Software & tools | Parallel Computing

RISC-V tools

We actively contribute to the RISC-V ecosystem via different software ports. As of today, these include:

  • Fastflow. Fastflow is a C++ framework for high-level pattern-based parallel programming performance (more info here). The RISC-V port is part of the official repository.
  • Pytorch. PyTorch is one of the most popular Python/C++ frameworks for training and using DNN models. The RISC-V port is available here.
  • OpenFL for RISC-V. We managed to port the official Intel® OpenFL federated learning framework to the RISC-V platform. The Python packages are available to be installed via pip from this repository.
    To properly add our proprietary repository to your pip configuration, just run
    pip config set global.index-url https://gitlab.di.unito.it/api/v4/projects/1057/packages/pypi/simple
    Then, to install OpenFL built for RISC-V, just run
    pip install openfl-riscv
    As side results, on this repository are also available the following RISC-V compatible Python packages: ninja (ninja-riscv), meson-python (meson-python-riscv), scipy (scipy-riscv), scikit-learn (scikit-learn-riscv).
Publications

2023

Gianluca Mittone, Walter Riviera, Iacopo Colonnelli, Robert Birke, Marco Aldinucci

Model-Agnostic Federated Learning Proceedings Article

In: Euro-Par 2023: Parallel Processing, pp. 383–396, Springer, Limassol, Cyprus, 2023, (https://arxiv.org/abs/2303.04906).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning, riscv

Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medić, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning Proceedings Article

In: 20th ACM International Conference on Computing Frontiers (CF ’23), ACM, Bologna, Italy, 2023, ISBN: 979-8-4007-0140-5/23/05, (https://arxiv.org/abs/2302.07946).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning, parallel, riscv

FastFederatedLearning

Fast Federated Learning (FFL) is a C/C++-based Federated Learning framework built on top of the parallel programming FastFlow framework. It exploits the Cereal library to efficiently serialise the updates sent over the network and the libtorch library to fully bypass the need for Python code. The first release of this software comprises three examples based on three different communication topologies: master-worker, peer-to-peer, and tree-based.
FastFederatedLearning is freely available on GitHub under the LGPLv3 license. It has been successfully tested on x86_64, ARM, and RISC-V platforms. FFL has scripts for automatically installing the framework and reproducing all the experiments reported in the original paper. More information about software usage can be found on the official repository.

G. Mittone, N. Tonci, R. Birke, I. Colonnelli, D. Medić, A. Bartolini, R. Esposito, E. Parisi, F. Beneventi, M. Polato, M. Torquati, L. Benini, and M. Aldinucci, “Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning“, 20th ACM International Conference on Computing Frontiers, 2023. DOI: 10.1145/3587135.3592211

Publications

2024

Bruno Casella, Walter Riviera, Marco Aldinucci, Gloria Menegaz

Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis Journal Article

In: STAR Protocols, 2024, (https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf).

Abstract | Links | BibTeX | Tags: epi, federated, icsc

2023

Gianluca Mittone, Giulio Malenza, Marco Aldinucci, Robert Birke

Distributed Edge Inference: an Experimental Study on Multiview Detection Proceedings Article

In: UCC ’23: Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing Companion, Taormina, Italy, 2023, (eupilot, icsc, In press).

Abstract | Links | BibTeX | Tags: federated, learning

Gianluca Mittone, Samuele Fonio

Benchmarking Federated Learning Scalability Proceedings Article

In: Proceedings of the 2nd Italian Conference on Big Data and Data Science, ITADATA 2023, September 11-13, 2023, CEUR, Naples, Italy, 2023, (In press).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, In press, learning, parallel

Gianluca Mittone, Walter Riviera, Iacopo Colonnelli, Robert Birke, Marco Aldinucci

Model-Agnostic Federated Learning Proceedings Article

In: Euro-Par 2023: Parallel Processing, pp. 383–396, Springer, Limassol, Cyprus, 2023, (https://arxiv.org/abs/2303.04906).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning, riscv

Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medić, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning Proceedings Article

In: 20th ACM International Conference on Computing Frontiers (CF ’23), ACM, Bologna, Italy, 2023, ISBN: 979-8-4007-0140-5/23/05, (https://arxiv.org/abs/2302.07946).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning, parallel, riscv

Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lio

A Federated Learning Benchmark for Drug-Target Interaction Proceedings Article

In: Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), ACM, Austin, Texas, 2023, ISBN: 978-1-4503-9419-2/23/04, (https://arxiv.org/abs/2302.07684).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning

Bruno Casella, Samuele Fonio

Architecture-Based FedAvg for Vertical Federated Learning Proceedings Article

In: Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy, 4 December 2023, 2023, (https://iris.unito.it/bitstream/2318/1949730/1/HALF_HVL_for_DML_ICC23___Taormina-2.pdf).

Abstract | Links | BibTeX | Tags: ai, epi, federated, icsc

Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato

Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning Proceedings Article

In: Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France, 2 October 2023, 2023, (https://ieeexplore.ieee.org/document/10350429).

Abstract | Links | BibTeX | Tags: ai, federated

Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato

FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis Journal Article

In: Computer Vision and Image Understanding, 2023, (https://www.sciencedirect.com/science/article/pii/S107731422300262X?via%3Dihub).

Abstract | Links | BibTeX | Tags: ai, federated

Bruno Casella, Walter Riviera, Marco Aldinucci, Gloria Menegaz

MERGE: A model for multi-input biomedical federated learning Journal Article

In: Patterns, pp. 100856, 2023, ISSN: 2666-3899.

Abstract | Links | BibTeX | Tags: ai, epi, federated, icsc

Yasir Arfat, Gianluca Mittone, Iacopo Colonnelli, Fabrizio D’Ascenzo, Roberto Esposito, Marco Aldinucci

Pooling critical datasets with Federated Learning Proceedings Article

In: 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023, pp. 329–337, IEEE, Napoli, Italy, 2023.

Abstract | Links | BibTeX | Tags: admire, c3s, federated, hpc4ai, learning

OpenFL-extended

OpenFL-extended is an extended version of the official Intel® OpenFL federated learning (FL) framework. OpenFL-extended fully supports the standard FL workflow already provided by OpenFL, but in addition, it provides support for both federated bagging and federated boosting approaches. Federated bagging is implemented through simple bagging of models trained by different parties from the aggregator, while federated boosting is obtained employing the AdaBoost.F algorithm developed at the University of Turin[1]. Through these approaches, OpenFL extended is fully model-agnostic, which means that it can be used to build federations out of any Machine Learning model, not only Deep Neural Networks.
OpenFL extended is freely available on GitHub under the LGPLv3 license. It is fully Python-based and comes with a wide range of ready-made examples. It has been tested on x86_64, ARM and RISC-V architectures. More information about software usage can be found on the official repository.

This software’s publication is currently under review, but an open-access version of the paper is available on arXiv.

G. Mittone, W. Riviera, I. Colonnelli, R. Birke, M. Aldinucci, “Model-Agnostic Federated Learning“, arXive, 2023. DOI: 10.48550/arXiv.2303.04906
[1] M. Polato, R. Esposito, and M. Aldinucci. “Boosting the federation: Cross-silo federated learning without gradient descent.” 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022.

Publications

2024

Bruno Casella, Walter Riviera, Marco Aldinucci, Gloria Menegaz

Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis Journal Article

In: STAR Protocols, 2024, (https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf).

Abstract | Links | BibTeX | Tags: epi, federated, icsc

2023

Gianluca Mittone, Giulio Malenza, Marco Aldinucci, Robert Birke

Distributed Edge Inference: an Experimental Study on Multiview Detection Proceedings Article

In: UCC ’23: Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing Companion, Taormina, Italy, 2023, (eupilot, icsc, In press).

Abstract | Links | BibTeX | Tags: federated, learning

Gianluca Mittone, Samuele Fonio

Benchmarking Federated Learning Scalability Proceedings Article

In: Proceedings of the 2nd Italian Conference on Big Data and Data Science, ITADATA 2023, September 11-13, 2023, CEUR, Naples, Italy, 2023, (In press).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, In press, learning, parallel

Gianluca Mittone, Walter Riviera, Iacopo Colonnelli, Robert Birke, Marco Aldinucci

Model-Agnostic Federated Learning Proceedings Article

In: Euro-Par 2023: Parallel Processing, pp. 383–396, Springer, Limassol, Cyprus, 2023, (https://arxiv.org/abs/2303.04906).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning, riscv

Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medić, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning Proceedings Article

In: 20th ACM International Conference on Computing Frontiers (CF ’23), ACM, Bologna, Italy, 2023, ISBN: 979-8-4007-0140-5/23/05, (https://arxiv.org/abs/2302.07946).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning, parallel, riscv

Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lio

A Federated Learning Benchmark for Drug-Target Interaction Proceedings Article

In: Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), ACM, Austin, Texas, 2023, ISBN: 978-1-4503-9419-2/23/04, (https://arxiv.org/abs/2302.07684).

Abstract | Links | BibTeX | Tags: eupilot, federated, icsc, learning

Bruno Casella, Samuele Fonio

Architecture-Based FedAvg for Vertical Federated Learning Proceedings Article

In: Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy, 4 December 2023, 2023, (https://iris.unito.it/bitstream/2318/1949730/1/HALF_HVL_for_DML_ICC23___Taormina-2.pdf).

Abstract | Links | BibTeX | Tags: ai, epi, federated, icsc

Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato

Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning Proceedings Article

In: Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France, 2 October 2023, 2023, (https://ieeexplore.ieee.org/document/10350429).

Abstract | Links | BibTeX | Tags: ai, federated

Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato

FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis Journal Article

In: Computer Vision and Image Understanding, 2023, (https://www.sciencedirect.com/science/article/pii/S107731422300262X?via%3Dihub).

Abstract | Links | BibTeX | Tags: ai, federated

Bruno Casella, Walter Riviera, Marco Aldinucci, Gloria Menegaz

MERGE: A model for multi-input biomedical federated learning Journal Article

In: Patterns, pp. 100856, 2023, ISSN: 2666-3899.

Abstract | Links | BibTeX | Tags: ai, epi, federated, icsc

Yasir Arfat, Gianluca Mittone, Iacopo Colonnelli, Fabrizio D’Ascenzo, Roberto Esposito, Marco Aldinucci

Pooling critical datasets with Federated Learning Proceedings Article

In: 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023, pp. 329–337, IEEE, Napoli, Italy, 2023.

Abstract | Links | BibTeX | Tags: admire, c3s, federated, hpc4ai, learning

Jupyter Workflow

Jupyter Workflow

Jupyter Workflow is an extension of the IPython kernel designed to support distributed literate workflows. The Jupyter Workflow kernel enables Jupyter Notebooks to describe complex workflows and to execute them in a distributed fashion on hybrid cloud/HPC infrastructures. In particular, code cells are regarded as the nodes of a distributed workflow graph, whereas cell metadata are used to express data and control dependencies, parallel execution patterns (e.g. Scatter/Gather), and target execution infrastructures.

Jupyter Workflow code is available on GitHub under the LGPLv3 license, and the related Python package is downloadable from PyPI. More details about the tool and its applications can be found on the Jupyter Workkflow website.

I. Colonnelli, M. Aldinucci, B. Cantalupo, L. Padovani, S. Rabellino, C. Spampinato, R. Morelli, R. Di Carlo, N. Magini and C. Cavazzoni, “Distributed workflows with Jupyter”, Future Generation Computer Systems, vol. 128, pp. 282-298, 2022. doi: 10.1016/j.future.2021.10.007.

Publications

2023

Marco Aldinucci, Elena Maria Baralis, Valeria Cardellini, Iacopo Colonnelli, Marco Danelutto, Sergio Decherchi, Giuseppe Di Modica, Luca Ferrucci, Marco Gribaudo, Francesco Iannone, Marco Lapegna, Doriana Medic, Giuseppa Muscianisi, Francesca Righetti, Eva Sciacca, Nicola Tonellotto, Mauro Tortonesi, Paolo Trunfio, Tullio Vardanega

A Systematic Mapping Study of Italian Research on Workflows Proceedings Article

In: Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, SC-W 2023, pp. 2065–2076, ACM, Denver, CO, USA, 2023.

Abstract | Links | BibTeX | Tags: icsc, jupyter-workflow, streamflow

Iacopo Colonnelli

Workflow models for heterogeneous distributed systems Miscellaneous

2nd Italian Conference on Big Data and Data Science (ITADATA 2023), 2023, (Best PhD Thesis Award).

Links | BibTeX | Tags: jupyter-workflow, streamflow

Iacopo Colonnelli

UNITO tools presentation Miscellaneous

CN HPC Flagship 3 Working Day, 2023.

Links | BibTeX | Tags: jupyter-workflow, streamflow

Iacopo Colonnelli

Workflow Models for Heterogeneous Distributed Systems Proceedings Article

In: Bena, Nicola, Martino, Beniamino Di, Maratea, Antonio, Sperduti, Alessandro, Nardo, Emanuel Di, Ciaramella, Angelo, Montella, Raffaele, Ardagna, Claudio A. (Ed.): Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023, CEUR-WS.org, 2023.

Abstract | Links | BibTeX | Tags: across, eupex, icsc, jupyter-workflow, streamflow

2022

Iacopo Colonnelli, Marco Aldinucci

Hybrid Workflows For Large-Scale Scientific Applications Miscellaneous

6th EAGE High Performance Computing Workshop, 2022.

Abstract | Links | BibTeX | Tags: across, eupex, jupyter-workflow, textarossa

Iacopo Colonnelli, Dario Tranchitella

Dossier: multi-tenant distributed Jupyter Notebooks Miscellaneous

DoK Talks 141, 2022, (Invited talk).

Abstract | Links | BibTeX | Tags: across, deephealth, hpc4ai, jupyter-workflow

Iacopo Colonnelli

Distributed workflows with Jupyter Miscellaneous

J on The Beach 2022, 2022, (Workshop).

Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow

Iacopo Colonnelli, Dario Tranchitella

OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes Miscellaneous

J on The Beach 2022, 2022.

Links | BibTeX | Tags: across, deephealth, hpc4ai, jupyter-workflow, streamflow

Iacopo Colonnelli, Marco Aldinucci, Barbara Cantalupo, Luca Padovani, Sergio Rabellino, Concetto Spampinato, Roberto Morelli, Rosario Di Carlo, Nicolò Magini, Carlo Cavazzoni

Distributed workflows with Jupyter Journal Article

In: Future Generation Computer Systems, vol. 128, pp. 282–298, 2022, ISSN: 0167-739X.

Abstract | Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow

2020

Iacopo Colonnelli, Sergio Rabellino

JupyterFlow: Jupyter Notebooks su larga scala Miscellaneous

Workshop GARR 2020, 2020.

Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, jupyter-workflow

StreamFlow

The StreamFlow framework is a container-native Workflow Management System (WMS) written in Python 3 and based on the Common Workflow Language (CWL) Standard.

StreamFlow has been designed around two main principles:

  • Allowing the execution of tasks in multi-container environments in order to support the concurrent execution of multiple communicating tasks in a multi-agent ecosystem
  • Relaxing the requirement of a single shared data space to allow for hybrid workflow executions on top of multi-cloud or hybrid cloud/HPC infrastructures.

StreamFlow source code is available on GitHub under the LGPLv3 license. Moreover, a Python package is downloadable from PyPI and Docker containers can be found on Docker Hub. More details about the tool and its applications can be found on the StreamFlow website.
StreamFlow has been selected as an exploring technology by the EC Innovation Radar initiative.

I. Colonnelli, B. Cantalupo, I. Merelli and M. Aldinucci, “StreamFlow: cross-breeding cloud with HPC,” in IEEE Transactions on Emerging Topics in Computing, doi: 10.1109/TETC.2020.3019202.

Publications

2024

Alberto Mulone

Cross-Platform Full Waveform Inversion Miscellaneous

ICSC – Spoke 1 meeting, 2024.

Abstract | Links | BibTeX | Tags: icsc, streamflow

2023

Alberto Scionti, Iacopo Colonnelli

Orchestrating Multi-Domain Workflows: The ACROSS Approach Miscellaneous

Workflows Community: Modern Workflows for Continuum and Cross-Facility Computing, 2023.

Links | BibTeX | Tags: across, streamflow

Marco Aldinucci, Elena Maria Baralis, Valeria Cardellini, Iacopo Colonnelli, Marco Danelutto, Sergio Decherchi, Giuseppe Di Modica, Luca Ferrucci, Marco Gribaudo, Francesco Iannone, Marco Lapegna, Doriana Medic, Giuseppa Muscianisi, Francesca Righetti, Eva Sciacca, Nicola Tonellotto, Mauro Tortonesi, Paolo Trunfio, Tullio Vardanega

A Systematic Mapping Study of Italian Research on Workflows Proceedings Article

In: Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, SC-W 2023, pp. 2065–2076, ACM, Denver, CO, USA, 2023.

Abstract | Links | BibTeX | Tags: icsc, jupyter-workflow, streamflow

Iacopo Colonnelli

ACROSS: HPC Big Data Artificial Intelligence Cross Stack Platform Towards Exascale Miscellaneous

LN HPC-KTT Assemblea Nazionale 2023, 2023.

Links | BibTeX | Tags: across, streamflow

Iacopo Colonnelli, Doriana Medić, Barbara Cantalupo, Marco Aldinucci

Università degli Studi di Torino: Alpha parallel research group Miscellaneous

HaMMon Kick-Off meeting, 2023.

Links | BibTeX | Tags: icsc, streamflow

Iacopo Colonnelli

Workflow models for heterogeneous distributed systems Miscellaneous

2nd Italian Conference on Big Data and Data Science (ITADATA 2023), 2023, (Best PhD Thesis Award).

Links | BibTeX | Tags: jupyter-workflow, streamflow

Alberto Mulone, Sherine Awad, Davide Chiarugi, Marco Aldinucci

Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment Miscellaneous

47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, 2023.

Abstract | Links | BibTeX | Tags: across, icsc, streamflow

Iacopo Colonnelli

Workflows and the Common Workflow Language (CWL) Miscellaneous

OSA2Micro: An Open Science Approach to Microbiology data integration, 2023, (Invited talk).

Links | BibTeX | Tags: streamflow

Iacopo Colonnelli

UNITO tools presentation Miscellaneous

CN HPC Flagship 3 Working Day, 2023.

Links | BibTeX | Tags: jupyter-workflow, streamflow

Sofia Karvounari, Eleni Mathioulaki, Michael R. Crusoe, Iacopo Colonnelli

Standardised Workflows at EBRAINS Miscellaneous

Human Brain Project Summit 2023, 2023, (Invited talk).

Abstract | Links | BibTeX | Tags: across, eupex, invited, space, streamflow

Iacopo Colonnelli

CWL for HPC: are we there yet? Miscellaneous

2023 CWL Conference, 2023, (Invited talk).

Abstract | Links | BibTeX | Tags: across, eupex, invited, streamflow

Iacopo Colonnelli

Workflow Models for Heterogeneous Distributed Systems Proceedings Article

In: Bena, Nicola, Martino, Beniamino Di, Maratea, Antonio, Sperduti, Alessandro, Nardo, Emanuel Di, Ciaramella, Angelo, Montella, Raffaele, Ardagna, Claudio A. (Ed.): Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023, CEUR-WS.org, 2023.

Abstract | Links | BibTeX | Tags: across, eupex, icsc, jupyter-workflow, streamflow

Sandro Gepiro Contaldo, Luca Alessandri, Iacopo Colonnelli, Marco Beccuti, Marco Aldinucci

Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow Book Chapter

In: Calogero, Raffaele Adolfo, Benes, Vladimir (Ed.): Single Cell Transcriptomics: Methods and Protocols, pp. 337–345, Springer US, New York, NY, 2023, ISBN: 978-1-0716-2756-3.

Abstract | Links | BibTeX | Tags: streamflow

2022

Iacopo Colonnelli

StreamFlow Miscellaneous

2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses, 2022, (Invited talk).

Links | BibTeX | Tags: across, deephealth, eupex, invited, streamflow, textarossa

Iacopo Colonnelli

StreamFlow: a topology-aware WMS Miscellaneous

ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls, 2022, (Invited talk).

Links | BibTeX | Tags: across, deephealth, eupex, invited, streamflow, textarossa

Iacopo Colonnelli

StreamFlow: A framework for hybrid workflows Miscellaneous

EUPEX WP5 bi-weekly meeting, 2022.

Links | BibTeX | Tags: eupex, streamflow

Iacopo Colonnelli

Distributed workflows with Jupyter Miscellaneous

J on The Beach 2022, 2022, (Workshop).

Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow

Iacopo Colonnelli, Dario Tranchitella

OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes Miscellaneous

J on The Beach 2022, 2022.

Links | BibTeX | Tags: across, deephealth, hpc4ai, jupyter-workflow, streamflow

Iacopo Colonnelli

StreamFlow: A framework for hybrid workflows Miscellaneous

ACROSS WP4 meeting, 2022.

Links | BibTeX | Tags: across, streamflow

Marco Aldinucci, David Atienza, Federico Bolelli, Mónica Caballero, Iacopo Colonnelli, José Flich, Jon Ander Gómez, David González, Costantino Grana, Marco Grangetto, Simone Leo, Pedro López, Dana Oniga, Roberto Paredes, Luca Pireddu, Eduardo Quiñones, Tatiana Silva, Enzo Tartaglione, Marina Zapater

The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures Book Section

In: Curry, Edward, Auer, Sören, Berre, Arne J., Metzger, Andreas, Perez, Maria S., Zillner, Sonja (Ed.): Technologies and Applications for Big Data Value, pp. 183–202, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-78307-5.

Abstract | Links | BibTeX | Tags: deephealth, streamflow

Eduardo Quiñones, Jesus Perales, Jorge Ejarque, Asaf Badouh, Santiago Marco, Fabrice Auzanneau, François Galea, David González, José Ramón Hervás, Tatiana Silva, Iacopo Colonnelli, Barbara Cantalupo, Marco Aldinucci, Enzo Tartaglione, Rafael Tornero, José Flich, Jose Maria Martinez, David Rodriguez, Izan Catalán, Jorge Garcia, Carles Hernández

The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud Computing Infrastructures for IA-based Medical Solutions Book Section

In: Terzo, Olivier, Martinovič, Jan (Ed.): HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, pp. 191–216, CRC Press, Boca Raton, Florida, 2022, ISBN: 978-1-0320-0984-1.

Abstract | Links | BibTeX | Tags: deephealth, streamflow

Martin Golasowski, Jan Martinovič, Marc Levrier, Stephan Hachinger, Sophia Karagiorgou, Aikaterini Papapostolou, Spiros Mouzakitis, Ioannis Tsapelas, Monica Caballero, Marco Aldinucci, Jon Ander Gómez, Antony Chazapis, Jean-Thomas Acquaviva

Toward the Convergence of High-Performance Computing, Cloud, and Big Data Domains Book Section

In: Terzo, Olivier, Martinovič, Jan (Ed.): HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, pp. 1–16, CRC Press, Boca Raton, Florida, 2022, ISBN: 978-1-0320-0984-1.

Abstract | Links | BibTeX | Tags: deephealth, streamflow

Dana Oniga, Barbara Cantalupo, Enzo Tartaglione, Daniele Perlo, Marco Grangetto, Marco Aldinucci, Federico Bolelli, Federico Pollastri, Michele Cancilla, Laura Canalini, Costantino Grana, Cristina Muñoz Alcalde, Franco Alberto Cardillo, Monica Florea

Applications of AI and HPC in the Health Domain Book Section

In: Terzo, Olivier, Martinovič, Jan (Ed.): HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, pp. 217–239, CRC Press, Boca Raton, Florida, 2022, ISBN: 978-1-0320-0984-1.

Abstract | Links | BibTeX | Tags: deephealth, streamflow

Iacopo Colonnelli, Marco Aldinucci, Barbara Cantalupo, Luca Padovani, Sergio Rabellino, Concetto Spampinato, Roberto Morelli, Rosario Di Carlo, Nicolò Magini, Carlo Cavazzoni

Distributed workflows with Jupyter Journal Article

In: Future Generation Computer Systems, vol. 128, pp. 282–298, 2022, ISSN: 0167-739X.

Abstract | Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow

2021

Iacopo Colonnelli

StreamFlow: A framework for hybrid workflows Miscellaneous

ACROSS WP4 meeting, 2021.

Links | BibTeX | Tags: across, streamflow

Marco Aldinucci

The modernization of HPC applications for the cloud era Miscellaneous

Fifth EAGE Workshop on High Performance Computing for Upstream, 2021, (Keynote talk).

Abstract | BibTeX | Tags: across, admire, deephealth, keynote, streamflow

Giovanni Agosta, William Fornaciari, Andrea Galimberti, Giuseppe Massari, Federico Reghenzani, Federico Terraneo, Davide Zoni, Carlo Brandolese, Massimo Celino, Francesco Iannone, Paolo Palazzari, Giuseppe Zummo, Massimo Bernaschi, Pasqua D’Ambra, Sergio Saponara, Marco Danelutto, Massimo Torquati, Marco Aldinucci, Yasir Arfat, Barbara Cantalupo, Iacopo Colonnelli, Roberto Esposito, Alberto Riccardo Martinelli, Gianluca Mittone, Olivier Beaumont, Berenger Bramas, Lionel Eyraud-Dubois, Brice Goglin, Abdou Guermouche, Raymond Namyst, Samuel Thibault, Antonio Filgueras, Miquel Vidal, Carlos Alvarez, Xavier Martorell, Ariel Oleksiak, Michal Kulczewski, Alessandro Lonardo, Piero Vicini, Francesco Lo Cicero, Francesco Simula, Andrea Biagioni, Paolo Cretaro, Ottorino Frezza, Pier Stanislao Paolucci, Matteo Turisini, Francesco Giacomini, Tommaso Boccali, Simone Montangero, Roberto Ammendola

TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale Proceedings Article

In: Proc. of the 24th Euromicro Conference on Digital System Design (DSD), IEEE, Palermo, Italy, 2021.

Abstract | Links | BibTeX | Tags: streamflow, textarossa

Marco Aldinucci

From skeletons to workflows in the cloud-edge era Miscellaneous

14th Intl. Symposium on High-Level Programming and Applications (HLPP), 2021, (Keynote talk).

Abstract | Links | BibTeX | Tags: across, admire, deephealth, keynote, streamflow

Marco Aldinucci, Iacopo Colonnelli

The Universal Cloud-HPC Pipeline for the AI-Assisted Explainable Diagnosis of COVID-19 Pneumonia Miscellaneous

NVidia GTC’21, 2021, (Invited talk).

Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, invited, streamflow

Iacopo Colonnelli

StreamFlow: cross breeding cloud with HPC Miscellaneous

2021 CWL Mini Conference, 2021, (Invited talk).

Abstract | Links | BibTeX | Tags: deephealth, invited, streamflow

Iacopo Colonnelli, Barbara Cantalupo, Concetto Spampinato, Matteo Pennisi, Marco Aldinucci

Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 diagnosis Proceedings Article

In: Iannone, Francesco (Ed.): ENEA CRESCO in the fight against COVID-19, ENEA, 2021.

Abstract | Links | BibTeX | Tags: streamflow

Iacopo Colonnelli, Barbara Cantalupo, Roberto Esposito, Matteo Pennisi, Concetto Spampinato, Marco Aldinucci

HPC Application Cloudification: The StreamFlow Toolkit Proceedings Article

In: Bispo, João, Cherubin, Stefano, Flich, José (Ed.): 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021), pp. 5:1–5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 2021, ISSN: 2190-6807.

Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, streamflow

Iacopo Colonnelli, Barbara Cantalupo, Ivan Merelli, Marco Aldinucci

StreamFlow: cross-breeding cloud with HPC Journal Article

In: IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 4, pp. 1723–1737, 2021.

Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, streamflow

2020

Iacopo Colonnelli

StreamFlow: cross breeding cloud with HPC Miscellaneous

HPC-Europa3 2nd Transnational Access Meeting (TAM), 2020, (Invited talk).

Abstract | Links | BibTeX | Tags: invited, streamflow

2019

Iacopo Colonnelli

StreamFlow: un approccio dichiarativo a workflow e pipeline di micro-servizi Miscellaneous

Workshop GARR 2019, 2019.

Abstract | Links | BibTeX | Tags: streamflow

CAPIO

CAPIO (Cross-Application Programmable I/O) is a middleware capable of transparently injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without modifying the business code. The contribution is twofold: at design time, a new I/O coordination language allows users to annotate workflow data dependencies with synchronization semantics; at run time, a user-space software layer automatically turns a batch execution into a streaming execution according to the semantics expressed in the configuration file.

CAPIO is a libre software available on Github (https://github.com/High-Performance-IO/capio) under the LGPLv3 license

Publications

2023

Alberto Riccardo Martinelli, Massimo Torquati, Marco Aldinucci, Iacopo Colonnelli, Barbara Cantalupo

CAPIO: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows Proceedings Article

In: 2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC), IEEE, Goa, India, 2023, (In press).

Abstract | Links | BibTeX | Tags: admire, capio, eupex, icsc

FastFlow

FastFlow | Parallel programming frameworks

FastFlow (斋戒流) is a C++ parallel programming framework advocating high-level, pattern-based parallel programming. It chiefly supports streaming and data parallelism, targeting heterogenous platforms composed of clusters of shared-memory platforms, possibly equipped with computing accelerators such as NVidia GPGPUs, Xeon Phi, Tilera TILE64.

At today,  FastFlow has been the background technology of 3 European Projects and 1 National project for an aggregate total cost of 12M € (ParaPhrase FP7, REPARA FP7, Rephrase H2020, and IMPACT, see projects section). We are still actively developing  FastFlow along with its underlying technology, and we are wide open to turn challenges in research and innovation. More details can be found in the main FastFlow website.

FastFlow comes as a C++ template library designed as a stack of layers that progressively abstracts the programming of parallel applications. The goal of the stack is threefold: portability, extensibility, and performance. For this, all three layers are realized as thin strata of C++ templates that are 1) seamlessly portable, 2) easily extended via subclassing, and 3) statically compiled and cross-optimized with the application. The terse design ensures easy portability on almost all OSes and CPUs with a C++ compiler.

More details in the FastFlow website.

Publications
68 entries « 1 of 2 »

2020

Jose Daniel Garcia, Jose Daniel Rio, Marco Aldinucci, Fabio Tordini, Marco Danelutto, Gabriele Mencagli, Massimo Torquati

Challenging the abstraction penalty in parallel patterns libraries: Adding FastFlow support to GrPPI Journal Article

In: The Journal of Supercomputing, vol. 76, no. 7, pp. 5139–5159, 2020.

Abstract | Links | BibTeX | Tags: fastflow, rephrase

2019

Marco Danelutto, Tiziano De Matteis, Daniele De Sensi, Gabriele Mencagli, Massimo Torquati, Marco Aldinucci, Peter Kilpatrick

The RePhrase Extended Pattern Set for Data Intensive Parallel Computing Journal Article

In: International Journal of Parallel Programming, vol. 47, no. 1, pp. 74–93, 2019.

Abstract | Links | BibTeX | Tags: fastflow, rephrase

Massimo Torquati, Gabriele Mencagli, Maurizio Drocco, Marco Aldinucci, Tiziano De Matteis, Marco Danelutto

On Dynamic Memory Allocation in Sliding-Window Parallel Patterns for Streaming Analytics Journal Article

In: The Journal of Supercomputing, vol. 75, no. 8, pp. 4114–4131, 2019.

Abstract | Links | BibTeX | Tags: fastflow, rephrase

2018

Gabriele Mencagli, Massimo Torquati, Fabio Lucattini, Salvatore Cuomo, Marco Aldinucci

Harnessing sliding-window execution semantics for parallel stream processing Journal Article

In: Journal of Parallel and Distributed Computing, vol. 116, pp. 74–88, 2018, ISSN: 0743-7315.

Abstract | Links | BibTeX | Tags: fastflow, rephrase

Claudia Misale, Maurizio Drocco, Guy Tremblay, Alberto R. Martinelli, Marco Aldinucci

PiCo: High-performance data analytics pipelines in modern C++ Journal Article

In: Future Generation Computer Systems, vol. 87, pp. 392–403, 2018.

Abstract | Links | BibTeX | Tags: bigdata, fastflow, toreador

2017

Maurizio Drocco

Parallel Programming with Global Asynchronous Memory: Models, C++ APIs and Implementations PhD Thesis

Computer Science Department, University of Torino, 2017.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase, repara, rephrase, toreador

Claudia Misale

PiCo: A Domain-Specific Language for Data Analytics Pipelines PhD Thesis

Computer Science Department, University of Torino, 2017.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase, repara, rephrase, toreador

Marco Aldinucci, Marco Danelutto, Daniele De Sensi, Gabriele Mencagli, Massimo Torquati

Towards Power-Aware Data Pipelining on Multicores Proceedings Article

In: Proceedings of the 10th International Symposium on High-Level Parallel Programming and Applications, Valladolid, Spain, 2017.

Abstract | Links | BibTeX | Tags: fastflow, rephrase

Marco Aldinucci, Marco Danelutto, Peter Kilpatrick, Massimo Torquati

FastFlow: high-level and efficient streaming on multi-core Book Chapter

In: Pllana, Sabri, Xhafa, Fatos (Ed.): Programming Multi-core and Many-core Computing Systems, Chapter 13, pp. 261–280, John Wiley & Sons, Ltd, 2017, ISBN: 9781119332015.

Abstract | Links | BibTeX | Tags: fastflow

Fabio Tordini, Maurizio Drocco, Claudia Misale, Luciano Milanesi, Pietro Liò, Ivan Merelli, Massimo Torquati, Marco Aldinucci

NuChart-II: the road to a fast and scalable tool for Hi-C data analysis Journal Article

In: International Journal of High Performance Computing Applications, vol. 31, no. 3, pp. 196–211, 2017.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, interomics, mimomics, repara, rephrase

2016

Fabio Tordini

The road towards a Cloud-based High-Performance solution for genomic data analysis PhD Thesis

Computer Science Department, University of Torino, Italy, 2016.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow

Maurizio Drocco, Claudia Misale, Marco Aldinucci

A Cluster-As-Accelerator approach for SPMD-free Data Parallelism Proceedings Article

In: Proc. of 24th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), pp. 350–353, IEEE, Crete, Greece, 2016.

Abstract | Links | BibTeX | Tags: fastflow, rephrase

Vladimir Janjic, Christopher Brown, Kenneth MacKenzie, Kevin Hammond, Marco Danelutto, Marco Aldinucci, Jose Daniel Garcia

RPL: A Domain-Specific Language for Designing and Implementing Parallel C++ Applications Proceedings Article

In: Proc. of Intl. Euromicro PDP 2016: Parallel Distributed and network-based Processing, IEEE, Crete, Greece, 2016.

Abstract | Links | BibTeX | Tags: fastflow, rephrase

Marco Aldinucci, Sonia Campa, Marco Danelutto, Peter Kilpatrick, Massimo Torquati

Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing Journal Article

In: International Journal of Parallel Programming, vol. 44, no. 3, pp. 531–551, 2016, ISSN: 0885-7458.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase, repara

Fabio Tordini, Ivan Merelli, Pietro Liò, Luciano Milanesi, Marco Aldinucci

NuchaRt: embedding high-level parallel computing in R for augmented Hi-C data analysis Book Section

In: Publishing, Springer International (Ed.): Computational Intelligence Methods for Bioinformatics and Biostatistics, vol. 9874, pp. 259–272, Springer International Publishing, Cham (ZG), 2016, ISBN: 978-3-319-44331-7.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, interomics, mimomics, repara

Fabio Tordini

A cloud solution for multi-omics data integration Proceedings Article

In: Proceedings of the 16th IEEE International Conference on Scalable Computing and Communication, pp. 559–566, IEEE Computer Society, 2016, (Best paper award).

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, rephrase

Manuel F. Dolz, David Rio Astorga, Javier Fernández, J. Daniel Garc’ıa, Félix Garc’ıa-Carballeira, Marco Danelutto, Massimo Torquati

Embedding Semantics of the Single-Producer/Single-Consumer Lock-Free Queue into a Race Detection Tool Proceedings Article

In: Proceedings of the 7th International Workshop on Programming Models and Applications for Multicores and Manycores, pp. 20–29, ACM, Barcelona, Spain, 2016, ISBN: 978-1-4503-4196-7.

Links | BibTeX | Tags: fastflow, repara

Andrea Bracciali, Marco Aldinucci, Murray Patterson, Tobias Marschall, Nadia Pisanti, Ivan Merelli, Massimo Torquati

pWhatsHap: efficient haplotyping for future generation sequencing Journal Article

In: BMC Bioinformatics, vol. 17, no. Suppl 11, pp. 342, 2016.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase, rephrase

2015

Paolo Inaudi

Progettazione e sviluppo di un provider libfabric per la rete ad alte prestazioni Ronniee/A3Cube Masters Thesis

Computer Science Department, University of Torino, 2015.

BibTeX | Tags: fastflow

Marco Aldinucci, Marco Danelutto, Maurizio Drocco, Peter Kilpatrick, Guilherme Peretti Pezzi, Massimo Torquati

The Loop-of-Stencil-Reduce paradigm Proceedings Article

In: Proc. of Intl. Workshop on Reengineering for Parallelism in Heterogeneous Parallel Platforms (RePara), pp. 172–177, IEEE, Helsinki, Finland, 2015.

Abstract | Links | BibTeX | Tags: fastflow, nvidia, repara

Fabio Tordini, Maurizio Drocco, Ivan Merelli, Luciano Milanesi, Pietro Liò, Marco Aldinucci

NuChart-II: a graph-based approach for the analysis and interpretation of Hi-C data Proceedings Article

In: Serio, Clelia Di, Liò, Pietro, Nonis, Alessandro, Tagliaferri, Roberto (Ed.): Proc. of 11th Intl. Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), pp. 298–311, Springer, Cambridge, UK, 2015, ISBN: 978-3-319-24461-7.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, hirma, interomics, mimomics, paraphrase, repara

Maurizio Drocco, Claudia Misale, Guilherme Peretti Pezzi, Fabio Tordini, Marco Aldinucci

Memory-Optimised Parallel Processing of Hi-C Data Proceedings Article

In: Proc. of 23rd Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), pp. 1–8, IEEE, 2015.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, impact, paraphrase, repara

Fabio Tordini, Maurizio Drocco, Claudia Misale, Luciano Milanesi, Pietro Liò, Ivan Merelli, Marco Aldinucci

Parallel Exploration of the Nuclear Chromosome Conformation with NuChart-II Proceedings Article

In: Proc. of 23rd Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), IEEE, 2015.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, impact, paraphrase, repara

Paolo Viviani

Parallel Computing Techniques for High Energy Physics Masters Thesis

Physics Department, University of Torino, 2015.

Abstract | BibTeX | Tags: fastflow, impact

Ivan Merelli, Fabio Tordini, Maurizio Drocco, Marco Aldinucci, Pietro Liò, Luciano Milanesi

Integrating Multi-omic features exploiting Chromosome Conformation Capture data Journal Article

In: Frontiers in Genetics, vol. 6, no. 40, 2015, ISSN: 1664-8021.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, hirma, interomics, mimomics

Marco Aldinucci, Andrea Bracciali, Tobias Marschall, Murray Patterson, Nadia Pisanti, Massimo Torquati

High-Performance Haplotype Assembly Proceedings Article

In: Serio, Clelia Di, Liò, Pietro, Nonis, Alessandro, Tagliaferri, Roberto (Ed.): Computational Intelligence Methods for Bioinformatics and Biostatistics – 11th International Meeting, CIBB 2014, Cambridge, UK, June 26-28, 2014, Revised Selected Papers, pp. 245–258, Springer, Cambridge, UK, 2015.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow

Marco Aldinucci, Guilherme Peretti Pezzi, Maurizio Drocco, Concetto Spampinato, Massimo Torquati

Parallel Visual Data Restoration on Multi-GPGPUs using Stencil-Reduce Pattern Journal Article

In: International Journal of High Performance Computing Applications, vol. 29, no. 4, pp. 461–472, 2015.

Abstract | Links | BibTeX | Tags: fastflow, impact, nvidia, paraphrase

2014

Marco Aldinucci, Sonia Campa, Marco Danelutto, Peter Kilpatrick, Massimo Torquati

Pool evolution: a domain specific parallel pattern Proceedings Article

In: Proc.of the 7th Intl. Symposium on High-level Parallel Programming and Applications (HLPP), Amsterdam, The Netherlands, 2014.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase, repara

Marco Aldinucci, Massimo Torquati, Maurizio Drocco, Guilherme Peretti Pezzi, Concetto Spampinato

FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs Proceedings Article

In: GPU Technology Conference (GTC), San Jose, CA, USA, 2014.

Abstract | Links | BibTeX | Tags: fastflow, gpu, impact, nvidia, paraphrase

Marco Aldinucci, Massimo Torquati, Maurizio Drocco, Guilherme Peretti Pezzi, Concetto Spampinato

An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs Proceedings Article

In: GPU Technology Conference (GTC), San Jose, CA, USA, 2014.

Abstract | Links | BibTeX | Tags: fastflow, gpu, impact, nvidia, paraphrase

Daniele Buono, Marco Danelutto, Tiziano De Matteis, Gabriele Mencagli, Massimo Torquati

A Lightweight Run-Time Support For Fast Dense Linear Algebra on Multi-Core Proceedings Article

In: Proc. of the 12th International Conference on Parallel and Distributed Computing and Networks (PDCN 2014), IASTED, ACTA press, 2014.

BibTeX | Tags: fastflow

Marco Aldinucci, Massimo Torquati, Concetto Spampinato, Maurizio Drocco, Claudia Misale, Cristina Calcagno, Mario Coppo

Parallel stochastic systems biology in the cloud Journal Article

In: Briefings in Bioinformatics, vol. 15, no. 5, pp. 798–813, 2014, ISSN: 1467-5463.

Abstract | Links | BibTeX | Tags: biobits, bioinformatics, fastflow, impact, paraphrase

Marco Aldinucci, Sonia Campa, Marco Danelutto, Peter Kilpatrick, Massimo Torquati

Design patterns percolating to parallel programming framework implementation Journal Article

In: International Journal of Parallel Programming, vol. 42, no. 6, pp. 1012–1031, 2014, ISSN: 0885-7458.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

Marco Aldinucci, Salvatore Ruggieri, Massimo Torquati

Decision Tree Building on Multi-Core using FastFlow Journal Article

In: Concurrency and Computation: Practice and Experience, vol. 26, no. 3, pp. 800–820, 2014.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

Marco Aldinucci, Cristina Calcagno, Mario Coppo, Ferruccio Damiani, Maurizio Drocco, Eva Sciacca, Salvatore Spinella, Massimo Torquati, Angelo Troina

On designing multicore-aware simulators for systems biology endowed with on-line statistics Journal Article

In: BioMed Research International, 2014.

Abstract | Links | BibTeX | Tags: biobits, bioinformatics, fastflow, paraphrase

Marco Aldinucci, Maurizio Drocco, Guilherme Peretti Pezzi, Claudia Misale, Fabio Tordini, Massimo Torquati

Exercising high-level parallel programming on streams: a systems biology use case Proceedings Article

In: Proc. of 34th IEEE Intl. Conference on Distributed Computing Systems Workshops (ICDCSW), IEEE, Madrid, Spain, 2014.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, impact, paraphrase

Marco Aldinucci, Guilherme Peretti Pezzi, Maurizio Drocco, Fabio Tordini, Peter Kilpatrick, Massimo Torquati

Parallel video denoising on heterogeneous platforms Proceedings Article

In: Proc. of Intl. Workshop on High-level Programming for Heterogeneous and Hierarchical Parallel Systems (HLPGPU), 2014.

Abstract | Links | BibTeX | Tags: fastflow, impact, paraphrase

Claudia Misale

Accelerating Bowtie2 with a lock-less concurrency approach and memory affinity Proceedings Article

In: Aldinucci, Marco, D’Agostino, Daniele, Kilpatrick, Peter (Ed.): Proc. of Intl. Euromicro PDP 2014: Parallel Distributed and network-based Processing, IEEE, Torino, Italy, 2014, ((Best paper award)).

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

Alessandro Secco, Irfan Uddin, Guilherme Peretti Pezzi, Massimo Torquati

Message passing on InfiniBand RDMA for parallel run-time supports Proceedings Article

In: Aldinucci, Marco, D’Agostino, Daniele, Kilpatrick, Peter (Ed.): Proc. of Intl. Euromicro PDP 2014: Parallel Distributed and network-based Processing, IEEE, Torino, Italy, 2014.

Abstract | Links | BibTeX | Tags: fastflow, impact, paraphrase

Maurizio Drocco, Marco Aldinucci, Massimo Torquati

A Dynamic Memory Allocator for heterogeneous platforms Proceedings Article

In: Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) – Poster Abstracts, HiPEAC, Fiuggi, Italy, 2014.

Abstract | Links | BibTeX | Tags: fastflow, nvidia

Claudia Misale, Giulio Ferrero, Massimo Torquati, Marco Aldinucci

Sequence alignment tools: one parallel pattern to rule them all? Journal Article

In: BioMed Research International, 2014.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow, paraphrase, repara

2013

Maurizio Drocco

Parallel stochastic simulators in systems biology: the evolution of the species Masters Thesis

Computer Science Department, University of Torino, Italy, 2013.

Abstract | Links | BibTeX | Tags: fastflow

Marco Aldinucci, Fabio Tordini, Maurizio Drocco, Massimo Torquati, Mario Coppo

Parallel stochastic simulators in system biology: the evolution of the species Proceedings Article

In: Proc. of 21st Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), IEEE, Belfast, Nothern Ireland, U.K., 2013.

Abstract | Links | BibTeX | Tags: bioinformatics, fastflow

Claudia Misale, Marco Aldinucci, Massimo Torquati

Memory affinity in multi-threading: the Bowtie2 case study Proceedings Article

In: Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) – Poster Abstracts, HiPEAC, Fiuggi, Italy, 2013, ISBN: 9789038221908.

Abstract | Links | BibTeX | Tags: fastflow

Marco Aldinucci, Sonia Campa, Peter Kilpatrick, Massimo Torquati

Structured Data Access Annotations for Massively Parallel Computations Proceedings Article

In: Euro-Par 2012 Workshops, Proc. of the ParaPhrase Workshop on Parallel Processing, pp. 381–390, Springer, 2013.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

Marco Aldinucci, Sonia Campa, Marco Danelutto, Peter Kilpatrick, Massimo Torquati

Targeting Distributed Systems in FastFlow Proceedings Article

In: Euro-Par 2012 Workshops, Proc. of the CoreGrid Workshop on Grids, Clouds and P2P Computing, pp. 47–56, Springer, 2013.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

Marco Aldinucci, Sonia Campa, Fabio Tordini, Massimo Torquati, Peter Kilpatrick

An abstract annotation model for skeletons Book Section

In: Beckert, Bernhard, Damiani, Ferruccio, Boer, Frank S., Bonsangue, Marcello M. (Ed.): Formal Methods for Components and Objects: Intl. Symposium, FMCO 2011, Torino, Italy, October 3-5, 2011, Revised Invited Lectures, vol. 7542, pp. 257–276, Springer, 2013, ISBN: 978-3-642-35886-9.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

2012

Marco Aldinucci, Concetto Spampinato, Maurizio Drocco, Massimo Torquati, Simone Palazzo

A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising Proceedings Article

In: Djemal, K., Deriche, M., Puech, W., Ucan, Osman N. (Ed.): Proc. of 2nd Intl. Conference on Image Processing Theory Tools and Applications (IPTA), pp. 97–102, IEEE, Istambul, Turkey, 2012, ISBN: 978-1-4673-2582-0.

Abstract | Links | BibTeX | Tags: fastflow, impact

Marco Aldinucci, Marco Danelutto, Peter Kilpatrick, Massimiliano Meneghin, Massimo Torquati

An Efficient Unbounded Lock-Free Queue for Multi-core Systems Proceedings Article

In: Proc. of 18th Intl. Euro-Par 2012 Parallel Processing, pp. 662–673, Springer, Rhodes Island, Greece, 2012.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

Marco Aldinucci, Marco Danelutto, Peter Kilpatrick, Massimo Torquati

Targeting heterogeneous architectures via macro data flow Journal Article

In: Parallel Processing Letters, vol. 22, no. 2, 2012, ISSN: 0129-6264.

Abstract | Links | BibTeX | Tags: fastflow, paraphrase

68 entries « 1 of 2 »

Discontinued Parallel Computing tools

Read more

Parallel Programming with Global Asynchronous Memory: Models, C++ APIs and Implementations
M. Drocco, “Parallel programming with global asynchronous memory: models, C++ APIs and implementations,” PhD Thesis, 2017.  doi:10.5281/zenodo.1037585 

PiCo (Pipeline Composition) is an open-source C++11 header-only DSL for high-performance data analytics, featuring low latency, high throughput, and minimal memory footprint on multi-core platforms. For more information see the PiCo paper.

The full software package supporting the development of distributed and multi-core applications based on autonomic components and behavioural skeletons is available under GPL license. More information on the GridCOMP page. The Grid Component Model (GCM) has been standardised by ETSI: DTS/GRID-0004-1 (27/08/2008), DTS/GRID-004-2 (27/08/2008), DTS/GRID-0004-3 (20/03/2009), DTS/GRID-0004-4 (24/03/2010).

VirtuaLinux is a Linux meta-distribution that allows the creation, deployment and administration of virtualized clusters with no single point of failure. VirtuaLinux architecture supports disk-less configurations and provides an efficient, iSCSI based abstraction of the SAN. Clusters running VirtuaLinux exhibits no master node to boost resilience and flexibility. Thanks to its storage virtualisation layer, VIrtuaLinux was able to deploy hundreds of VMs in a few seconds. Actually, VirtuaLinux realises a cloud (but the cloud word with the current meaning did not exist in 2006).

Muskel is a parallel programming library providing users with structured parallel constructs (skeletons) that can be used to implement efficient parallel applications. Muskel applications run on networks/clusters of workstations equipped with Java (1.5 or greater). The skeletons are implemented exploiting macro data flow technology. Muskel extends Lithium with many interesting features, in particular with adaptive and autonomic features.

AD-HOC (Adaptive Distributed Herd of Object Caches), is a fast and robust distributed object repository. It provides applications with a distributed storage manager that virtualise PC’s memories into a unique common distributed storage space. Ad-HOC can effectively be used to implement DSMs as well as distributed cache subsystems. a high-performance distributed shared memory server for cluster and grid, and its applications. ADHOC is a basic block enabling the development of shared memory run-time supports and applications for dynamic and unreliable executing environments (C++, GPL). The libraries and applications developed on top of ADHOC include:

  • parallel file system exhibiting the same API and better performance of the PVFS;
  • distributed cache that can be plugged in the Apache webserver with no modifications of Apache code. The cache substantially improve web server farm performance with no additional costs;
  • a Distributed Shared Memory (DSM) for ASSIST.

ASSIST (A Software development System based on Integrated Skeleton Technology) is a parallel programming environment based on skeleton and coordination language technology aimed at the development of distributed high-performance applications. ASSIST applications should be compiled in binary packages that can be deployed and run on grids, including those exhibiting heterogeneous platforms. Deployment and run are provided through standard middleware services (e.g. Globus) enriched with the ASSIST run-time support. ASSIST applications are described by means of a coordination language, which can express arbitrary graphs of modules, interconnected by typed streams of data. For more information see ASSIST papers.

Lithium is a Java-based parallel programming library providing users with structured parallel constructs (patterns/skeletons) that can be used to implement efficient parallel applications on clusters. The skeletons (including pipe, farm, map, reduce, loop) are implemented exploiting macro data flow technology. Lithium skeletons admit a formal specification of both functional and extra-functional behaviour.

Eskimo (Easy SKeleton Interface – Memory Oriented), which was part of my PhD dissertation, is a first (maybe a bit naive) tentative to bring skeletal/pattern-based programming on the shared memory model. To my knowledge, there were no previous experiments since skeletal programming was exclusively living in the message passing arena. From a certain viewpoint, it can be considered an ancestor of Fastflow (and other libraries in this class, such as Intel TBB).

META is a toolkit for the source-to-source optimisation of pattern-based/skeletal parallel programs (OCaml, GPL). It includes a quite efficient subtree-matching implementation.

SkIE (Skeleton-based Integrated Environment) is a skeleton-based parallel programming environment. SkIE was an engineered version of P3L developed within Quadrics Supercomputing World (QSW) and Alenia Aerospace. Within QSW, I have designed and developed part of the compiler back-end.