}
Bruno Casella, Roberto Esposito, Carlo Cavazzoni, Marco Aldinucci
Benchmarking FedAvg and FedCurv for Image Classification Tasks Proceedings Article
In: Anisetti, Marco, Bonifati, Angela, Bena, Nicola, Ardagna, Claudio, Malerba, Donato (Ed.): Proceedings of the 1st Italian Conference on Big Data and Data Science, ITADATA 2022, September 20-21, 2022, CEUR-WS.org, 2022.
Abstract | Links | BibTeX | Tags: eupilot
@inproceedings{casella2022benchmarking,
title = {Benchmarking FedAvg and FedCurv for Image Classification Tasks},
author = {Bruno Casella and Roberto Esposito and Carlo Cavazzoni and Marco Aldinucci},
editor = {Marco Anisetti and Angela Bonifati and Nicola Bena and Claudio Ardagna and Donato Malerba},
url = {https://ceur-ws.org/Vol-3340/paper40.pdf},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the 1st Italian Conference on Big Data and Data Science, ITADATA 2022, September 20-21, 2022},
volume = {3340},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {Classic Machine Learning (ML) techniques require training on data available in a single data lake (either centralized or distributed). However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres. Federated Learning (FL) is a distributed machine learning approach which has emerged as an effective way to address privacy concerns by only sharing local AI models while keeping the data decentralized. Two critical challenges of Federated Learning are managing the heterogeneous systems in the same federated network and dealing with real data, which are often not independently and identically distributed (non-IID) among the clients. In this paper, we focus on the second problem, i.e., the problem of statistical heterogeneity of the data in the same federated network. In this setting, local models might be strayed far from the local optimum of the complete dataset, thus possibly hindering the convergence of the federated model. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv), aiming at tackling the non-IID setting, have already been proposed. This work provides an empirical assessment of the behaviour of FedAvg and FedCurv in common non-IID scenarios. Results show that the number of epochs per round is an important hyper-parameter that, when tuned appropriately, can lead to significant performance gains while reducing the communication cost. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.},
keywords = {eupilot},
pubstate = {published},
tppubtype = {inproceedings}
}
Marco Aldinucci, David Atienza, Federico Bolelli, Mónica Caballero, Iacopo Colonnelli, José Flich, Jon Ander Gómez, David González, Costantino Grana, Marco Grangetto, Simone Leo, Pedro López, Dana Oniga, Roberto Paredes, Luca Pireddu, Eduardo Quiñones, Tatiana Silva, Enzo Tartaglione, Marina Zapater
In: Curry, Edward, Auer, Sören, Berre, Arne J., Metzger, Andreas, Perez, Maria S., Zillner, Sonja (Ed.): Technologies and Applications for Big Data Value, pp. 183–202, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-78307-5.
Abstract | Links | BibTeX | Tags: deephealth, streamflow
@incollection{22:TABDV,
title = {The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures},
author = {Marco Aldinucci and David Atienza and Federico Bolelli and Mónica Caballero and Iacopo Colonnelli and José Flich and Jon Ander Gómez and David González and Costantino Grana and Marco Grangetto and Simone Leo and Pedro López and Dana Oniga and Roberto Paredes and Luca Pireddu and Eduardo Quiñones and Tatiana Silva and Enzo Tartaglione and Marina Zapater},
editor = {Edward Curry and Sören Auer and Arne J. Berre and Andreas Metzger and Maria S. Perez and Sonja Zillner},
url = {https://link.springer.com/content/pdf/10.1007/978-3-030-78307-5_9.pdf},
doi = {10.1007/978-3-030-78307-5_9},
isbn = {978-3-030-78307-5},
year = {2022},
date = {2022-01-01},
booktitle = {Technologies and Applications for Big Data Value},
pages = {183–202},
publisher = {Springer International Publishing},
address = {Cham},
chapter = {9},
abstract = {At the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quickly. The European Union is dedicating a lot of resources to maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools, providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others.},
keywords = {deephealth, streamflow},
pubstate = {published},
tppubtype = {incollection}
}
Bruno Casella, Alessio Chisari, Sebastiano Battiato, Mario Giuffrida
Transfer Learning via Test-time Neural Networks Aggregation Proceedings Article
In: Farinella, Giovanni Maria, Radeva, Petia, Bouatouch, Kadi (Ed.): Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022, Volume 5: VISAPP, Online Streaming, February 6-8, 2022, pp. 642–649, INSTICC SciTePress, 2022, ISBN: 978-989-758-555-5.
Abstract | Links | BibTeX | Tags: ai
@inproceedings{22:VISAPP:transferlearning,
title = {Transfer Learning via Test-time Neural Networks Aggregation},
author = {Bruno Casella and Alessio Chisari and Sebastiano Battiato and Mario Giuffrida},
editor = {Giovanni Maria Farinella and Petia Radeva and Kadi Bouatouch},
url = {https://iris.unito.it/retrieve/handle/2318/1844159/947123/TRANSFER_LEARNING_VIA_TEST_TIME_NEURAL_NETWORKS_AGGREGATION.pdf},
doi = {10.5220/0010907900003124},
isbn = {978-989-758-555-5},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022, Volume 5: VISAPP, Online Streaming, February 6-8, 2022},
pages = {642–649},
publisher = {SciTePress},
organization = {INSTICC},
abstract = {It has been demonstrated that deep neural networks outperform traditional machine learning. However, deep networks lack generalisability, that is, they will not perform as good as in a new (testing) set drawn from a different distribution due to the domain shift. In order to tackle this known issue, several transfer learning approaches have been proposed, where the knowledge of a trained model is transferred into another to improve performance with different data. However, most of these approaches require additional training steps, or they suffer from catastrophic forgetting that occurs when a trained model has overwritten previously learnt knowledge. We address both problems with a novel transfer learning approach that uses network aggregation. We train dataset-specific networks together with an aggregation network in a unified framework. The loss function includes two main components: a task-specific loss (such as cross-entropy) and an aggregation loss. The proposed aggregation loss allows our model to learn how trained deep network parameters can be aggregated with an aggregation operator. We demonstrate that the proposed approach learns model aggregation at test time without any further training step, reducing the burden of transfer learning to a simple arithmetical operation. The proposed approach achieves comparable performance w.r.t. the baseline. Besides, if the aggregation operator has an inverse, we will show that our model also inherently allows for selective forgetting, i.e., the aggregated model can forget one of the datasets it was trained on, retaining information on the others.},
keywords = {ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Eduardo Quiñones, Jesus Perales, Jorge Ejarque, Asaf Badouh, Santiago Marco, Fabrice Auzanneau, François Galea, David González, José Ramón Hervás, Tatiana Silva, Iacopo Colonnelli, Barbara Cantalupo, Marco Aldinucci, Enzo Tartaglione, Rafael Tornero, José Flich, Jose Maria Martinez, David Rodriguez, Izan Catalán, Jorge Garcia, Carles Hernández
In: Terzo, Olivier, Martinovič, Jan (Ed.): HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, pp. 191–216, CRC Press, Boca Raton, Florida, 2022, ISBN: 978-1-0320-0984-1.
Abstract | Links | BibTeX | Tags: deephealth, streamflow
@incollection{22:deephealth:HPCbook,
title = {The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud Computing Infrastructures for IA-based Medical Solutions},
author = {Eduardo Quiñones and Jesus Perales and Jorge Ejarque and Asaf Badouh and Santiago Marco and Fabrice Auzanneau and François Galea and David González and José Ramón Hervás and Tatiana Silva and Iacopo Colonnelli and Barbara Cantalupo and Marco Aldinucci and Enzo Tartaglione and Rafael Tornero and José Flich and Jose Maria Martinez and David Rodriguez and Izan Catalán and Jorge Garcia and Carles Hernández},
editor = {Olivier Terzo and Jan Martinovič},
url = {https://iris.unito.it/retrieve/handle/2318/1832050/912413/Preprint.pdf},
doi = {10.1201/9781003176664},
isbn = {978-1-0320-0984-1},
year = {2022},
date = {2022-01-01},
booktitle = {HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision},
pages = {191–216},
publisher = {CRC Press},
address = {Boca Raton, Florida},
chapter = {10},
abstract = {This chapter presents the DeepHealth HPC toolkit for an efficient execution of deep learning (DL) medical application into HPC and cloud-computing infrastructures, featuring many-core, GPU, and FPGA acceleration devices. The toolkit offers to the European Computer Vision Library and the European Distributed Deep Learning Library (EDDL), developed in the DeepHealth project as well, the mechanisms to distribute and parallelize DL operations on HPC and cloud infrastructures in a fully transparent way. The toolkit implements workflow managers used to orchestrate HPC workloads for an efficient parallelization of EDDL training operations on HPC and cloud infrastructures, and includes the parallel programming models for an efficient execution EDDL inference and training operations on many-core, GPUs and FPGAs acceleration devices.},
keywords = {deephealth, streamflow},
pubstate = {published},
tppubtype = {incollection}
}
Martin Golasowski, Jan Martinovič, Marc Levrier, Stephan Hachinger, Sophia Karagiorgou, Aikaterini Papapostolou, Spiros Mouzakitis, Ioannis Tsapelas, Monica Caballero, Marco Aldinucci, Jon Ander Gómez, Antony Chazapis, Jean-Thomas Acquaviva
Toward the Convergence of High-Performance Computing, Cloud, and Big Data Domains Book Section
In: Terzo, Olivier, Martinovič, Jan (Ed.): HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, pp. 1–16, CRC Press, Boca Raton, Florida, 2022, ISBN: 978-1-0320-0984-1.
Abstract | Links | BibTeX | Tags: deephealth, streamflow
@incollection{22:intro:HPCbook,
title = {Toward the Convergence of High-Performance Computing, Cloud, and Big Data Domains},
author = {Martin Golasowski and Jan Martinovič and Marc Levrier and Stephan Hachinger and Sophia Karagiorgou and Aikaterini Papapostolou and Spiros Mouzakitis and Ioannis Tsapelas and Monica Caballero and Marco Aldinucci and Jon Ander Gómez and Antony Chazapis and Jean-Thomas Acquaviva},
editor = {Olivier Terzo and Jan Martinovič},
doi = {10.1201/9781003176664},
isbn = {978-1-0320-0984-1},
year = {2022},
date = {2022-01-01},
booktitle = {HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision},
pages = {1–16},
publisher = {CRC Press},
address = {Boca Raton, Florida},
chapter = {1},
abstract = {Convergence between big data, high-performance computing, and the cloud is the key driving factor for sustainable economic growth in the future. Technological advances in many fields are determined by competence to gain precise information from the large amounts of data collected, which in turn requires powerful computing resources. This chapter provides an overview on the evolution of the three fields and four different points of view on their convergence provided by the CYBELE, DeepHealth, Evolve, and LEXIS projects funded by the European Union under the Horizon 2020 Programme.},
keywords = {deephealth, streamflow},
pubstate = {published},
tppubtype = {incollection}
}
Dana Oniga, Barbara Cantalupo, Enzo Tartaglione, Daniele Perlo, Marco Grangetto, Marco Aldinucci, Federico Bolelli, Federico Pollastri, Michele Cancilla, Laura Canalini, Costantino Grana, Cristina Muñoz Alcalde, Franco Alberto Cardillo, Monica Florea
Applications of AI and HPC in the Health Domain Book Section
In: Terzo, Olivier, Martinovič, Jan (Ed.): HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, pp. 217–239, CRC Press, Boca Raton, Florida, 2022, ISBN: 978-1-0320-0984-1.
Abstract | Links | BibTeX | Tags: deephealth, streamflow
@incollection{22:applications:HPCbook,
title = {Applications of AI and HPC in the Health Domain},
author = {Dana Oniga and Barbara Cantalupo and Enzo Tartaglione and Daniele Perlo and Marco Grangetto and Marco Aldinucci and Federico Bolelli and Federico Pollastri and Michele Cancilla and Laura Canalini and Costantino Grana and Cristina Muñoz Alcalde and Franco Alberto Cardillo and Monica Florea},
editor = {Olivier Terzo and Jan Martinovič},
doi = {10.1201/9781003176664},
isbn = {978-1-0320-0984-1},
year = {2022},
date = {2022-01-01},
booktitle = {HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision},
pages = {217–239},
publisher = {CRC Press},
address = {Boca Raton, Florida},
chapter = {11},
abstract = {This chapter presents the applications of artificial intelligence (AI) and high-computing performance (HPC) in the health domain, illustrated by the description of five of the use cases that are developed in the DeepHealth project. In the context of the European Commission supporting the use of AI and HPC in the health sector, DeepHealth Project is helping health experts process large quantities of images, putting at their disposal DeepLearning and computer vision techniques, combined in the DeepHealth toolkit and HPC infrastructures. The DeepHealth toolkit is tested and validated through 15 use cases, each of them representing a biomedical application. The most promising use cases are described in the chapter, which concludes with the value proposition and the benefits that DeepHealth toolkit offers to future end users.},
keywords = {deephealth, streamflow},
pubstate = {published},
tppubtype = {incollection}
}
Iacopo Colonnelli, Marco Aldinucci, Barbara Cantalupo, Luca Padovani, Sergio Rabellino, Concetto Spampinato, Roberto Morelli, Rosario Di Carlo, Nicolò Magini, Carlo Cavazzoni
Distributed workflows with Jupyter Journal Article
In: Future Generation Computer Systems, vol. 128, pp. 282–298, 2022, ISSN: 0167-739X.
Abstract | Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow
@article{21:FGCS:jupyflow,
title = {Distributed workflows with Jupyter},
author = {Iacopo Colonnelli and Marco Aldinucci and Barbara Cantalupo and Luca Padovani and Sergio Rabellino and Concetto Spampinato and Roberto Morelli and Rosario Di Carlo and Nicolò Magini and Carlo Cavazzoni},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X21003976},
doi = {10.1016/j.future.2021.10.007},
issn = {0167-739X},
year = {2022},
date = {2022-01-01},
journal = {Future Generation Computer Systems},
volume = {128},
pages = {282–298},
abstract = {The designers of a new coordination interface enacting complex workflows have to tackle a dichotomy: choosing a language-independent or language-dependent approach. Language-independent approaches decouple workflow models from the host code's business logic and advocate portability. Language-dependent approaches foster flexibility and performance by adopting the same host language for business and coordination code. Jupyter Notebooks, with their capability to describe both imperative and declarative code in a unique format, allow taking the best of the two approaches, maintaining a clear separation between application and coordination layers but still providing a unified interface to both aspects. We advocate the Jupyter Notebooks' potential to express complex distributed workflows, identifying the general requirements for a Jupyter-based Workflow Management System (WMS) and introducing a proof-of-concept portable implementation working on hybrid Cloud-HPC infrastructures. As a byproduct, we extended the vanilla IPython kernel with workflow-based parallel and distributed execution capabilities. The proposed Jupyter-workflow (Jw) system is evaluated on common scenarios for High Performance Computing (HPC) and Cloud, showing its potential in lowering the barriers between prototypical Notebooks and production-ready implementations.},
keywords = {across, deephealth, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {article}
}
Bart Cox, Robert Birke, Lydia Y. Chen
Memory-aware and context-aware multi-DNN inference on the edge Journal Article
In: Pervasive and Mobile Computing, vol. 83, pp. 1–16, 2022, ISSN: 1574-1192.
Abstract | Links | BibTeX | Tags: ai
@article{COX2022101594,
title = {Memory-aware and context-aware multi-DNN inference on the edge},
author = {Bart Cox and Robert Birke and Lydia Y. Chen},
url = {https://www.sciencedirect.com/science/article/pii/S1574119222000372},
doi = {https://doi.org/10.1016/j.pmcj.2022.101594},
issn = {1574-1192},
year = {2022},
date = {2022-01-01},
journal = {Pervasive and Mobile Computing},
volume = {83},
pages = {1–16},
abstract = {Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted by executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. It is of paramount importance to guarantee low response times of such multi-DNN executions as it affects not only users quality of experience but also safety. The challenge, largely unaddressed by the state of the art, is how to overcome the memory limitation of edge devices without altering the DNN models. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution and scheduling framework, which requires no modification of DNN models. The aim of Masa is to consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. The enabling features of Masa are (i) modeling inter- and intra-network dependency, (ii) leveraging complimentary memory usage of each layer, and (iii) exploring the context dependency of DNNs. We verify the correctness and scheduling optimality via mixed integer programming. We extensively evaluate two versions of Masa, context-oblivious and context-aware, on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Guglielmo Gallone, Jeehoon Kang, Francesco Bruno, Jung-Kyu Han, Ovidio De Filippo, Han-Mo Yang, Mattia Doronzo, Kyung-Woo Park, Gianluca Mittone, Hyun-Jae Kang, Radoslaw Parma, Hyeon-Cheol Gwon, Enrico Cerrato, Woo Jung Chun, Grzegorz Smolka, Seung-Ho Hur, Gerard Helft, Seung Hwan Han, Saverio Muscoli, Young Bin Song, Filippo Figini, Ki Hong Choi, Giacomo Boccuzzi, Soon-Jun Hong, Daniela Trabattoni, Chang-Wook Nam, Massimo Giammaria, Hyo-Soo Kim, Federico Conrotto, Javier Escaned, Carlo Di Mario, Fabrizio D'Ascenzo, Bon-Kwon Koo, Gaetano Maria Ferrari
Impact of Left Ventricular Ejection Fraction on Procedural and Long-Term Outcomes of Bifurcation Percutaneous Coronary Intervention Journal Article
In: The American Journal of Cardiology, vol. 172, pp. 18–25, 2022, ISSN: 0002-9149.
Abstract | Links | BibTeX | Tags: ai, cardio
@article{GALLONE202218,
title = {Impact of Left Ventricular Ejection Fraction on Procedural and Long-Term Outcomes of Bifurcation Percutaneous Coronary Intervention},
author = {Guglielmo Gallone and Jeehoon Kang and Francesco Bruno and Jung-Kyu Han and Ovidio De Filippo and Han-Mo Yang and Mattia Doronzo and Kyung-Woo Park and Gianluca Mittone and Hyun-Jae Kang and Radoslaw Parma and Hyeon-Cheol Gwon and Enrico Cerrato and Woo Jung Chun and Grzegorz Smolka and Seung-Ho Hur and Gerard Helft and Seung Hwan Han and Saverio Muscoli and Young Bin Song and Filippo Figini and Ki Hong Choi and Giacomo Boccuzzi and Soon-Jun Hong and Daniela Trabattoni and Chang-Wook Nam and Massimo Giammaria and Hyo-Soo Kim and Federico Conrotto and Javier Escaned and Carlo Di Mario and Fabrizio D'Ascenzo and Bon-Kwon Koo and Gaetano Maria Ferrari},
url = {https://www.sciencedirect.com/science/article/pii/S0002914922001692},
doi = {https://doi.org/10.1016/j.amjcard.2022.02.015},
issn = {0002-9149},
year = {2022},
date = {2022-01-01},
journal = {The American Journal of Cardiology},
volume = {172},
pages = {18–25},
abstract = {The association of left ventricular ejection fraction (LVEF) with procedural and long-term outcomes after state-of-the-art percutaneous coronary intervention (PCI) of bifurcation lesions remains unsettled. A total of 5,333 patients who underwent contemporary coronary bifurcation PCI were included in the intercontinental retrospective combined insights from the unified RAIN (veRy thin stents for patients with left mAIn or bifurcatioN in real life) and COBIS (COronary BIfurcation Stenting) III bifurcation registries. Of 5,003 patients (93.8%) with known baseline LVEF, 244 (4.9%) had LVEF <40% (bifurcation with reduced ejection fraction [BIFrEF] group), 430 (8.6%) had LVEF 40% to 49% (bifurcation with mildly reduced ejection fraction [BIFmEF] group) and 4,329 (86.5%) had ejection fraction (EF) ≥50% (bifurcation with preserved ejection fraction [BIFpEF] group). The primary end point was the Kaplan-Meier estimate of major adverse cardiac events (MACEs) (a composite of all-cause death, myocardial infarction, and target vessel revascularization). Patients with BIFrEF had a more complex clinical profile and coronary anatomy. No difference in procedural (30 days) MACE was observed across EF categories, also after adjustment for in-study outcome predictors (BIFrEF vs BIFmEF: adjusted hazard ratio [adj-HR] 1.39, 95% confidence interval [CI] 0.37 to 5.21},
keywords = {ai, cardio},
pubstate = {published},
tppubtype = {article}
}
Adriano Marques Garcia, Dalvan Griebler, Claudio Schepke, Luiz Gustavo Fernandes
SPBench: a framework for creating benchmarks of stream processing applications Journal Article
In: Computing, vol. 105, no. 5, pp. 1077-1099, 2022, ISBN: 1436-5057.
Abstract | Links | BibTeX | Tags: parallel
@article{GARCIA:Computing:22,
title = {SPBench: a framework for creating benchmarks of stream processing applications},
author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes},
url = {https://iris.unito.it/retrieve/f17ea8c2-ddd8-425b-b4e7-8315218a6969/s00607-021-01025-6_preprint.pdf},
doi = {10.1007/s00607-021-01025-6},
isbn = {1436-5057},
year = {2022},
date = {2022-01-01},
journal = {Computing},
volume = {105},
number = {5},
pages = {1077-1099},
publisher = {Springer},
abstract = {In a fast-changing data-driven world, real-time data processing systems are becoming ubiquitous in everyday applications. The increasing data we produce, such as audio, video, image, and, text are demanding quickly and efficiently computation. Stream Parallelism allows accelerating this computation for real-time processing. But it is still a challenging task and most reserved for experts. In this paper, we present SPBench, a framework for benchmarking stream processing applications. It aims to support users with a set of real-world stream processing applications, which are made accessible through an Application Programming Interface (API) and executable via Command Line Interface (CLI) to create custom benchmarks. We tested SPBench by implementing parallel benchmarks with Intel Threading Building Blocks (TBB), FastFlow, and SPar. This evaluation provided useful insights and revealed the feasibility of the proposed framework in terms of usage, customization, and performance analysis. SPBench demonstrated to be a high-level, reusable, extensible, and easy of use abstraction to build parallel stream processing benchmarks on multi-core architectures.},
keywords = {parallel},
pubstate = {published},
tppubtype = {article}
}
2021
Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise Proceedings Article
In: 8th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), pp. 52–62, ACM, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{bdcat-ghiassi21,
title = {TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise},
author = {Amirmasoud Ghiassi and Robert Birke and Lydia Y. Chen},
url = {https://doi.org/10.1145/3492324.3494166},
doi = {10.1145/3492324.3494166},
year = {2021},
date = {2021-12-01},
booktitle = {8th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)},
pages = {52–62},
publisher = {ACM},
abstract = {Big Data systems allow collecting massive datasets to feed the data hungry deep learning. Labelling these ever-bigger datasets is increasingly challenging and label errors affect even highly curated sets. This makes robustness to label noise a critical property for weakly-supervised classifiers. The related works on resilient deep networks tend to focus on a limited set of synthetic noise patterns, and with disparate views on their impacts, e.g., robustness against symmetric v.s. asymmetric noise patterns. In this paper, we first extend the theoretical analysis of test accuracy for any given noise patterns. Based on the insights, we design TrustNet that first learns the pattern of noise corruption, being it both symmetric or asymmetric, from a small set of trusted data. Then, TrustNet is trained via a robust loss function, which weights the given labels against the inferred labels from the learned noise pattern. The weight is adjusted based on model uncertainty across training epochs. We evaluate TrustNet on synthetic label noise for CIFAR-10, CIFAR-100 and big real-world data with label noise, i.e., Clothing1M. We compare against state-of-the-art methods demonstrating the strong robustness of TrustNet under a diverse set of noise patterns.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zilong Zhao, Aditya Kunar, Robert Birke, Lydia Y. Chen
CTAB-GAN: Effective Table Data Synthesizing Proceedings Article
In: Balasubramanian, Vineeth N., Tsang, Ivor (Ed.): Proceedings of The 13th Asian Conference on Machine Learning, pp. 97–112, PMLR, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{pmlr-v157-zhao21a,
title = {CTAB-GAN: Effective Table Data Synthesizing},
author = {Zilong Zhao and Aditya Kunar and Robert Birke and Lydia Y. Chen},
editor = {Vineeth N. Balasubramanian and Ivor Tsang},
url = {https://proceedings.mlr.press/v157/zhao21a.html},
year = {2021},
date = {2021-11-01},
booktitle = {Proceedings of The 13th Asian Conference on Machine Learning},
volume = {157},
pages = {97–112},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
abstract = {While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GAN) and address two main data types in industry, i.e., continuous and categorical. In this paper, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types, including a mix of continuous and categorical variables. Moreover, we address data imbalance and long tail issues, i.e., certain variables have drastic frequency differences across large values. To achieve those aims, we first introduce the information loss, classification loss and generator loss to the conditional GAN. Secondly, we design a novel conditional vector, which efficiently encodes the mixed data type and skewed distribution of data variable. We extensively evaluate CTAB-GAN with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results into higher accuracy for five machine learning algorithms, by up to 17%.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Taraneh Younesian, Zilong Zhao, Amirmasoud Ghiassi, Robert Birke, Lydia Y Chen
QActor: Active Learning on Noisy Labels Proceedings Article
In: Balasubramanian, Vineeth N., Tsang, Ivor (Ed.): Proceedings of The 13th Asian Conference on Machine Learning, pp. 548–563, PMLR, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{pmlr-v157-younesian21a,
title = {QActor: Active Learning on Noisy Labels},
author = {Taraneh Younesian and Zilong Zhao and Amirmasoud Ghiassi and Robert Birke and Lydia Y Chen},
editor = {Vineeth N. Balasubramanian and Ivor Tsang},
url = {https://proceedings.mlr.press/v157/younesian21a.html},
year = {2021},
date = {2021-11-01},
booktitle = {Proceedings of The 13th Asian Conference on Machine Learning},
volume = {157},
pages = {548–563},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
abstract = {Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media from non-experts. Active querying experts are conventionally adopted to provide labels for the informative samples which don't have labels, instead of possibly incorrect labels. The new challenge that arises here is how to discern the informative and noisy labels which benefit from expert cleaning. In this paper, we aim to leverage the stringent oracle budget to robustly maximize learning accuracy. We propose a noise-aware active learning framework, QActor, and a novel measure emphCENT, which considers both cross-entropy and entropy to select informative and noisy labels for an expert cleansing. QActor iteratively cleans samples via quality models and actively querying an expert on those noisy yet informative samples. To adapt to learning capacity per iteration, QActor dynamically adjusts the query limit according to the learning loss for each learning iteration. We extensively evaluate different image datasets with noise label ratios ranging between 30% and 60%. Our results show that QActor can nearly match the optimal accuracy achieved using only clean data at the cost of only an additional 10% of ground truth data from the oracle.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Giuliano Albanese, Robert Birke, Georgia Giannopoulou, Sandro Schönborn, Thanikesavan Sivanthi
Evaluation of Networking Options for Containerized Deployment of Real-Time Applications Proceedings Article
In: 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8, IEEE, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{etfa-albanese21,
title = {Evaluation of Networking Options for Containerized Deployment of Real-Time Applications},
author = {Giuliano Albanese and Robert Birke and Georgia Giannopoulou and Sandro Schönborn and Thanikesavan Sivanthi},
url = {https://doi.org/10.1109/ETFA45728.2021.9613320},
doi = {10.1109/ETFA45728.2021.9613320},
year = {2021},
date = {2021-09-01},
booktitle = {26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)},
pages = {1–8},
publisher = {IEEE},
abstract = {Enterprises in the field of industrial automation experience an increasing demand for providing virtualized software solutions. Inspired by the recent trends in serverless and cloud computing, software virtualization is considered even for safety-critical applications with hard real-time requirements, as a means of avoiding hardware vendor lock-in and reducing volume and maintenance cost of devices. In this work, we evaluate the applicability of OS-level virtualization to an industrial automation use case. Our application runs in Docker containers on top of Linux patched with PREEMPT_RT. We investigate the ability of Docker coupled with diverse networking technologies to fulfill the latency requirements of the application under normal or heavy system load. We empirically compare four networking technologies with respect to communication latency and frequency of missing packets. The results indicate that Docker with certain technologies, such as the Single Root I/O Virtualization interface, performs robustly even under heavy load, enabling sufficient performance isolation and low overhead that does not jeopardise the real-time performance of our application.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Giovanni Agosta, William Fornaciari, Andrea Galimberti, Giuseppe Massari, Federico Reghenzani, Federico Terraneo, Davide Zoni, Carlo Brandolese, Massimo Celino, Francesco Iannone, Paolo Palazzari, Giuseppe Zummo, Massimo Bernaschi, Pasqua D'Ambra, Sergio Saponara, Marco Danelutto, Massimo Torquati, Marco Aldinucci, Yasir Arfat, Barbara Cantalupo, Iacopo Colonnelli, Roberto Esposito, Alberto Riccardo Martinelli, Gianluca Mittone, Olivier Beaumont, Berenger Bramas, Lionel Eyraud-Dubois, Brice Goglin, Abdou Guermouche, Raymond Namyst, Samuel Thibault, Antonio Filgueras, Miquel Vidal, Carlos Alvarez, Xavier Martorell, Ariel Oleksiak, Michal Kulczewski, Alessandro Lonardo, Piero Vicini, Francesco Lo Cicero, Francesco Simula, Andrea Biagioni, Paolo Cretaro, Ottorino Frezza, Pier Stanislao Paolucci, Matteo Turisini, Francesco Giacomini, Tommaso Boccali, Simone Montangero, Roberto Ammendola
TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale Proceedings Article
In: Proc. of the 24th Euromicro Conference on Digital System Design (DSD), IEEE, Palermo, Italy, 2021.
Abstract | Links | BibTeX | Tags: streamflow, textarossa
@inproceedings{21:DSD:textarossa,
title = {TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale},
author = {Giovanni Agosta and William Fornaciari and Andrea Galimberti and Giuseppe Massari and Federico Reghenzani and Federico Terraneo and Davide Zoni and Carlo Brandolese and Massimo Celino and Francesco Iannone and Paolo Palazzari and Giuseppe Zummo and Massimo Bernaschi and Pasqua D'Ambra and Sergio Saponara and Marco Danelutto and Massimo Torquati and Marco Aldinucci and Yasir Arfat and Barbara Cantalupo and Iacopo Colonnelli and Roberto Esposito and Alberto Riccardo Martinelli and Gianluca Mittone and Olivier Beaumont and Berenger Bramas and Lionel Eyraud-Dubois and Brice Goglin and Abdou Guermouche and Raymond Namyst and Samuel Thibault and Antonio Filgueras and Miquel Vidal and Carlos Alvarez and Xavier Martorell and Ariel Oleksiak and Michal Kulczewski and Alessandro Lonardo and Piero Vicini and Francesco Lo Cicero and Francesco Simula and Andrea Biagioni and Paolo Cretaro and Ottorino Frezza and Pier Stanislao Paolucci and Matteo Turisini and Francesco Giacomini and Tommaso Boccali and Simone Montangero and Roberto Ammendola},
doi = {10.1109/DSD53832.2021.00051},
year = {2021},
date = {2021-08-01},
booktitle = {Proc. of the 24th Euromicro Conference on Digital System Design (DSD)},
publisher = {IEEE},
address = {Palermo, Italy},
abstract = {To achieve high performance and high energy effi- ciency on near-future exascale computing systems, three key technology gaps needs to be bridged. These gaps include: en- ergy efficiency and thermal control; extreme computation effi- ciency via HW acceleration and new arithmetics; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA aims at tackling this gap through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models and tools derived from European research.},
keywords = {streamflow, textarossa},
pubstate = {published},
tppubtype = {inproceedings}
}
Amirmasoud Ghiassi, Robert Birke, Rui Han, Lydia Y. Chen
LABELNET: Recovering Noisy Labels Proceedings Article
In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8, IEEE, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{ijcnn-ghiassi21,
title = {LABELNET: Recovering Noisy Labels},
author = {Amirmasoud Ghiassi and Robert Birke and Rui Han and Lydia Y. Chen},
url = {https://doi.org/10.1109/IJCNN52387.2021.9533562},
doi = {10.1109/IJCNN52387.2021.9533562},
year = {2021},
date = {2021-07-01},
booktitle = {International Joint Conference on Neural Networks (IJCNN)},
pages = {1–8},
publisher = {IEEE},
abstract = {Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i.e. erroneous, labels. Recent studies improve the robustness of deep models against noisy labels without the knowledge of true labels. In this paper, we advocate to derive a stronger classifier which proactively makes use of the noisy labels in addition to the original images - turning noisy labels into learning features. To such an end, we propose a novel framework, LABELNET, composed of Amateur and Expert, which iteratively learn from each other. Amateur is a regular image classifier trained by the feedback of Expert, which imitates how human experts would correct the predicted labels from Amateur using the noise pattern learnt from the knowledge of both the noisy and ground truth labels. The trained Amateur and Expert proactively leverage the images and their noisy labels to infer image classes. Our empirical evaluations on noisy versions of MNIST, CIFAR-10, CIFAR-100 and real-world data of Clothing1M show that the proposed model can achieve robust classification against a wide range of noise ratios and with as little as 20-50% training data, compared to state-of-the-art deep models that solely focus on distilling the impact of noisy labels.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Marco Aldinucci, Giovanni Agosta, Antonio Andreini, Claudio A. Ardagna, Andrea Bartolini, Alessandro Cilardo, Biagio Cosenza, Marco Danelutto, Roberto Esposito, William Fornaciari, Roberto Giorgi, Davide Lengani, Raffaele Montella, Mauro Olivieri, Sergio Saponara, Daniele Simoni, Massimo Torquati
The Italian research on HPC key technologies across EuroHPC Proceedings Article
In: ACM Computing Frontiers, pp. 279–286, ACM, Virtual Conference, Italy, 2021.
Abstract | Links | BibTeX | Tags: admire, eupex, eupilot, textarossa
@inproceedings{21:CINI_acm_CF,
title = {The Italian research on HPC key technologies across EuroHPC},
author = {Marco Aldinucci and Giovanni Agosta and Antonio Andreini and Claudio A. Ardagna and Andrea Bartolini and Alessandro Cilardo and Biagio Cosenza and Marco Danelutto and Roberto Esposito and William Fornaciari and Roberto Giorgi and Davide Lengani and Raffaele Montella and Mauro Olivieri and Sergio Saponara and Daniele Simoni and Massimo Torquati},
url = {https://iris.unito.it/retrieve/handle/2318/1783118/744641/preprint.pdf},
doi = {10.1145/3457388.3458508},
year = {2021},
date = {2021-05-01},
booktitle = {ACM Computing Frontiers},
pages = {279–286},
publisher = {ACM},
address = {Virtual Conference, Italy},
abstract = {High-Performance Computing (HPC) is one of the strategic priorities for research and innovation worldwide due to its relevance for industrial and scientific applications. We envision HPC as composed of three pillars: infrastructures, applications, and key technologies and tools. While infrastructures are by construction centralized in large-scale HPC centers, and applications are generally within the purview of domain-specific organizations, key technologies fall in an intermediate case where coordination is needed, but design and development are often decentralized. A large group of Italian researchers has started a dedicated laboratory within the National Interuniversity Consortium for Informatics (CINI) to address this challenge. The laboratory, albeit young, has managed to succeed in its first attempts to propose a coordinated approach to HPC research within the EuroHPC Joint Undertaking, participating in the calls 2019-20 to five successful proposals for an aggregate total cost of 95M Euro. In this paper, we outline the working group's scope and goals and provide an overview of the five funded projects, which become fully operational in March 2021, and cover a selection of key technologies provided by the working group partners, highlighting their usage development within the projects.},
keywords = {admire, eupex, eupilot, textarossa},
pubstate = {published},
tppubtype = {inproceedings}
}
Carmelo Pino, Simone Palazzo, Francesca Trenta, Francesca Cordero, Ulas Bagci, Francesco Rundo, Sebastiano Battiato, Daniela Giordano, Marco Aldinucci, Concetto Spampinato
Interpretable Deep Model for Predicting Gene-Addicted Non-Small-Cell Lung Cancer in CT Scans Proceedings Article
In: 18th IEEE Intl. Symposium on Biomedical Imaging (ISBI), IEEE, Nice, France, 2021.
Abstract | Links | BibTeX | Tags: deephealth
@inproceedings{21:ct:isbi,
title = {Interpretable Deep Model for Predicting Gene-Addicted Non-Small-Cell Lung Cancer in CT Scans},
author = {Carmelo Pino and Simone Palazzo and Francesca Trenta and Francesca Cordero and Ulas Bagci and Francesco Rundo and Sebastiano Battiato and Daniela Giordano and Marco Aldinucci and Concetto Spampinato},
url = {https://iris.unito.it/retrieve/handle/2318/1790376/764762/21_ISBI_smallcell.pdf},
doi = {10.1109/ISBI48211.2021.9433832},
year = {2021},
date = {2021-04-01},
booktitle = {18th IEEE Intl. Symposium on Biomedical Imaging (ISBI)},
publisher = {IEEE},
address = {Nice, France},
abstract = {Genetic profiling and characterization of lung cancers have recently emerged as a new technique for targeted therapeutic treatment based on immunotherapy or molecular drugs. However, the most effective way to discover specific gene mutations through tissue biopsy has several limitations, from invasiveness to being a risky procedure. Recently, quantitative assessment of visual features from CT data has been demonstrated to be a valid alternative to biopsy for the diagnosis of gene-addicted tumors. In this paper, we present a deep model for automated lesion segmentation and classification as gene-addicted or not. The segmentation approach extends the 2D Tiramisu architecture for 3D segmentation through dense blocks and squeeze-and-excitation layers, while a multi-scale 3D CNN is used for lesion classification. We also train our model with adversarial samples, and show that this approach acts as a gradient regularizer and enhances model interpretability. We also built a dataset, the first of its nature, consisting of 73 CT scans annotated with the presence of a specific genomics profile. We test our approach on this dataset achieving a segmentation accuracy of 93.11% (Dice score) and a classification accuracy in identifying oncogene-addicted lung tumors of 82.00%.},
keywords = {deephealth},
pubstate = {published},
tppubtype = {inproceedings}
}
Chi Hong, Amirmasoud Ghiassi, Yichi Zhou, Robert Birke, Lydia Y. Chen
Online Label Aggregation: A Variational Bayesian Approach Proceedings Article
In: Leskovec, Jure, Grobelnik, Marko, Najork, Marc, Tang, Jie, Zia, Leila (Ed.): WWW '21: The Web Conference 2021, pp. 1904–1915, ACM / IW3C2, 2021.
Abstract | Links | BibTeX | Tags: ai
@inproceedings{www-hong21,
title = {Online Label Aggregation: A Variational Bayesian Approach},
author = {Chi Hong and Amirmasoud Ghiassi and Yichi Zhou and Robert Birke and Lydia Y. Chen},
editor = {Jure Leskovec and Marko Grobelnik and Marc Najork and Jie Tang and Leila Zia},
url = {https://doi.org/10.1145/3442381.3449933},
doi = {10.1145/3442381.3449933},
year = {2021},
date = {2021-04-01},
booktitle = {WWW '21: The Web Conference 2021},
pages = {1904–1915},
publisher = {ACM / IW3C2},
abstract = {Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregating results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solutions that can incrementally infer true label distribution via subsets of data items. In this paper, we propose a novel online label aggregation framework, BiLA , which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. BiLA is flexible to accommodate any generating distribution of labels by the exact computation of its posterior distribution. We also derive the convergence bound of the proposed optimizer. We compare BiLA with the state of the art based on minimax entropy, neural networks and expectation maximization algorithms, on synthetic and real-world data sets. Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1.5 percent points for synthetic and real-world datasets, respectively.},
keywords = {ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Adriano Marques Garcia, Dalvan Griebler, Claudio Schepke, Luiz Gustavo Fernandes
Introducing a Stream Processing Framework for Assessing Parallel Programming Interfaces Proceedings Article
In: 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 84-88, IEEE, Valladolid, Spain, 2021.
Abstract | Links | BibTeX | Tags: parallel
@inproceedings{GARCIA:PDP:21,
title = {Introducing a Stream Processing Framework for Assessing Parallel Programming Interfaces},
author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes},
url = {https://iris.unito.it/retrieve/8aa73a3f-0b1f-41e4-9440-a87bbaf6e9c4/PDP_2021__Stream_bench_Framework_.pdf},
doi = {10.1109/PDP52278.2021.00021},
year = {2021},
date = {2021-03-01},
booktitle = {29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
pages = {84-88},
publisher = {IEEE},
address = {Valladolid, Spain},
series = {PDP'21},
abstract = {Stream Processing applications are spread across different sectors of industry and people's daily lives. The increasing data we produce, such as audio, video, image, and text are demanding quickly and efficiently computation. It can be done through Stream Parallelism, which is still a challenging task and most reserved for experts. We introduce a Stream Processing framework for assessing Parallel Programming Interfaces (PPIs). Our framework targets multi-core architectures and C++ stream processing applications, providing an API that abstracts the details of the stream operators of these applications. Therefore, users can easily identify all the basic operators and implement parallelism through different PPIs. In this paper, we present the proposed framework, implement three applications using its API, and show how it works, by using it to parallelize and evaluate the applications with the PPIs Intel TBB, FastFlow, and SPar. The performance results were consistent with the literature.},
keywords = {parallel},
pubstate = {published},
tppubtype = {inproceedings}
}
Bart Cox, Jeroen Galjaard, Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
Masa: Responsive Multi-DNN Inference on the Edge Proceedings Article
In: 19th IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–10, IEEE, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{percom-cox21a,
title = {Masa: Responsive Multi-DNN Inference on the Edge},
author = {Bart Cox and Jeroen Galjaard and Amirmasoud Ghiassi and Robert Birke and Lydia Y. Chen},
url = {https://doi.org/10.1109/PERCOM50583.2021.9439111},
doi = {10.1109/PERCOM50583.2021.9439111},
year = {2021},
date = {2021-03-01},
booktitle = {19th IEEE International Conference on Pervasive Computing and Communications (PerCom)},
pages = {1–10},
publisher = {IEEE},
abstract = {Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted via executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. The response times of multi-DNN highly affect users' quality of experience and safety as well. Different DNNs exhibit diversified resource requirements and execution patterns across layers and networks, which may easily exceed the available device memory and riskily degrade the responsiveness. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution framework, an on-device middleware featuring on modeling inter- and intra-network dependency and leveraging complimentary memory usage of each layer. Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. We extensively evaluate Masa on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Jeroen Galjaard, Bart Cox, Amirmasoud Ghiassi, Lydia Y. Chen, Robert Birke
MemA: Fast Inference of Multiple Deep Models Proceedings Article
In: 19th IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, pp. 281–286, IEEE, 2021.
Abstract | Links | BibTeX | Tags:
@inproceedings{percom-galjaard21,
title = {MemA: Fast Inference of Multiple Deep Models},
author = {Jeroen Galjaard and Bart Cox and Amirmasoud Ghiassi and Lydia Y. Chen and Robert Birke},
url = {https://doi.org/10.1109/PerComWorkshops51409.2021.9430952},
doi = {10.1109/PerComWorkshops51409.2021.9430952},
year = {2021},
date = {2021-03-01},
booktitle = {19th IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events},
pages = {281–286},
publisher = {IEEE},
abstract = {The execution of deep neural network (DNN) inference jobs on edge devices has become increasingly popular. Multiple of such inference models can concurrently analyse the on-device data, e.g. images, to extract valuable insights. Prior art focuses on low-power accelerators, compressed neural network architectures, and specialized frameworks to reduce execution time of single inference jobs on edge devices which are resource constrained. However, it is little known how different scheduling policies can further improve the runtime performance of multi-inference jobs without additional edge resources. To enable the exploration of scheduling policies, we first develop an execution framework, EdgeCaffe, which splits the DNN inference jobs by loading and execution of each network layer. We empirically characterize the impact of loading and scheduling policies on the execution time of multi-inference jobs and point out their dependency on the available memory space. We propose a novel memory-aware scheduling policy, MemA, which opportunistically interleaves the executions of different types of DNN layers based on their estimated run-time memory demands. Our evaluation on exhaustive combinations of five networks, data inputs, and memory configurations show that MemA can alleviate the degradation of execution times of multi-inference (up to 5×) under severely constrained memory compared to standard scheduling policies without affecting accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Gianluca Bontempi, Ricardo Chavarriaga, Hans De Canck, Emanuela Girardi, Holger Hoos, Iarla Kilbane‐Dawe, Tonio Ball, Ann Nowé, Jose Sousa, Davide Bacciu, Marco Aldinucci, Manlio De Domenico, Alessandro Saffiotti, Marco Maratea
The CLAIRE COVID-19 initiative: approach, experiences and recommendations Journal Article
In: Ethics and Information Technology, 2021.
Abstract | Links | BibTeX | Tags: deephealth
@article{21:eit:covidclaire,
title = {The CLAIRE COVID-19 initiative: approach, experiences and recommendations},
author = {Gianluca Bontempi and Ricardo Chavarriaga and Hans De Canck and Emanuela Girardi and Holger Hoos and Iarla Kilbane‐Dawe and Tonio Ball and Ann Nowé and Jose Sousa and Davide Bacciu and Marco Aldinucci and Manlio De Domenico and Alessandro Saffiotti and Marco Maratea},
url = {https://iris.unito.it/retrieve/handle/2318/1784271/747923/Bontempi2021_Article_TheCLAIRECOVID-19InitiativeApp-3.pdf},
doi = {10.1007/s10676-020-09567-7},
year = {2021},
date = {2021-02-01},
journal = {Ethics and Information Technology},
publisher = {Springer},
abstract = {A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.},
keywords = {deephealth},
pubstate = {published},
tppubtype = {article}
}
Ivan Lanese, Doriana Medić, Claudio Antares Mezzina
Static versus dynamic reversibility in CCS Journal Article
In: Acta Informatica, vol. 58, pp. 1–34, 2021.
Abstract | Links | BibTeX | Tags: semantics
@article{21:journals:LaneseMM21,
title = {Static versus dynamic reversibility in CCS},
author = {Ivan Lanese and Doriana Medić and Claudio Antares Mezzina},
url = {https://doi.org/10.1007/s00236-019-00346-6},
doi = {10.1007/s00236-019-00346-6},
year = {2021},
date = {2021-01-01},
journal = {Acta Informatica},
volume = {58},
pages = {1–34},
abstract = {The notion of reversible computing is attracting interest because of its applications in diverse fields, in particular the study of programming abstractions for fault tolerant systems. Most computational models are not naturally reversible since computation causes loss of information, and history information must be stored to enable reversibility. In the literature, two approaches to reverse the CCS process calculus exist, differing on how history information is kept. Reversible CCS (RCCS), proposed by Danos and Krivine, exploits dedicated stacks of memories attached to each thread. CCS with Keys (CCSK), proposed by Phillips and Ulidowski, makes CCS operators static so that computation does not cause information loss. In this paper we show that RCCS and CCSK are equivalent in terms of LTS isomorphism.},
keywords = {semantics},
pubstate = {published},
tppubtype = {article}
}
Clément Aubert, Doriana Medić
Explicit Identifiers and Contexts in Reversible Concurrent Calculus Proceedings Article
In: Reversible Computation - 13th International Conference, RC 2021, Virtual Event, July 7-8, 2021, Proceedings, Springer, 2021.
Abstract | Links | BibTeX | Tags: semantics
@inproceedings{21:RC:AubertM21,
title = {Explicit Identifiers and Contexts in Reversible Concurrent Calculus},
author = {Clément Aubert and Doriana Medić},
url = {https://doi.org/10.1007/978-3-030-79837-6_9},
doi = {10.1007/978-3-030-79837-6_9},
year = {2021},
date = {2021-01-01},
booktitle = {Reversible Computation - 13th International Conference, RC 2021, Virtual Event, July 7-8, 2021, Proceedings},
publisher = {Springer},
abstract = {Existing formalisms for the algebraic specification and representation of networks of reversible agents suffer some shortcomings. Despite multiple attempts, reversible declensions of the Calculus of Communicating Systems (CCS) do not offer satisfactory adaptation of notions usual in ?forward-only? process algebras, such as replication or context. Existing formalisms disallow the ?hot-plugging? of processes during their execution in contexts with their own past. They also assume the existence of ?eternally fresh? keys or identifiers that, if implemented poorly, could result in unnecessary bottlenecks and look-ups involving all the threads. In this paper, we begin investigating those issues, by first designing a process algebra endowed with a mechanism to generate identifiers without the need to consult with the other threads. We use this calculus to recast the possible representations of non-determinism in CCS, and as a by-product establish a simple and straightforward definition of concurrency. Our reversible calculus is then proven to satisfy expected properties. We also observe that none of the reversible bisimulations defined thus far are congruences under our notion of ?reversible? contexts.},
keywords = {semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
C. Pino, G. Vecchio, Marco Fronda, Marco Calandri, Marco Aldinucci, Concetto Spampinato
TwinLiverNet: Predicting TACE Treatment Outcome from CT scans for Hepatocellular Carcinoma using Deep Capsule Networks Proceedings Article
In: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021, pp. 3039–3043, IEEE, 2021.
Abstract | Links | BibTeX | Tags: ai
@inproceedings{21:DBLP:conf/embc/PinoVFCAS21,
title = {TwinLiverNet: Predicting TACE Treatment Outcome from CT scans for Hepatocellular Carcinoma using Deep Capsule Networks},
author = {C. Pino and G. Vecchio and Marco Fronda and Marco Calandri and Marco Aldinucci and Concetto Spampinato},
url = {https://doi.org/10.1109/EMBC46164.2021.9630913},
doi = {10.1109/EMBC46164.2021.9630913},
year = {2021},
date = {2021-01-01},
booktitle = {43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021},
pages = {3039–3043},
publisher = {IEEE},
abstract = {Predicting response to treatment plays a key role to assist radiologists in hepato-cellular carcinoma (HCC) therapy planning. The most widely used treatment for unresectable HCC is the trans-arterial chemoembolization (TACE). A complete radiological response after the first TACE is a reliable predictor of treatment favourable outcome. However, visual inspection of contrast-enhanced CT scans is time-consuming, error prone and too operator-dependent. Thus, in this paper we propose TwinLiverNet: a deep neural network that is able to predict TACE treatment outcome through learning visual cue from CT scans. TwinLiverNet, specifically, integrates 3D convolutions and capsule networks and is designed to process simultaneously late arterial and delayed phases from contrast-enhanced CTs. Experimental results carried out on a dataset consisting of 126 HCC lesions show that TwinLiverNet reaches an average accuracy of 82% in predicting complete response to TACE treatment. Furthermore, combining multiple CT phases (specifically, late arterial and delayed ones) yields a performance increase of over 12 percent points. Finally, the introduction of capsule layers into the model avoids the model to overfit, while enhancing accuracy.Clinical relevance— TwinLiverNet supports radiologists in visual inspection of CT scans to assess TACE treatment outcome, while reducing inter-operator variability.},
keywords = {ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Marco Aldinucci, Valentina Cesare, Iacopo Colonnelli, Alberto Riccardo Martinelli, Gianluca Mittone, Barbara Cantalupo
Practical Parallelizazion of a Laplace Solver with MPI Proceedings Article
In: Iannone, Francesco (Ed.): ENEA CRESCO in the fight against COVID-19, pp. 21–24, ENEA, 2021.
Abstract | BibTeX | Tags: hpc4ai
@inproceedings{21:laplace:enea,
title = {Practical Parallelizazion of a Laplace Solver with MPI},
author = {Marco Aldinucci and Valentina Cesare and Iacopo Colonnelli and Alberto Riccardo Martinelli and Gianluca Mittone and Barbara Cantalupo},
editor = {Francesco Iannone},
year = {2021},
date = {2021-01-01},
booktitle = {ENEA CRESCO in the fight against COVID-19},
pages = {21–24},
publisher = {ENEA},
abstract = {This work exposes a practical methodology for the semi-automatic parallelization of existing code. We show how a scientific sequential code can be parallelized through our approach. The obtained parallel code is only slightly different from the starting sequential one, providing an example of how little re-designing our methodology involves. The performance of the parallelized code, executed on the CRESCO6 cluster, is then exposed and discussed. We also believe in the educational value of this approach and suggest its use as a teaching device for students.},
keywords = {hpc4ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Iacopo Colonnelli, Barbara Cantalupo, Concetto Spampinato, Matteo Pennisi, Marco Aldinucci
Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 diagnosis Proceedings Article
In: Iannone, Francesco (Ed.): ENEA CRESCO in the fight against COVID-19, ENEA, 2021.
Abstract | Links | BibTeX | Tags: streamflow
@inproceedings{21:covi:enea,
title = {Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 diagnosis},
author = {Iacopo Colonnelli and Barbara Cantalupo and Concetto Spampinato and Matteo Pennisi and Marco Aldinucci},
editor = {Francesco Iannone},
url = {https://iris.unito.it/retrieve/handle/2318/1796029/779853/21_AI-pipelines_ENEA-COVID19.pdf},
doi = {10.5281/zenodo.5151511},
year = {2021},
date = {2021-01-01},
booktitle = {ENEA CRESCO in the fight against COVID-19},
publisher = {ENEA},
abstract = {HPC is an enabling platform for AI. The introduction of AI workloads in the HPC applications basket has non-trivial consequences both on the way of designing AI applications and on the way of providing HPC computing. This is the leitmotif of the convergence between HPC and AI. The formalized definition of AI pipelines is one of the milestones of HPC-AI convergence. If well conducted, it allows, on the one hand, to obtain portable and scalable applications. On the other hand, it is crucial for the reproducibility of scientific pipelines. In this work, we advocate the StreamFlow Workflow Management System as a crucial ingredient to define a parametric pipeline, called ``CLAIRE COVID-19 Universal Pipeline'', which is able to explore the optimization space of methods to classify COVID-19 lung lesions from CT scans, compare them for accuracy, and therefore set a performance baseline. The universal pipeline automatizes the training of many different Deep Neural Networks (DNNs) and many different hyperparameters. It, therefore, requires a massive computing power, which is found in traditional HPC infrastructure thanks to the portability-by-design of pipelines designed with StreamFlow. Using the universal pipeline, we identified a DNN reaching over 90% accuracy in detecting COVID-19 lesions in CT scans.},
keywords = {streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Ovidio De Filippo, Jeehoon Kang, Francesco Bruno, Jung-Kyu Han, Andrea Saglietto, Han-Mo Yang, Giuseppe Patti, Kyung-Woo Park, Radoslaw Parma, Hyo-Soo Kim, Leonardo De Luca, Hyeon-Cheol Gwon, Mario Iannaccone, Woo Jung Chun, Grzegorz Smolka, Seung-Ho Hur, Enrico Cerrato, Seung Hwan Han, Carlo Mario, Young Bin Song, Javier Escaned, Ki Hong Choi, Gerard Helft, Joon-Hyung Doh, Alessandra Truffa Giachet, Soon-Jun Hong, Saverio Muscoli, Chang-Wook Nam, Guglielmo Gallone, Davide Capodanno, Daniela Trabattoni, Yoichi Imori, Veronica Dusi, Bernardo Cortese, Antonio Montefusco, Federico Conrotto, Iacopo Colonnelli, Imad Sheiban, Gaetano Maria Ferrari, Bon-Kwon Koo, Fabrizio D'Ascenzo
In: The American Journal of Cardiology, 2021, ISSN: 0002-9149.
Abstract | Links | BibTeX | Tags: ai, cardio
@article{21:ajc:bifurcat,
title = {Benefit of Extended Dual Antiplatelet Therapy Duration in Acute Coronary Syndrome Patients Treated with Drug Eluting Stents for Coronary Bifurcation Lesions (from the BIFURCAT Registry)},
author = {Ovidio De Filippo and Jeehoon Kang and Francesco Bruno and Jung-Kyu Han and Andrea Saglietto and Han-Mo Yang and Giuseppe Patti and Kyung-Woo Park and Radoslaw Parma and Hyo-Soo Kim and Leonardo De Luca and Hyeon-Cheol Gwon and Mario Iannaccone and Woo Jung Chun and Grzegorz Smolka and Seung-Ho Hur and Enrico Cerrato and Seung Hwan Han and Carlo Mario and Young Bin Song and Javier Escaned and Ki Hong Choi and Gerard Helft and Joon-Hyung Doh and Alessandra Truffa Giachet and Soon-Jun Hong and Saverio Muscoli and Chang-Wook Nam and Guglielmo Gallone and Davide Capodanno and Daniela Trabattoni and Yoichi Imori and Veronica Dusi and Bernardo Cortese and Antonio Montefusco and Federico Conrotto and Iacopo Colonnelli and Imad Sheiban and Gaetano Maria Ferrari and Bon-Kwon Koo and Fabrizio D'Ascenzo},
url = {https://www.sciencedirect.com/science/article/pii/S0002914921006354},
doi = {10.1016/j.amjcard.2021.07.005},
issn = {0002-9149},
year = {2021},
date = {2021-01-01},
journal = {The American Journal of Cardiology},
abstract = {Optimal dual antiplatelet therapy (DAPT) duration for patients undergoing percutaneous coronary intervention (PCI) for coronary bifurcations is an unmet issue. The BIFURCAT registry was obtained by merging two registries on coronary bifurcations. Three groups were compared in a two-by-two fashion: short-term DAPT (≤ 6 months), intermediate-term DAPT (6-12 months) and extended DAPT (>12 months). Major adverse cardiac events (MACE) (a composite of all-cause death, myocardial infarction (MI), target-lesion revascularization and stent thrombosis) were the primary endpoint. Single components of MACE were the secondary endpoints. Events were appraised according to the clinical presentation: chronic coronary syndrome (CCS) versus acute coronary syndrome (ACS). 5537 patients (3231 ACS, 2306 CCS) were included. After a median follow-up of 2.1 years (IQR 0.9-2.2), extended DAPT was associated with a lower incidence of MACE compared with intermediate-term DAPT (2.8% versus 3.4%, adjusted HR 0.23 [0.1-0.54], p <0.001), driven by a reduction of all-cause death in the ACS cohort. In the CCS cohort, an extended DAPT strategy was not associated with a reduced risk of MACE. In conclusion, among real-world patients receiving PCI for coronary bifurcation, an extended DAPT strategy was associated with a reduction of MACE in ACS but not in CCS patients.},
keywords = {ai, cardio},
pubstate = {published},
tppubtype = {article}
}
Yasir Arfat, Gianluca Mittone, Roberto Esposito, Barbara Cantalupo, Gaetano Maria De Ferrari, Marco Aldinucci
A Review of Machine Learning for Cardiology Journal Article
In: Minerva cardiology and angiology, 2021.
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai
@article{21:ai4numbers:minerva,
title = {A Review of Machine Learning for Cardiology},
author = {Yasir Arfat and Gianluca Mittone and Roberto Esposito and Barbara Cantalupo and Gaetano Maria De Ferrari and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/handle/2318/1796298/780512/21_AI4numbers-preprint.pdf},
doi = {10.23736/s2724-5683.21.05709-4},
year = {2021},
date = {2021-01-01},
journal = {Minerva cardiology and angiology},
abstract = {This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {article}
}
Marco Aldinucci, Valentina Cesare, Iacopo Colonnelli, Alberto Riccardo Martinelli, Gianluca Mittone, Barbara Cantalupo, Carlo Cavazzoni, Maurizio Drocco
Practical Parallelization of Scientific Applications with OpenMP, OpenACC and MPI Journal Article
In: Journal of Parallel and Distributed Computing, vol. 157, pp. 13–29, 2021.
Abstract | Links | BibTeX | Tags: HPC
@article{21:jpdc:loop,
title = {Practical Parallelization of Scientific Applications with OpenMP, OpenACC and MPI},
author = {Marco Aldinucci and Valentina Cesare and Iacopo Colonnelli and Alberto Riccardo Martinelli and Gianluca Mittone and Barbara Cantalupo and Carlo Cavazzoni and Maurizio Drocco},
url = {https://iris.unito.it/retrieve/handle/2318/1792557/770851/Practical_Parallelization_JPDC_preprint.pdf},
doi = {10.1016/j.jpdc.2021.05.017},
year = {2021},
date = {2021-01-01},
journal = {Journal of Parallel and Distributed Computing},
volume = {157},
pages = {13–29},
abstract = {This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a little re-designing effort, turning an old codebase into emphmodern code, i.e., parallel and robust code. We propose a semi-automatic methodology to parallelize scientific applications designed with a purely sequential programming mindset, possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate that the same methodology works for the parallelization in the shared memory model (via OpenMP), message passing model (via MPI), and General Purpose Computing on GPU model (via OpenACC). The method is demonstrated parallelizing four real-world sequential codes in the domain of physics and material science. The methodology itself has been distilled in collaboration with MSc students of the Parallel Computing course at the University of Torino, that applied it for the first time to the project works that they presented for the final exam of the course. Every year the course hosts some special lectures from industry representatives, who present how they use parallel computing and offer codes to be parallelizeda.},
keywords = {HPC},
pubstate = {published},
tppubtype = {article}
}
Daniele D'Agostino, Ivan Merelli, Marco Aldinucci, Daniele Cesini
Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming Journal Article
In: Scientific Programming, vol. 2021, pp. 5514284, 2021, ISBN: 1058-9244.
Abstract | Links | BibTeX | Tags: HPC
@article{21:dagostino:lowpower,
title = {Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming},
author = {Daniele D'Agostino and Ivan Merelli and Marco Aldinucci and Daniele Cesini},
url = {https://downloads.hindawi.com/journals/sp/2021/5514284.pdf},
doi = {10.1155/2021/5514284},
isbn = {1058-9244},
year = {2021},
date = {2021-01-01},
journal = {Scientific Programming},
volume = {2021},
pages = {5514284},
publisher = {Hindawi},
abstract = {Energy consumption is one of the major issues in today’s computer science, and an increasing number of scientific communities are interested in evaluating the tradeoff between time-to-solution and energy-to-solution. Despite, in the last two decades, computing which revolved around centralized computing infrastructures, such as supercomputing and data centers, the wide adoption of the Internet of Things (IoT) paradigm is currently inverting this trend due to the huge amount of data it generates, pushing computing power back to places where the data are generated—the so-called fog/edge computing. This shift towards a decentralized model requires an equivalent change in the software engineering paradigms, development environments, hardware tools, languages, and computation models for scientific programming because the local computational capabilities are typically limited and require a careful evaluation of power consumption. This paper aims to present how these concepts can be actually implemented in scientific software by presenting the state of the art of powerful, less power-hungry processors from one side and energy-aware tools and techniques from the other one.},
keywords = {HPC},
pubstate = {published},
tppubtype = {article}
}
Daniele D'Agostino, Pietro Liò, Marco Aldinucci, Ivan Merelli
Advantages of using graph databases to explore chromatin conformation capture experiments Journal Article
In: BMC Bioinformatics, vol. 22, no. 2, pp. 43–58, 2021, ISBN: 1471-2105.
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai
@article{21:neohic:bmc,
title = {Advantages of using graph databases to explore chromatin conformation capture experiments},
author = {Daniele D'Agostino and Pietro Liò and Marco Aldinucci and Ivan Merelli},
url = {https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-020-03937-0.pdf},
doi = {10.1186/s12859-020-03937-0},
isbn = {1471-2105},
year = {2021},
date = {2021-01-01},
journal = {BMC Bioinformatics},
volume = {22},
number = {2},
pages = {43–58},
abstract = {High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells.},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {article}
}
Marco Aldinucci
L'infrastruttura necessaria per creare interoperabilità tra pubbliche amministrazioni Book Section
In: Perin, Roberto Cavallo (Ed.): L'amministrazione pubblica con i big data: da Torino un dibattito sull'intelligenza artificiale, pp. 225–232, 2021, ISBN: 9788875901806.
Abstract | Links | BibTeX | Tags:
@incollection{21:bigdata:ius,
title = {L'infrastruttura necessaria per creare interoperabilità tra pubbliche amministrazioni},
author = {Marco Aldinucci},
editor = {Roberto Cavallo Perin},
url = {https://iris.unito.it/retrieve/handle/2318/1784335/748058/15.Aldinucci.pdf},
isbn = {9788875901806},
year = {2021},
date = {2021-01-01},
booktitle = {L'amministrazione pubblica con i big data: da Torino un dibattito sull'intelligenza artificiale},
pages = {225–232},
chapter = {15},
abstract = {L'articolo affronta il tema dell'interoperabilità dal punto di vista informatico, ponendo l'accento sulle infrastrutture necessarie affinché la comunicazione tra sistemi informatici pubblici sia possibile. La struttura a silos su cui si basa il sistema informativo della pubblica amministrazione italiana risulta inadeguato all'approccio della big data analysis che, a contrario, richiede la piena comunicabilità tra sistemi informativi affinché il reperimento dei dati su cui condurre sperimentazioni sia quanto più facile e mirato.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schinina, Simone Palazzo, Federica Proietto Salanitri, Giovanni Bellitto, Francesco Rundo, Marco Aldinucci, Massimo Cristofaro, others
An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans Journal Article
In: Artificial Intelligence in Medicine, pp. 102114, 2021.
Abstract | Links | BibTeX | Tags: ai
@article{pennisi2021explainable,
title = {An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans},
author = {Matteo Pennisi and Isaak Kavasidis and Concetto Spampinato and Vincenzo Schinina and Simone Palazzo and Federica Proietto Salanitri and Giovanni Bellitto and Francesco Rundo and Marco Aldinucci and Massimo Cristofaro and others},
url = {https://iris.unito.it/retrieve/handle/2318/1792619/770952/2021_COVID_AIM_preprint.pdf},
doi = {10.1016/j.artmed.2021.102114},
year = {2021},
date = {2021-01-01},
journal = {Artificial Intelligence in Medicine},
pages = {102114},
publisher = {Elsevier},
abstract = {COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes. Next, we combine the segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the model's classification results with those obtained by three expert radiologists on a dataset of 166 CT scans. Results showed a sensitivity of 90.3% and a specificity of 93.5% for COVID-19 detection, at least on par with those yielded by the expert radiologists, and an average lesion categorization accuracy of about 84%. Moreover, a significant role is played by prior lung and lobe segmentation, that allowed us to enhance classification performance by over 6 percent points. The interpretation of the trained AI models reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai. The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decisions and that proactively involves them in the decision loop to further improve the COVID-19 understanding.},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Zilong Zhao, Robert Birke, Rui Han, Bogdan Robu, Sara Bouchenak, Sonia Ben Mokhtar, Lydia Y. Chen
Enhancing Robustness of On-Line Learning Models on Highly Noisy Data Journal Article
In: IEEE Trans. Dependable Secur. Comput., vol. 18, no. 5, pp. 2177–2192, 2021.
Abstract | Links | BibTeX | Tags: ai
@article{ZhaoBHRBMC21,
title = {Enhancing Robustness of On-Line Learning Models on Highly Noisy Data},
author = {Zilong Zhao and Robert Birke and Rui Han and Bogdan Robu and Sara Bouchenak and Sonia Ben Mokhtar and Lydia Y. Chen},
url = {https://doi.org/10.1109/TDSC.2021.3063947},
doi = {10.1109/TDSC.2021.3063947},
year = {2021},
date = {2021-01-01},
journal = {IEEE Trans. Dependable Secur. Comput.},
volume = {18},
number = {5},
pages = {2177–2192},
abstract = {Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this article, we extend a two-layer on-line data selection framework: Robust Anomaly Detector (RAD) with a newly designed ensemble prediction where both layers contribute to the final anomaly detection decision. To adapt to the on-line nature of anomaly detection, we consider additional features of conflicting opinions of classifiers, repetitive cleaning, and oracle knowledge. We on-line learn from incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. We specifically focus on three use cases, (i) detecting 10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big data jobs, and (iii) recognising 100 celebrities faces. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98.95 percent for IoT device attacks (i.e., +7%), up to 85.03 percent for cloud task failures (i.e., +14%) under 40 percent label noise, and for its extension, it can reach up to 77.51 percent for face recognition (i.e., +39%) under 30 percent label noise. The proposed RAD and its extensions are general and can be applied to different anomaly detection algorithms.},
keywords = {ai},
pubstate = {published},
tppubtype = {article}
}
Robert Birke, Juan F. Pérez, Zhan Qiu, Mathias Björkqvist, Lydia Y. Chen
sPARE: Partial Replication for Multi-Tier Applications in the Cloud Journal Article
In: IEEE Trans. Serv. Comput., vol. 14, no. 2, pp. 574–588, 2021.
Abstract | Links | BibTeX | Tags: parallel
@article{BirkePQBC21,
title = {sPARE: Partial Replication for Multi-Tier Applications in the Cloud},
author = {Robert Birke and Juan F. Pérez and Zhan Qiu and Mathias Björkqvist and Lydia Y. Chen},
url = {https://doi.org/10.1109/TSC.2017.2780845},
doi = {10.1109/TSC.2017.2780845},
year = {2021},
date = {2021-01-01},
journal = {IEEE Trans. Serv. Comput.},
volume = {14},
number = {2},
pages = {574–588},
abstract = {Offering consistent low latency remains a key challenge for distributed applications, especially when deployed on the cloud where virtual machines (VMs) suffer from capacity variability caused by co-located tenants. Replicating redundant requests was shown to be an effective mechanism to defend application performance from high capacity variability. While the prior art centers on single-tier systems, it still remains an open question how to design replication strategies for distributed multi-tier systems. In this paper, we design a first of its kind PArtial REplication system, sPARE, that replicates and dispatches read-only workloads for distributed multi-tier web applications. The two key components of sPARE are (i) the variability-aware replicator that coordinates the replication levels on all tiers via an iterative searching algorithm, and (ii) the replication-aware arbiter that uses a novel token-based arbitration algorithm (TAD) to dispatch requests in each tier. We evaluate sPARE on web serving and searching applications, i.e., MediaWiki and Solr, the former deployed on our private cloud and the latter on Amazon EC2. Our results based on various interference patterns and traffic loads show that sPARE is able to improve the tail latency of MediaWiki and Solr by a factor of almost 2.7x and 2.9x, respectively.},
keywords = {parallel},
pubstate = {published},
tppubtype = {article}
}
Iacopo Colonnelli, Barbara Cantalupo, Roberto Esposito, Matteo Pennisi, Concetto Spampinato, Marco Aldinucci
HPC Application Cloudification: The StreamFlow Toolkit Proceedings Article
In: Bispo, João, Cherubin, Stefano, Flich, José (Ed.): 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021), pp. 5:1–5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 2021, ISSN: 2190-6807.
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, streamflow
@inproceedings{colonnelli_et_al:OASIcs.PARMA-DITAM.2021.5,
title = {HPC Application Cloudification: The StreamFlow Toolkit},
author = {Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Matteo Pennisi and Concetto Spampinato and Marco Aldinucci},
editor = {João Bispo and Stefano Cherubin and José Flich},
url = {https://drops.dagstuhl.de/opus/volltexte/2021/13641/pdf/OASIcs-PARMA-DITAM-2021-5.pdf},
doi = {10.4230/OASIcs.PARMA-DITAM.2021.5},
issn = {2190-6807},
year = {2021},
date = {2021-01-01},
booktitle = {12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021)},
volume = {88},
pages = {5:1–5:13},
publisher = {Schloss Dagstuhl – Leibniz-Zentrum für Informatik},
address = {Dagstuhl, Germany},
series = {Open Access Series in Informatics (OASIcs)},
abstract = {Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud infrastructure can be offloaded to HPC environments to speed them up. We introduce StreamFlow, a novel Workflow Management System that supports such a design pattern and makes it possible to run the steps of a standard workflow model on independent processing elements with no shared storage. We validated the proposed approach's effectiveness on the CLAIRE COVID-19 universal pipeline, i.e. a reproducible workflow capable of automating the comparison of (possibly all) state-of-the-art pipelines for the diagnosis of COVID-19 interstitial pneumonia from CT scans images based on Deep Neural Networks (DNNs).},
keywords = {deephealth, hpc4ai, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Fabrizio D'Ascenzo, Ovidio De Filippo, Guglielmo Gallone, Gianluca Mittone, Marco Agostino Deriu, Mario Iannaccone, Albert Ariza-Solé, Christoph Liebetrau, Sergio Manzano-Fernández, Giorgio Quadri, Tim Kinnaird, Gianluca Campo, Jose Paulo Simao Henriques, James M Hughes, Alberto Dominguez-Rodriguez, Marco Aldinucci, Umberto Morbiducci, Giuseppe Patti, Sergio Raposeiras-Roubin, Emad Abu-Assi, Gaetano Maria De Ferrari, Francesco Piroli, Andrea Saglietto, Federico Conrotto, Pierluigi Omedé, Antonio Montefusco, Mauro Pennone, Francesco Bruno, Pier Paolo Bocchino, Giacomo Boccuzzi, Enrico Cerrato, Ferdinando Varbella, Michela Sperti, Stephen B. Wilton, Lazar Velicki, Ioanna Xanthopoulou, Angel Cequier, Andres Iniguez-Romo, Isabel Munoz Pousa, Maria Cespon Fernandez, Berenice Caneiro Queija, Rafael Cobas-Paz, Angel Lopez-Cuenca, Alberto Garay, Pedro Flores Blanco, Andrea Rognoni, Giuseppe Biondi Zoccai, Simone Biscaglia, Ivan Nunez-Gil, Toshiharu Fujii, Alessandro Durante, Xiantao Song, Tetsuma Kawaji, Dimitrios Alexopoulos, Zenon Huczek, Jose Ramon Gonzalez Juanatey, Shao-Ping Nie, Masa-aki Kawashiri, Iacopo Colonnelli, Barbara Cantalupo, Roberto Esposito, Sergio Leonardi, Walter Grosso Marra, Alaide Chieffo, Umberto Michelucci, Dario Piga, Marta Malavolta, Sebastiano Gili, Marco Mennuni, Claudio Montalto, Luigi Oltrona Visconti, Yasir Arfat
Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets Journal Article
In: The Lancet, vol. 397, no. 10270, pp. 199–207, 2021, ISSN: 0140-6736.
Abstract | Links | BibTeX | Tags: ai, cardio, deephealth, hpc4ai
@article{21:lancet,
title = {Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets},
author = {Fabrizio D'Ascenzo and Ovidio De Filippo and Guglielmo Gallone and Gianluca Mittone and Marco Agostino Deriu and Mario Iannaccone and Albert Ariza-Solé and Christoph Liebetrau and Sergio Manzano-Fernández and Giorgio Quadri and Tim Kinnaird and Gianluca Campo and Jose Paulo Simao Henriques and James M Hughes and Alberto Dominguez-Rodriguez and Marco Aldinucci and Umberto Morbiducci and Giuseppe Patti and Sergio Raposeiras-Roubin and Emad Abu-Assi and Gaetano Maria De Ferrari and Francesco Piroli and Andrea Saglietto and Federico Conrotto and Pierluigi Omedé and Antonio Montefusco and Mauro Pennone and Francesco Bruno and Pier Paolo Bocchino and Giacomo Boccuzzi and Enrico Cerrato and Ferdinando Varbella and Michela Sperti and Stephen B. Wilton and Lazar Velicki and Ioanna Xanthopoulou and Angel Cequier and Andres Iniguez-Romo and Isabel Munoz Pousa and Maria Cespon Fernandez and Berenice Caneiro Queija and Rafael Cobas-Paz and Angel Lopez-Cuenca and Alberto Garay and Pedro Flores Blanco and Andrea Rognoni and Giuseppe Biondi Zoccai and Simone Biscaglia and Ivan Nunez-Gil and Toshiharu Fujii and Alessandro Durante and Xiantao Song and Tetsuma Kawaji and Dimitrios Alexopoulos and Zenon Huczek and Jose Ramon Gonzalez Juanatey and Shao-Ping Nie and Masa-aki Kawashiri and Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Sergio Leonardi and Walter Grosso Marra and Alaide Chieffo and Umberto Michelucci and Dario Piga and Marta Malavolta and Sebastiano Gili and Marco Mennuni and Claudio Montalto and Luigi Oltrona Visconti and Yasir Arfat},
url = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},
doi = {10.1016/S0140-6736(20)32519-8},
issn = {0140-6736},
year = {2021},
date = {2021-01-01},
journal = {The Lancet},
volume = {397},
number = {10270},
pages = {199–207},
abstract = {Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding. Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making.},
keywords = {ai, cardio, deephealth, hpc4ai},
pubstate = {published},
tppubtype = {article}
}
Iacopo Colonnelli, Barbara Cantalupo, Ivan Merelli, Marco Aldinucci
StreamFlow: cross-breeding cloud with HPC Journal Article
In: IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 4, pp. 1723–1737, 2021.
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, streamflow
@article{20Lstreamflow:tetc,
title = {StreamFlow: cross-breeding cloud with HPC},
author = {Iacopo Colonnelli and Barbara Cantalupo and Ivan Merelli and Marco Aldinucci},
url = {https://arxiv.org/pdf/2002.01558},
doi = {10.1109/TETC.2020.3019202},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Emerging Topics in Computing},
volume = {9},
number = {4},
pages = {1723–1737},
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single cell transcriptomic data analysis workflow.},
keywords = {deephealth, hpc4ai, streamflow},
pubstate = {published},
tppubtype = {article}
}
2020
Adriano Marques Garcia, Matheus Serpa, Dalvan Griebler, Claudio Schepke, Luiz Gustavo Fernandes, Philippe O. A. Navaux
The Impact of CPU Frequency Scaling on Power Consumption of Computing Infrastructures Proceedings Article
In: International Conference on Computational Science and its Applications (ICCSA), pp. 142-157, Springer, Cagliari, Italy, 2020.
Abstract | Links | BibTeX | Tags: parallel
@inproceedings{GARCIA:ICCSA:20,
title = {The Impact of CPU Frequency Scaling on Power Consumption of Computing Infrastructures},
author = {Adriano Marques Garcia and Matheus Serpa and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes and Philippe O. A. Navaux},
url = {https://iris.unito.it/retrieve/3b8f3dc0-cd4d-4f36-801d-9e8c613ea2e8/ICCSA_Energy_governors_preprint.pdf},
doi = {10.1007/978-3-030-58817-5_12},
year = {2020},
date = {2020-07-01},
booktitle = {International Conference on Computational Science and its Applications (ICCSA)},
volume = {12254},
pages = {142-157},
publisher = {Springer},
address = {Cagliari, Italy},
series = {ICCSA'20},
abstract = {Since the demand for computing power increases, new architectures emerged to obtain better performance. Reducing the power and energy consumption of these architectures is one of the main challenges to achieving high-performance computing. Current research trends aim at developing new software and hardware techniques to achieve the best performance and energy trade-offs. In this work, we investigate the impact of different CPU frequency scaling techniques such as ondemand, performance, and powersave on the power and energy consumption of multi-core based computer infrastructure. We apply these techniques in PAMPAR, a parallel benchmark suite implemented in PThreads, OpenMP, MPI-1, and MPI-2 (spawn). We measure the energy and execution time of 10 benchmarks, varying the number of threads. Our results show that although powersave consumes up to 43.1% less power than performance and ondemand governors, it consumes the triple of energy due to the high execution time. Our experiments also show that the performance governor consumes up to 9.8% more energy than ondemand for CPU-bound benchmarks. Finally, our results show that PThreads has the lowest power consumption, consuming less than the sequential version for memory-bound benchmarks. Regarding performance, the performance governor achieved 3% of performance over the ondemand.},
keywords = {parallel},
pubstate = {published},
tppubtype = {inproceedings}
}
Doriana Medić, Claudio Antares Mezzina, Iain Phillips, Nobuko Yoshida
A parametric framework for reversible emph(pi)-calculi Journal Article
In: Information and Computation, vol. 275, pp. 104644, 2020.
Abstract | Links | BibTeX | Tags: semantics
@article{20:journals:MedicMPY20,
title = {A parametric framework for reversible emph(pi)-calculi},
author = {Doriana Medić and Claudio Antares Mezzina and Iain Phillips and Nobuko Yoshida},
url = {https://doi.org/10.1016/j.ic.2020.104644},
doi = {10.1016/j.ic.2020.104644},
year = {2020},
date = {2020-01-01},
journal = {Information and Computation},
volume = {275},
pages = {104644},
abstract = {This paper presents a study of causality in a reversible, concurrent setting. There exist various notions of causality in Pi-calculus, which differ in the treatment of parallel extrusions of the same name. Hence, by using a parametric way of bookkeeping the order and the dependencies among extruders it is possible to map different causal semantics into the same framework. Starting from this simple observation, we present a uniform framework for reversible ?-calculi that is parametric with respect to a data structure that stores information about the extrusion of a name. Different data structures yield different approaches to the parallel extrusion problem. We map three well-known causal semantics into our framework. We prove causal-consistency for the three instances of our framework. Furthermore, we prove a causal correspondence between the appropriate instances of the framework and the Boreale-Sangiorgi semantics and an operational correspondence with the reversible emph(pi)-calculus causal semantics.},
keywords = {semantics},
pubstate = {published},
tppubtype = {article}
}
Ivan Lanese, Doriana Medić
A General Approach to Derive Uncontrolled Reversible Semantics Proceedings Article
In: 31st International Conference on Concurrency Theory, CONCUR 2020, September 1-4, 2020, Vienna, Austria (Virtual Conference), pp. 33:1–33:24, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.
Abstract | Links | BibTeX | Tags: semantics
@inproceedings{20:concur:LaneseM20,
title = {A General Approach to Derive Uncontrolled Reversible Semantics},
author = {Ivan Lanese and Doriana Medić},
url = {https://doi.org/10.4230/LIPIcs.CONCUR.2020.33},
doi = {10.4230/LIPIcs.CONCUR.2020.33},
year = {2020},
date = {2020-01-01},
booktitle = {31st International Conference on Concurrency Theory, CONCUR 2020, September 1-4, 2020, Vienna, Austria (Virtual Conference)},
volume = {171},
pages = {33:1–33:24},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
series = {LIPIcs},
abstract = {Reversible computing is a paradigm where programs can execute backward as well as in the usual forward direction. Reversible computing is attracting interest due to its applications in areas as different as biochemical modelling, simulation, robotics and debugging, among others. In concurrent systems the main notion of reversible computing is called causal-consistent reversibility, and it allows one to undo an action if and only if its consequences, if any, have already been undone. This paper presents a general and automatic technique to define a causal-consistent reversible extension for given forward models. We support models defined using a reduction semantics in a specific format and consider a causality relation based on resources consumed and produced. The considered format is general enough to fit many formalisms studied in the literature on causal-consistent reversibility, notably Higher-Order ?-calculus and Core Erlang, an intermediate language in the Erlang compilation. Reversible extensions of these models in the literature are ad hoc, while we build them using the same general technique. This also allows us to show in a uniform way that a number of relevant properties, causal-consistency in particular, hold in the reversible extensions we build. Our technique also allows us to go beyond the reversible models in the literature: we cover a larger fragment of Core Erlang, including remote error handling based on links, which has never been considered in the reversibility literature.},
keywords = {semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
Doriana Medić, Claudio Antares Mezzina, Iain Phillips, Nobuko Yoshida
Towards a Formal Account for Software Transactional Memory Proceedings Article
In: Reversible Computation - 12th International Conference, RC 2020, Oslo, Norway, July 9-10, 2020, Proceedings, pp. 255–263, Springer, 2020.
Abstract | Links | BibTeX | Tags: semantics
@inproceedings{20:RC:MedicM0Y20,
title = {Towards a Formal Account for Software Transactional Memory},
author = {Doriana Medić and Claudio Antares Mezzina and Iain Phillips and Nobuko Yoshida},
url = {https://doi.org/10.1007/978-3-030-52482-1_16},
doi = {10.1007/978-3-030-52482-1_16},
year = {2020},
date = {2020-01-01},
booktitle = {Reversible Computation - 12th International Conference, RC 2020, Oslo, Norway, July 9-10, 2020, Proceedings},
volume = {12227},
pages = {255–263},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
abstract = {Software transactional memory (STM) is a concurrency control mechanism for shared memory systems. It is opposite to the lock based mechanism, as it allows multiple processes to access the same set of variables in a concurrent way. Then according to the used policy, the effect of accessing to shared variables can be committed (hence, made permanent) or undone. In this paper, we define a formal framework for describing STMs and show how with a minor variation of the rules it is possible to model two common policies for STM: reader preference and writer preference.},
keywords = {semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
Vasco Amaral, Beatriz Norberto, Miguel Goulão, Marco Aldinucci, Siegfried Benkner, Andrea Bracciali, Paulo Carreira, Edgars Celms, Luís Correia, Clemens Grelck, Helen Karatza, Christoph Kessler, Peter Kilpatrick, Hugo Martiniano, Ilias Mavridis, Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Visa
Programming languages for data-Intensive HPC applications: A systematic mapping study Journal Article
In: Parallel Computing, pp. 102584, 2020, ISSN: 0167-8191.
Abstract | Links | BibTeX | Tags: HPC
@article{20:sms:chipset,
title = {Programming languages for data-Intensive HPC applications: A systematic mapping study},
author = {Vasco Amaral and Beatriz Norberto and Miguel Goulão and Marco Aldinucci and Siegfried Benkner and Andrea Bracciali and Paulo Carreira and Edgars Celms and Luís Correia and Clemens Grelck and Helen Karatza and Christoph Kessler and Peter Kilpatrick and Hugo Martiniano and Ilias Mavridis and Sabri Pllana and Ana Respício and José Simão and Luís Veiga and Ari Visa},
url = {https://iris.unito.it/retrieve/689605/1-s2.0-S0167819119301759-main.pdf},
doi = {https://doi.org/10.1016/j.parco.2019.102584},
issn = {0167-8191},
year = {2020},
date = {2020-01-01},
journal = {Parallel Computing},
pages = {102584},
abstract = {A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and projects to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.},
keywords = {HPC},
pubstate = {published},
tppubtype = {article}
}
Daniele D'Agostino, Pietro Liò, Marco Aldinucci, Ivan Merelli
NeoHiC: A web application for the analysis of Hi-C data Proceedings Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 98–107, 2020, ISBN: 978-3-030-63061-4.
Abstract | Links | BibTeX | Tags:
@inproceedings{20:neohic:cibb,
title = {NeoHiC: A web application for the analysis of Hi-C data},
author = {Daniele D'Agostino and Pietro Liò and Marco Aldinucci and Ivan Merelli},
url = {https://iris.unito.it/retrieve/handle/2318/1766001/690791/20_neohic_cibb.pdf},
doi = {10.1007/978-3-030-63061-4_10},
isbn = {978-3-030-63061-4},
year = {2020},
date = {2020-01-01},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {12313},
pages = {98–107},
abstract = {High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of chromatin interactions and 3D chromosome folding on a larger scale. A graph-based multi-level representation of Hi-C data is essential for proper visualisation of the spatial pattern they represent, in particular for comparing different experiments or for re-mapping omics-data in a space-aware context. The size of the HiC data hampers the straightforward use of currently available graph visualisation tools and libraries. In this paper, we present the first version of NeoHiC, a user-friendly web application for the progressive graph visualisation of Hi-C data based on the use of the Neo4j graph database. The user could select the richness of the environment of the query gene by choosing among a large number of proximity and distance metrics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Paul Metzger, Murray Cole, Christian Fensch, Marco Aldinucci, Enrico Bini
Enforcing Deadlines for Skeleton-based Parallel Programming Proceedings Article
In: 26th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Sydney, Australia, 2020.
Abstract | Links | BibTeX | Tags:
@inproceedings{20:farm:rtas,
title = {Enforcing Deadlines for Skeleton-based Parallel Programming},
author = {Paul Metzger and Murray Cole and Christian Fensch and Marco Aldinucci and Enrico Bini},
url = {https://iris.unito.it/retrieve/handle/2318/1741320/616056/20_ske_RTAS.pdf},
doi = {10.1109/RTAS48715.2020.000-7},
year = {2020},
date = {2020-01-01},
booktitle = {26th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)},
address = {Sydney, Australia},
abstract = {High throughput applications with real-time guar- antees are increasingly relevant. For these applications, parallelism must be exposed to meet deadlines. Directed Acyclic Graphs (DAGs) are a popular and very general application model that can capture any possible interaction among threads. However, we argue that by constraining the application structure to a set of composable ``skeletons'', at the price of losing some generality w.r.t. DAGs, the following advantages are gained: (i) a finer model of the application enables tighter analysis, (ii) specialised scheduling policies are applicable, (iii) programming is simplified, (iv) specialised implementation techniques can be exploited transparently, and (v) the program can be automatically tuned to minimise resource usage while still meeting its hard deadlines. As a first step towards a set of real-time skeletons we conduct a case study with the job farm skeleton and the hard real- time XMOS xCore-200 microcontroller. We present an analytical framework for job farms that reduces the number of required cores by scheduling jobs in batches, while ensuring that deadlines are still met. Our experimental results demonstrate that batching reduces the minimum sustainable period by up to 22%, leading to a reduced number of required cores. The framework chooses the best parameters in 83% of cases and never selects parameters that cause deadline misses. Finally, we show that the overheads introduced by the skeleton abstraction layer are negligible.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Vincent Reniers, Yuan Gao, Ren Zhang, Paolo Viviani, Akash Madhusudan, Bert Lagaisse, Svetla Nikova, Dimitri Van Landuyt, Riccardo Lombardi, Bart Preneel, Wouter Joosen
Authenticated and Auditable Data Sharing via Smart Contract Proceedings Article
In: Proceedings of the 35th ACM/SIGAPP Symposium on Applied Computing, pp. 1–8, ACM, Brno, Czech Republic, 2020, ISBN: xxx-x-xxxx-xxxx-x.
@inproceedings{20:sac:blockchain,
title = {Authenticated and Auditable Data Sharing via Smart Contract},
author = {Vincent Reniers and Yuan Gao and Ren Zhang and Paolo Viviani and Akash Madhusudan and Bert Lagaisse and Svetla Nikova and Dimitri Van Landuyt and Riccardo Lombardi and Bart Preneel and Wouter Joosen},
isbn = {xxx-x-xxxx-xxxx-x},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 35th ACM/SIGAPP Symposium on Applied Computing},
pages = {1–8},
publisher = {ACM},
address = {Brno, Czech Republic},
series = {SAC '20},
abstract = {Our main use case features multiple companies that iteratively optimize on the architectural properties of aircraft components in a decentralized manner. In each optimization step of the so-called multi-disciplinary optimization (MDO) process, sensitive data is exchanged, and we require auditability and traceability of actions taken to assure compliance with signed legal agreements. In this paper, we present a distributed protocol that coordinates authenticated and auditable exchanges of files, leveraging a smart contract. The entire life cycle of a file exchange, including file registration, access request and key distribution, is recorded and traceable via the smart contract. Moreover, when one party raises a dispute, the smart contract can identify the dishonest party without compromising the file's confidentiality. The proposed protocol provides a simple, novel, yet efficient approach to exchange files with support for data access auditability between companies involved in a private consortium with no incentive to share files outside of the protocol. We implemented the protocol in Solidity, deployed it on a private Ethereum blockchain, and validated it within the use case of a decentralized workflow.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Valentina Cesare, Iacopo Colonnelli, Marco Aldinucci
Practical Parallelization of Scientific Applications Proceedings Article
In: Proc. of 28th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), pp. 376–384, IEEE, Västerås, Sweden, 2020.
Abstract | Links | BibTeX | Tags: c3s, hpc4ai
@inproceedings{20:looppar:pdp,
title = {Practical Parallelization of Scientific Applications},
author = {Valentina Cesare and Iacopo Colonnelli and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/handle/2318/1735377/601141/2020_looppar_PDP.pdf},
doi = {10.1109/PDP50117.2020.00064},
year = {2020},
date = {2020-01-01},
booktitle = {Proc. of 28th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
pages = {376–384},
publisher = {IEEE},
address = {Västerås, Sweden},
abstract = {This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a limited re-designing effort, turning an old codebase into modern code, i.e., parallel and robust code. We propose an automatable methodology to parallelize scientific applications designed with a purely sequential programming mindset, thus possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate the methodology by way of an astrophysical application, where we model at the same time the kinematic profiles of 30 disk galaxies with a Monte Carlo Markov Chain (MCMC), which is sequential by definition. The parallel code exhibits a 12 times speedup on a 48-core platform.},
keywords = {c3s, hpc4ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Jose Daniel Garcia, Jose Daniel Rio, Marco Aldinucci, Fabio Tordini, Marco Danelutto, Gabriele Mencagli, Massimo Torquati
Challenging the abstraction penalty in parallel patterns libraries: Adding FastFlow support to GrPPI Journal Article
In: The Journal of Supercomputing, vol. 76, no. 7, pp. 5139–5159, 2020.
Abstract | Links | BibTeX | Tags: fastflow, rephrase
@article{19:jsupe:grppi,
title = {Challenging the abstraction penalty in parallel patterns libraries: Adding FastFlow support to GrPPI},
author = {Jose Daniel Garcia and Jose Daniel Rio and Marco Aldinucci and Fabio Tordini and Marco Danelutto and Gabriele Mencagli and Massimo Torquati},
url = {https://iris.unito.it/retrieve/handle/2318/1762686/744894/2020-js-grppi-postprint.pdf},
doi = {10.1007/s11227-019-02826-5},
year = {2020},
date = {2020-01-01},
journal = {The Journal of Supercomputing},
volume = {76},
number = {7},
pages = {5139–5159},
abstract = {In the last years, pattern-based programming has been recognized as a good practice for efficiently exploiting parallel hardware resources. Following this approach, multiple libraries have been designed for providing such high-level abstractions to ease the parallel programming. However, those libraries do not share a common interface. To pave the way, GrPPI has been designed for providing an intermediate abstraction layer between application developers and existing parallel programming frameworks like OpenMP, Intel TBB or ISO C++ threads. On the other hand, FastFlow has been adopted as an efficient object-based programming framework that may benefit from being supported as an additional GrPPI backend. However, the object-based approach presents some major challenges to be incorporated under the GrPPI type safe functional programming style. In this paper, we present the integration of FastFlow as a new GrPPI backend to demonstrate that structured parallel programming frameworks perfectly fit the GrPPI design. Additionally, we also demonstrate that GrPPI does not incur in additional overheads for providing its abstraction layer, and we study the programmability in terms of lines of code and cyclomatic complexity. In general, the presented work acts as reciprocal validation of both FastFlow (as an efficient, native structured parallel programming framework) and GrPPI (as an efficient abstraction layer on top of existing parallel programming frameworks).},
keywords = {fastflow, rephrase},
pubstate = {published},
tppubtype = {article}
}
2019
Adriano Marques Garcia, Claudio Schepke, Alessandro Gonçalves Girardi
PAMPAR: A new parallel benchmark for performance and energy consumption evaluation Journal Article
In: Concurrency and Computation: Practice and Experience, vol. 32, no. 20, pp. 1-21, 2019.
Abstract | Links | BibTeX | Tags: parallel
@article{GARCIA:CCPE:19,
title = {PAMPAR: A new parallel benchmark for performance and energy consumption evaluation},
author = {Adriano Marques Garcia and Claudio Schepke and Alessandro Gonçalves Girardi},
url = {https://iris.unito.it/retrieve/d514c682-a567-4a02-93b7-9e27b6d3da03/Concurrency___Computation__Practice___Experience__Final_Version_.pdf},
doi = {10.1002/cpe.5504},
year = {2019},
date = {2019-10-01},
journal = {Concurrency and Computation: Practice and Experience},
volume = {32},
number = {20},
pages = {1-21},
abstract = {This paper presents PAMPAR, a new benchmark to evaluate the performance and energy consumption of different Parallel Programming Interfaces (PPIs). The benchmark is composed of 11 algorithms implemented in PThreads, OpenMP, MPI-1, and MPI-2 (spawn) PPIs. Previous studies have used some of these pseudo-applications to perform this type of evaluation in different architectures since there is no benchmark that offers this variety of PPIs and communication models. In this work, we measure the energy and performance of each pseudo-application in a single architecture, varying the number of threads/processes. We also organize the pseudo-applications according to their memory accesses, floating-point operations, and branches. The goal is to show that this set of pseudo-applications has enough features to build a parallel benchmark. The results show that there is no single best case that provides both better performance and low energy consumption in the presented scenarios. Moreover, the pseudo-applications usage of the system resources are different enough to represent different scenarios and be efficient as a benchmark.},
keywords = {parallel},
pubstate = {published},
tppubtype = {article}
}
Paolo Viviani
Deep Learning at Scale with Nearest Neighbours Communications PhD Thesis
Computer Science Department, University of Torino, 2019.
Abstract | Links | BibTeX | Tags:
@phdthesis{19:dl:viviani:thesis,
title = {Deep Learning at Scale with Nearest Neighbours Communications},
author = {Paolo Viviani},
url = {https://zenodo.org/record/3516093/files/20190910_final_pdf.pdf},
doi = {10.5281/zenodo.3516093},
year = {2019},
date = {2019-09-01},
school = {Computer Science Department, University of Torino},
abstract = {As deep learning techniques become more and more popular, there is the need to move these applications from the data scientist's Jupyter notebook to efficient and reliable enterprise solutions. Moreover, distributed training of deep learning models will happen more and more outside the well-known borders of cloud and HPC infrastructure and will move to edge and mobile platforms. Current techniques for distributed deep learning have drawbacks in both these scenarios, limiting their long-term applicability. After a critical review of the established techniques for Data Parallel training from both a distributed computing and deep learning perspective, a novel approach based on nearest-neighbour communications is presented in order to overcome some of the issues related to mainstream approaches, such as global communication patterns. Moreover, in order to validate the proposed strategy, the Flexible Asynchronous Scalable Training (FAST) framework is introduced, which allows to apply the nearest-neighbours communications approach to a deep learning framework of choice. Finally, a relevant use-case is deployed on a medium-scale infrastructure to demonstrate both the framework and the methodology presented. Training convergence and scalability results are presented and discussed in comparison to a baseline defined by using state-of-the-art distributed training tools provided by a well-known deep learning framework.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Marco Aldinucci, Stefano Bagnasco, Matteo Concas, Stefano Lusso, Sergio Rabellino, Danilo Demarchi, Sara Vallero
Managing a heterogeneous scientific computing cluster with cloud-like tools: ideas and experience Proceedings Article
In: European Physical Journal Web of Conferences, pp. 07030, 2019.
Abstract | Links | BibTeX | Tags:
@inproceedings{2019EPJWC.21407030A,
title = {Managing a heterogeneous scientific computing cluster with cloud-like tools: ideas and experience},
author = {Marco Aldinucci and Stefano Bagnasco and Matteo Concas and Stefano Lusso and Sergio Rabellino and Danilo Demarchi and Sara Vallero},
url = {https://iris.unito.it/retrieve/533279/epjconf_chep2018_07030.pdf},
doi = {10.1051/epjconf/201921407030},
year = {2019},
date = {2019-07-01},
booktitle = {European Physical Journal Web of Conferences},
volume = {214},
pages = {07030},
series = {European Physical Journal Web of Conferences},
abstract = {Obtaining CPU cycles on an HPC cluster is nowadays relatively simple and sometimes even cheap for academic institutions. However, in most of the cases providers of HPC services would not allow changes on the configuration, implementation of special features or a lower-level control on the computing infrastructure, for example for testing experimental configurations. The variety of use cases proposed by several departments of the University of Torino, including ones from solid-state chemistry, computational biology, genomics and many others, called for different and sometimes conflicting configurations; furthermore, several R&D activities in the field of scientific computing, with topics ranging from GPU acceleration to Cloud Computing technologies, needed a platform to be carried out on. The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multi-purpose flexible HPC cluster designed and operated by a collaboration between the University of Torino and the Torino branch of the Istituto Nazionale di Fisica Nucleare. It is aimed at providing a flexible and reconfigurable infrastructure to cater to a wide range of different scientific computing needs, as well as a platform for R&D activities on computational technologies themselves. We describe some of the use cases that prompted the design and construction of the system, its architecture and a first characterisation of its performance by some synthetic benchmark tools and a few realistic use-case tests.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Adriano Marques Garcia, Claudio Schepke, Alessandro Gonçalves Girardi, Sherlon Almeida Silva
A New Parallel Benchmark for Performance Evaluation and Energy Consumption Proceedings Article
In: High Performance Computing for Computational Science – VECPAR 2018, pp. 188-201, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-15996-2.
Abstract | Links | BibTeX | Tags: parallel
@inproceedings{GARCIA:VECPAR:19,
title = {A New Parallel Benchmark for Performance Evaluation and Energy Consumption},
author = {Adriano Marques Garcia and Claudio Schepke and Alessandro Gonçalves Girardi and Sherlon Almeida Silva},
url = {https://iris.unito.it/retrieve/1272dea3-b1ea-4356-af0d-d180cef341b9/VECPAR_2018_paper_preprint.pdf},
doi = {10.1007/978-3-030-15996-2_14},
isbn = {978-3-030-15996-2},
year = {2019},
date = {2019-03-01},
booktitle = {High Performance Computing for Computational Science – VECPAR 2018},
pages = {188-201},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {This paper presents a new benchmark to evaluate performance and energy consumption of different Parallel Programming Interfaces (PPIs). The benchmark is composed of 11 algorithms implemented in PThreads, OpenMP, MPI-1 and MPI-2 (spawn) PPIs. Previous studies have used some of these applications to perform this type of evaluation in different architectures, since there is no benchmark that offers this variety of PPIs and communication models. In this work we measure the energy and performance of each application in a single architecture, varying the number of threads/processes. The goal is to show that this set of applications has enough features to form a parallel benchmark. The results show that there is no single best case that provides both better performance and low energy consumption in the presented scenarios. However, PThreads and OpenMP achieve the best trade-offs between performance and energy in most cases.},
keywords = {parallel},
pubstate = {published},
tppubtype = {inproceedings}
}
Massimo Torquati, Daniele De Sensi, Gabriele Mencagli, Marco Aldinucci, Marco Danelutto
Power-Aware Pipelining with Automatic Concurrency Control Journal Article
In: Concurrency and Computation: Practice and Experience, vol. 31, no. 5, 2019.
Abstract | Links | BibTeX | Tags: rephrase
@article{18:dynqueue:ccpe,
title = {Power-Aware Pipelining with Automatic Concurrency Control},
author = {Massimo Torquati and Daniele De Sensi and Gabriele Mencagli and Marco Aldinucci and Marco Danelutto},
url = {https://iris.unito.it/retrieve/handle/2318/1668445/414282/2018_CCPE.pdf},
doi = {10.1002/cpe.4652},
year = {2019},
date = {2019-01-01},
journal = {Concurrency and Computation: Practice and Experience},
volume = {31},
number = {5},
abstract = {Continuous streaming computations are usually composed of different modules, exchanging data through shared message queues. The selection of the algorithm used to access such queues (i.e. the concurrency control) is a critical aspect both for performance and power consumption. In this paper we describe the design of automatic concurrency control algorithm for implement- ing power-efficient communications on shared-memory multicores. The algorithm automatically switches between nonblocking and blocking concurrency protocols, getting the best from the two worlds, i.e. obtaining the same throughput offered by the nonblocking implementa- tion and the same power efficiency of the blocking concurrency protocol. We demonstrate the effectiveness of our approach using two micro-benchmarks and two real streaming applications},
keywords = {rephrase},
pubstate = {published},
tppubtype = {article}
}
Marco Aldinucci, Maurizio Drocco, Claudia Misale, Guy Tremblay
Languages for Big Data analysis Book Chapter
In: Sakr, Sherif, Zomaya, Albert (Ed.): Encyclopedia of Big Data Technologies, Springer International Publishing, Cham, 2019, ISBN: 978-3-319-63962-8.
Abstract | Links | BibTeX | Tags: parallel
@inbook{bigdata:encyclopedia:18,
title = {Languages for Big Data analysis},
author = {Marco Aldinucci and Maurizio Drocco and Claudia Misale and Guy Tremblay},
editor = {Sherif Sakr and Albert Zomaya},
url = {https://iris.unito.it/retrieve/handle/2318/1668051/413363/2019_bigdataframeworks_enc.pdf},
doi = {10.1007/978-3-319-63962-8_142-1},
isbn = {978-3-319-63962-8},
year = {2019},
date = {2019-01-01},
booktitle = {Encyclopedia of Big Data Technologies},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {In this chapter, some of the most common tools for Big Data analytics are surveyed, inter-alia, Apache Spark, Flink, Storm, and Beam. They are compared against well-defined features concerning programming model (language expressivity and semantics), and execution model (parallel behaviour and run-time support). The implementation of a running example is provided for all of them.},
keywords = {parallel},
pubstate = {published},
tppubtype = {inbook}
}
Marco Danelutto, Tiziano De Matteis, Daniele De Sensi, Gabriele Mencagli, Massimo Torquati, Marco Aldinucci, Peter Kilpatrick
The RePhrase Extended Pattern Set for Data Intensive Parallel Computing Journal Article
In: International Journal of Parallel Programming, vol. 47, no. 1, pp. 74–93, 2019.
Abstract | Links | BibTeX | Tags: fastflow, rephrase
@article{17:rephrasepatterns:ijpp,
title = {The RePhrase Extended Pattern Set for Data Intensive Parallel Computing},
author = {Marco Danelutto and Tiziano De Matteis and Daniele De Sensi and Gabriele Mencagli and Massimo Torquati and Marco Aldinucci and Peter Kilpatrick},
url = {https://iris.unito.it/retrieve/handle/2318/1659336/387667/2017_ijpp_rephrase.pdf},
doi = {10.1007/s10766-017-0540-z},
year = {2019},
date = {2019-01-01},
journal = {International Journal of Parallel Programming},
volume = {47},
number = {1},
pages = {74–93},
abstract = {We discuss the extended parallel pattern set identified within the EU-funded project RePhrase as a candidate pattern set to support data intensive applications targeting heterogeneous architectures. The set has been designed to include three classes of pattern, namely i) core patterns, modelling common, not necessarily data intensive parallelism exploitation patterns, usually to be used in composition; ii) high level patterns, modelling common, complex and complete parallelism exploitation patterns; and iii) building block patterns, modelling the single components of data intensive applications, suitable for use–in composition–to implement patterns not covered by the core and high level patterns. We discuss the expressive power of the RePhrase extended pattern set and results illustrating the performances that may be achieved with the FastFlow implementation of the high level patterns.},
keywords = {fastflow, rephrase},
pubstate = {published},
tppubtype = {article}
}
Massimo Torquati, Gabriele Mencagli, Maurizio Drocco, Marco Aldinucci, Tiziano De Matteis, Marco Danelutto
On Dynamic Memory Allocation in Sliding-Window Parallel Patterns for Streaming Analytics Journal Article
In: The Journal of Supercomputing, vol. 75, no. 8, pp. 4114–4131, 2019.
Abstract | Links | BibTeX | Tags: fastflow, rephrase
@article{17:dmadasp:jsupe,
title = {On Dynamic Memory Allocation in Sliding-Window Parallel Patterns for Streaming Analytics},
author = {Massimo Torquati and Gabriele Mencagli and Maurizio Drocco and Marco Aldinucci and Tiziano De Matteis and Marco Danelutto},
url = {https://iris.unito.it/retrieve/handle/2318/1648626/362381/17_torquati_jsc.pdf},
doi = {10.1007/s11227-017-2152-1},
year = {2019},
date = {2019-01-01},
journal = {The Journal of Supercomputing},
volume = {75},
number = {8},
pages = {4114–4131},
abstract = {This work studies the issues related to dynamic memory management in Data Stream Processing, an emerging paradigm enabling the real-time processing of live data streams. In this paper we consider two streaming parallel patterns and we discuss different implementation variants related on how dynamic memory is managed. The results show that the standard mechanisms provided by modern C++ are not entirely adequate for maximizing the performance. Instead, the combined use of an efficient general-purpose memory allocator, a custom allocator optimized for the pattern considered and a custom variant of the C++ shared pointer mechanism, provides a performance improvement up to 16% on the best case.},
keywords = {fastflow, rephrase},
pubstate = {published},
tppubtype = {article}
}
Doriana Medić
Relative expressiveness of calculi for reversible concurrency Journal Article
In: Bull. EATCS, vol. 129, 2019.
Abstract | Links | BibTeX | Tags: semantics
@article{19:eatcs:Medic19,
title = {Relative expressiveness of calculi for reversible concurrency},
author = {Doriana Medić},
url = {http://bulletin.eatcs.org/index.php/beatcs/article/view/590/601},
year = {2019},
date = {2019-01-01},
journal = {Bull. EATCS},
volume = {129},
abstract = {A number of formalisms have been proposed to model various approaches to reversibility and to better understand its properties and characteristics. However, the relation between these formalisms has hardly been studied. This paper examines the expressiveness of the causal-consistent reversibility in process algebras CCS and emph(pi)-calculus. In particular, we show, by means of encodings, that LTSs of two reversible extensions of CCS, Reversible CCS [1] and CCS with Keys [2], are isomorphic up to some structural transformations of processes. To study different causal semantics for ?-calculus, we devise a uniform framework for reversible emph(pi)-calculi that is parametric with respect to a data structure that stores information about the extrusion of a name. Depending on the used data structure, different causal semantics can be obtained. We show that reversibility induced by our framework when instantiated with three different data structures is causally-consistent and prove a causal correspondence between certain causal semantics and matching instance of the framework.},
keywords = {semantics},
pubstate = {published},
tppubtype = {article}
}
Paolo Viviani, Maurizio Drocco, Daniele Baccega, Iacopo Colonnelli, Marco Aldinucci
Deep Learning at Scale Proceedings Article
In: Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), pp. 124–131, IEEE, Pavia, Italy, 2019.
Abstract | Links | BibTeX | Tags: ai
@inproceedings{19:deeplearn:pdp,
title = {Deep Learning at Scale},
author = {Paolo Viviani and Maurizio Drocco and Daniele Baccega and Iacopo Colonnelli and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/handle/2318/1695211/487778/19_deeplearning_PDP.pdf},
doi = {10.1109/EMPDP.2019.8671552},
year = {2019},
date = {2019-01-01},
booktitle = {Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
pages = {124–131},
publisher = {IEEE},
address = {Pavia, Italy},
abstract = {This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.},
keywords = {ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Ivan Merelli, Federico Fornari, Fabio Tordini, Daniele D'Agostino, Marco Aldinucci, Daniele Cesini
Exploiting Docker containers over Grid computing for a comprehensive study of chromatin conformation in different cell types Journal Article
In: Journal of Parallel and Distributed Computing, vol. 134, pp. 116–127, 2019, ISSN: 0743-7315.
Abstract | Links | BibTeX | Tags: bioinformatics
@article{19:merelli:jpdc,
title = {Exploiting Docker containers over Grid computing for a comprehensive study of chromatin conformation in different cell types},
author = {Ivan Merelli and Federico Fornari and Fabio Tordini and Daniele D'Agostino and Marco Aldinucci and Daniele Cesini},
url = {https://iris.unito.it/retrieve/handle/2318/1711684/532767/2019_Nuchart_JPDC_open.pdf},
doi = {10.1016/j.jpdc.2019.08.002},
issn = {0743-7315},
year = {2019},
date = {2019-01-01},
journal = {Journal of Parallel and Distributed Computing},
volume = {134},
pages = {116–127},
abstract = {Many bioinformatic applications require to exploit the capabilities of several computational resources to effectively access and process large and distributed datasets. In this context, Grid computing has been largely used to face unprecedented challenges in Computational Biology, at the cost of complex workarounds needed to make applications successfully running. The Grid computing paradigm, in fact, has always suffered from a lack of flexibility. Although this has been partially solved by Cloud computing, the on-demand approach is way distant from the original idea of volunteering computing that boosted the Grid paradigm. A solution to outpace the impossibility of creating custom environments for running applications in Grid is represented by the containerization technology. In this paper, we describe our experience in exploiting a Docker-based approach to run in a Grid environment a novel, computationally intensive, bioinformatic application, which models the DNA spatial conformation inside the nucleus of eukaryotic cells. Results assess the feasibility of this approach in terms of performance and efforts to run large experiments.},
keywords = {bioinformatics},
pubstate = {published},
tppubtype = {article}
}
Clemens Grelck, Ewa Niewiadomska-Szynkiewicz, Marco Aldinucci, Andrea Bracciali, Elisabeth Larsson
Why High-Performance Modelling and Simulation for Big Data Applications Matters Book Chapter
In: Kołodziej, Joanna, González-Vélez, Horacio (Ed.): High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet, no. 11400, pp. 1–35, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-16272-6.
Abstract | Links | BibTeX | Tags: HPC
@inbook{Grelck2019,
title = {Why High-Performance Modelling and Simulation for Big Data Applications Matters},
author = {Clemens Grelck and Ewa Niewiadomska-Szynkiewicz and Marco Aldinucci and Andrea Bracciali and Elisabeth Larsson},
editor = {Joanna Kołodziej and Horacio González-Vélez},
url = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-16272-6_1.pdf},
doi = {10.1007/978-3-030-16272-6_1},
isbn = {978-3-030-16272-6},
year = {2019},
date = {2019-01-01},
booktitle = {High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet},
number = {11400},
pages = {1–35},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS},
abstract = {Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications.},
keywords = {HPC},
pubstate = {published},
tppubtype = {inbook}
}
Maurizio Drocco, Paolo Viviani, Iacopo Colonnelli, Marco Aldinucci, Marco Grangetto
Accelerating spectral graph analysis through wavefronts of linear algebra operations Proceedings Article
In: Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), pp. 9–16, IEEE, Pavia, Italy, 2019.
Abstract | Links | BibTeX | Tags: grid
@inproceedings{19:gsp:pdp,
title = {Accelerating spectral graph analysis through wavefronts of linear algebra operations},
author = {Maurizio Drocco and Paolo Viviani and Iacopo Colonnelli and Marco Aldinucci and Marco Grangetto},
url = {https://iris.unito.it/retrieve/handle/2318/1695315/488105/19_wavefront_PDP.pdf},
doi = {10.1109/EMPDP.2019.8671640},
year = {2019},
date = {2019-01-01},
booktitle = {Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
pages = {9–16},
publisher = {IEEE},
address = {Pavia, Italy},
abstract = {The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.},
keywords = {grid},
pubstate = {published},
tppubtype = {inproceedings}
}
Vincent Reniers, Dimitri Van Landuyt, Paolo Viviani, Bert Lagaisse, Riccardo Lombardi, Wouter Joosen
Analysis of Architectural Variants for Auditable Blockchain-based Private Data Sharing Proceedings Article
In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 346–354, ACM, Limassol, Cyprus, 2019, ISBN: 978-1-4503-5933-7.
Abstract | Links | BibTeX | Tags:
@inproceedings{19:sac:blockchain,
title = {Analysis of Architectural Variants for Auditable Blockchain-based Private Data Sharing},
author = {Vincent Reniers and Dimitri Van Landuyt and Paolo Viviani and Bert Lagaisse and Riccardo Lombardi and Wouter Joosen},
url = {https://doi.acm.org/10.1145/3297280.3297316},
doi = {10.1145/3297280.3297316},
isbn = {978-1-4503-5933-7},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing},
pages = {346–354},
publisher = {ACM},
address = {Limassol, Cyprus},
series = {SAC '19},
abstract = {Many applications by design depend on costly trusted third-party auditors. One such example is the industrial application case of federated multi-disciplinary optimization (MDO), in which different organizations contribute to a complex engineering design effort. Although blockchain and distributed ledger technology (DLT) has strong potential in reducing the dependence on such intermediaries, the architectural complexity involved in designing a solution is daunting. In this paper, we analyze the architectural variants for decentralized private data sharing while guaranteeing auditability in terms of data access operations. Non-repudiation of actions taken by each party is a key requirement, as is availability of the shared data. % through storage governed by the chain. The architectural variants analyzed focus on attaining:~(i)~confidential data exchange, (ii)~maintaining and governing access to the shared data, (iii)~providing data access auditability, (iv)~data validation or conflict resolution, and to a lesser degree (v)~transaction and identity privacy. We systematically enumerate architectural decisions at the levels of:~storage, policy-based file access control, data encryption methods, and auditability mechanisms for private data. This analysis is based on extensive assessment of the state of the art on decentralized private data access management using static or dynamic policies, and private data validation without exposing confidential information. The main contribution of this work is a comprehensive overview of architectural variants for decentralized control of private, encrypted data, and the involved trade-offs in terms of performance, auditable trust and security. These findings are validated in the context on the aforementioned industry case that involves federated multi-disciplinary optimization (MDO).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Adriano Marques Garcia, Claudio Schepke, Alessandro Gonçalves Girardi, Sherlon Almeida Silva
Power Consumption of Parallel Programming Interfaces in Multicore Architectures: A Case Study Proceedings Article
In: 2018 Symposium on High Performance Computing Systems (WSCAD), pp. 77-83, 2018.
Abstract | Links | BibTeX | Tags: parallel
@inproceedings{GARCIA:WSCAD:18,
title = {Power Consumption of Parallel Programming Interfaces in Multicore Architectures: A Case Study},
author = {Adriano Marques Garcia and Claudio Schepke and Alessandro Gonçalves Girardi and Sherlon Almeida Silva},
url = {https://iris.unito.it/retrieve/cab823a1-a6f7-483f-929a-607a166e0e78/A_Case_Study___Adriano___IEEE.pdf},
doi = {10.1109/WSCAD.2018.00021},
year = {2018},
date = {2018-10-01},
booktitle = {2018 Symposium on High Performance Computing Systems (WSCAD)},
pages = {77-83},
abstract = {This paper presents a case study on the power consumption of different Parallel Programming Interfaces (PPIs) in multicore architectures. The study is based on the PAMPAR benchmark, which is composed of 11 algorithms implemented in PThreads, OpenMP, MPI-1 and MPI-2 (spawn) PPIs. The results show that there is no single best case that provides both better performance and low power consumption in the presented scenarios. However, PThreads and OpenMP achieve the best trade-offs between performance and power in most cases.},
keywords = {parallel},
pubstate = {published},
tppubtype = {inproceedings}
}