Papers | Parallel Computing

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{Antelmi_ISWCWrks_24,

title = {High-Performance Computation on a Rust-based distributed ABM engine},

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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}