Talks | Parallel Computing

@misc{24:amulone:comete,

title = {Workflows for future High-Performance Computing},

author = {Alberto Mulone},

url = {https://datacloud.di.unito.it/index.php/s/ZGG8fLMp5B7qRHS},

year  = {2024},

date = {2024-04-01},

address = {Torino, Italy},

howpublished = {COMETE PhD Workshop},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:icolonne:cwl4hpccern,

title = {CWL in the HPC Ecosystem},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/PRmqdwWHt6P2PH7},

year  = {2024},

date = {2024-04-01},

address = {CERN, Meyrin, Switzerland},

howpublished = {Workshop on workflow languages for HEP analysis},

keywords = {across, eupex, icsc, space, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:pdp:karmarkar,

title = {Benchmarking Parallelization Models through Karmarkar`s algorithm},

author = {Malenza Giulio and Santimaria Marco Edoardo},

editor = {Horacio González-Vélez Adriana E. Chis},

url = {https://datacloud.di.unito.it/index.php/s/JjKcAJpYS7ctX9r},

doi = {10.1109/PDP62718.2024.00010},

year  = {2024},

date = {2024-03-01},

booktitle = {2024 32nd Euromicro International Conference on Parallel, Distributed and Network-based Processing},

pages = {1–8},

publisher = {IEEE},

address = {Dublin, Irelans},

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 = {misc}

}

@misc{24:icsc:spoke1:ifab,

title = {FLaaS: Federated Learning as a Service},

author = {Robert Birke},

url = {https://datacloud.di.unito.it/index.php/s/yHXdTnC8xEqoJ6Y},

year  = {2024},

date = {2024-02-01},

address = {Torino, Italy},

abstract = {Presentation about the Innovation Grant in collaboration with IFAB},

howpublished = {ICSC - Spoke 1 meeting},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:icsc:spoke1:eni,

title = {Cross-Platform Full Waveform Inversion},

author = {Alberto Mulone},

url = {https://datacloud.di.unito.it/index.php/s/M3HkxA5wsBPS5ro},

year  = {2024},

date = {2024-02-01},

address = {Torino, Italy},

abstract = {Presentation about the Innovation Grant in collaboration with ENI},

howpublished = {ICSC - Spoke 1 meeting},

keywords = {icsc, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:HiPEAC:riscv,

title = {RISC-V for AI},

author = {Gianluca Mittone},

url = {https://datacloud.di.unito.it/index.php/s/rFtxT7zryoKNGbP},

year  = {2024},

date = {2024-01-01},

address = {Garching bei München, München, Germany},

abstract = {AI-focused RISC-V-based hardware accelerators},

howpublished = {High Performance, Edge And Cloud computing Conference 2024 (HiPEAC 2024)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:FL:bicocca,

title = {Federated Learning: A Distributed System Viewpoint},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/FfEzADQtC73GgLs},

year  = {2023},

date = {2023-12-01},

abstract = {Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We'll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DDN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy through generative adversarial networks.},

howpublished = {Bicocca University seminars, Milan, Italy},

note = {Invited talk},

keywords = {eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ucc: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://datacloud.di.unito.it/index.php/s/XfjNZEPSNfSKPFr},

year  = {2023},

date = {2023-12-01},

address = {Taormina, Italy},

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.},

howpublished = {The 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:sc:works,

title = {A Systematic Mapping Study of Italian Research on Workflows},

author = {Marco Aldinucci and Elena 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 Medić and Giuseppa Muscianisi and Francesca Righetti and Eva Sciacca and Nicola Tonellotto and Mauro Tortonesi and Paolo Trunfio and Tullio Vardanega},

url = {https://datacloud.di.unito.it/index.php/s/2kgooG43pGCykji},

year  = {2023},

date = {2023-11-01},

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 developing an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, 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.},

howpublished = {18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023)},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:HaMMonProject,

title = {Università degli Studi di Torino: Alpha parallel research group},

author = {Iacopo Colonnelli and Doriana Medić and Barbara Cantalupo and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/cmgy9BZ3nwCR2QJ},

year  = {2023},

date = {2023-10-01},

address = {Bologna, Italy},

howpublished = {HaMMon Kick-Off meeting},

keywords = {icsc, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:GAIA:bigHPC,

title = {Performance portability in HPC: the Gaia use-case.},

author = {Giulio Malenza and Valentina Cesare and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/RqcZpizFtC9toFq},

year  = {2023},

date = {2023-09-01},

address = {Naples, Italy},

howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:iciap:benchmed,

title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},

author = {Samuele Fonio},

url = {https://datacloud.di.unito.it/index.php/s/sR7YeTGgfH4DtCR},

year  = {2023},

date = {2023-09-01},

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.},

howpublished = {Image Analysis and Processing - ICIAP 2023 - 22th International Conference - FedMed},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:itadata:fl_scaling,

title = {Benchmarking Federated Learning Scalability},

author = {Gianluca Mittone and Samuele Fonio},

url = {https://datacloud.di.unito.it/index.php/s/QZGxC4X3s5LG5oT},

year  = {2023},

date = {2023-09-01},

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.},

howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:europar:mafl,

title = {Model-Agnostic Federated Learning},

author = {Gianluca Mittone and Walter Riviera and Iacopo Colonnelli and Robert Birke and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/9T6G2tRreRomBAE},

year  = {2023},

date = {2023-09-01},

address = {Limassol, Cyprus},

abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). MAFL merges 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 machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5x speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},

howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:europar:phdtalk,

title = {Model-Agnostic Federated Learning},

author = {Gianluca Mittone and Robert Birke and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/pT3qxkwzzsHR3nS},

year  = {2023},

date = {2023-08-01},

address = {Limassol, Cyprus},

abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). 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 processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library.},

howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:OF:genova,

title = {Building an accelerated OpenFOAM Proof-of-Concept application using Modern C++.},

author = {Giulio Malenza},

url = {https://datacloud.di.unito.it/index.php/s/mB6omsDB8ERBkGW},

year  = {2023},

date = {2023-07-01},

address = {Genova, Italy},

howpublished = {18th OpenFOAM Workshop 2023, Genova},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{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://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy},

doi = {10.1109/COMPSAC57700.2023.00288},

year  = {2023},

date = {2023-06-01},

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 = {misc}

}

@misc{23:ACMCF,

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://datacloud.di.unito.it/index.php/s/BYyqZbHzzN4DL8Z},

year  = {2023},

date = {2023-05-01},

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 processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us 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 x86-64 and ARM platforms and an emerging RISC-V one. 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.},

howpublished = {20th ACM international conference on computing frontiers (CF '23)},

note = {Invited talk},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:WWW,

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://datacloud.di.unito.it/index.php/s/js7go3EorZxSLn9},

year  = {2023},

date = {2023-05-01},

abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially 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 is reconcilable with the industry's constraints. It does not require sharing any information that would reveal the entities' data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15% 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.},

howpublished = {2023 ACM international Web Conference (WWW '23)},

note = {Invited talk},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:casella:architecturalfedavg,

title = {Architecture-Based FedAvg for Vertical Federated Learning},

author = {Bruno Casella and Samuele Fonio},

url = {https://datacloud.di.unito.it/index.php/s/kJQxnqG4d2ZSicK},

doi = {10.1109/ICCVW60793.2023.00362},

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, fl, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:wide:medic,

title = {Towards formal model for location aware workflows},

author = {Doriana Medić and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/wpDd9HETzioixTW},

year  = {2023},

date = {2023-01-01},

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},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:cip:romania,

title = {EuroHPC and the Italian HPC ecosystem},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn},

year  = {2022},

date = {2022-06-01},

address = {Bucharest, Romania},

abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},

howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:eocoe:summit,

title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB},

year  = {2022},

date = {2022-06-01},

address = {Napoli, Italy},

abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},

howpublished = {EuroHPC EoCoE final summit},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:garr,

title = {Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc},

year  = {2022},

date = {2022-05-01},

address = {Palermo, Italy},

abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},

howpublished = {Condivisioni, Conferenza GARR 2022},

note = {Keynote talk},

keywords = {across, admire, eumaster4hpc, eupex, hpc4ai, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}