Papers | Parallel Computing
2024
Simone Leo, Michael R. Crusoe, Laura Rodríguez-Navas, Raül Sirvent, Alexander Kanitz, Paul De Geest, Rudolf Wittner, Luca Pireddu, Daniel Garijo, José M. Fernández, Iacopo Colonnelli, Matej Gallo, Tazro Ohta, Hirotaka Suetake, Salvador Capella-Gutierrez, Renske Wit, Bruno P. Kinoshita, Stian Soiland-Reyes
Recording provenance of workflow runs with RO-Crate Journal Article
In: PLoS ONE, vol. 19, no. 9, pp. 1–35, 2024.
Abstract | Links | BibTeX | Tags: across, eupex, icsc, streamflow
@article{24:pone:wfrunrocrate,
title = {Recording provenance of workflow runs with RO-Crate},
author = {Simone Leo and Michael R. Crusoe and Laura Rodríguez-Navas and Raül Sirvent and Alexander Kanitz and Paul De Geest and Rudolf Wittner and Luca Pireddu and Daniel Garijo and José M. Fernández and Iacopo Colonnelli and Matej Gallo and Tazro Ohta and Hirotaka Suetake and Salvador Capella-Gutierrez and Renske Wit and Bruno P. Kinoshita and Stian Soiland-Reyes},
url = {https://iris.unito.it/retrieve/57752f7b-9f8f-4013-8cef-5b498703d882/journal.pone.0309210.pdf},
doi = {10.1371/journal.pone.0309210},
year = {2024},
date = {2024-09-01},
journal = {PLoS ONE},
volume = {19},
number = {9},
pages = {1–35},
publisher = {Public Library of Science},
abstract = {Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and Schema.org to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain.},
keywords = {across, eupex, icsc, streamflow},
pubstate = {published},
tppubtype = {article}
}
Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and Schema.org to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain.
2023
Iacopo Colonnelli
Workflow Models for Heterogeneous Distributed Systems Proceedings Article
In: Bena, Nicola, Martino, Beniamino Di, Maratea, Antonio, Sperduti, Alessandro, Nardo, Emanuel Di, Ciaramella, Angelo, Montella, Raffaele, Ardagna, Claudio A. (Ed.): Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023, CEUR-WS.org, 2023.
Abstract | Links | BibTeX | Tags: across, eupex, icsc, jupyter-workflow, streamflow
@inproceedings{23:colonnelli:itadata,
title = {Workflow Models for Heterogeneous Distributed Systems},
author = {Iacopo Colonnelli},
editor = {Nicola Bena and Beniamino Di Martino and Antonio Maratea and Alessandro Sperduti and Emanuel Di Nardo and Angelo Ciaramella and Raffaele Montella and Claudio A. Ardagna},
url = {https://ceur-ws.org/Vol-3606/invited77.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023},
volume = {3606},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {This article introduces a novel hybrid workflow abstraction that injects topology awareness directly into the definition of a distributed workflow model. In particular, the article briefly discusses the advantages brought by this approach to the design and orchestration of large-scale data-oriented workflows, the current level of support from state-of-the-art workflow systems, and some future research directions.},
keywords = {across, eupex, icsc, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Alberto Mulone, Sherine Awad, Davide Chiarugi, Marco Aldinucci
Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment Proceedings Article
In: Shahriar, Hossain, Teranishi, Yuuichi, Cuzzocrea, Alfredo, Sharmin, Moushumi, Towey, Dave, Majumder, A. K. M. Jahangir Alam, Kashiwazaki, Hiroki, Yang, Ji-Jiang, Takemoto, Michiharu, Sakib, Nazmus, Banno, Ryohei, Ahamed, Sheikh Iqbal (Ed.): 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, pp. 1858–1863, IEEE, Torino, Italy, 2023.
Abstract | Links | BibTeX | Tags: across, icsc, streamflow
@inproceedings{23:mulone:wide:vcp,
title = {Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment},
author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},
editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},
url = {https://iris.unito.it/bitstream/2318/1919364/1/paper.pdf},
doi = {10.1109/COMPSAC57700.2023.00288},
year = {2023},
date = {2023-01-01},
booktitle = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},
pages = {1858–1863},
publisher = {IEEE},
address = {Torino, Italy},
abstract = {In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.},
howpublished = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},
keywords = {across, icsc, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Iacopo Colonnelli, Bruno Casella, Gianluca Mittone, Yasir Arfat, Barbara Cantalupo, Roberto Esposito, Alberto Riccardo Martinelli, Doriana Medić, Marco Aldinucci
Federated Learning meets HPC and cloud Proceedings Article
In: Bufano, Filomena, Riggi, Simone, Sciacca, Eva, Schillirò, Francesco (Ed.): Astrophysics and Space Science Proceedings, pp. 193–199, Springer, Catania, Italy, 2023, ISBN: 978-3-031-34167-0, (Keynote talk).
Abstract | Links | BibTeX | Tags: across, eupilot, streamflow
@inproceedings{22:ml4astro,
title = {Federated Learning meets HPC and cloud},
author = {Iacopo Colonnelli and Bruno Casella and Gianluca Mittone and Yasir Arfat and Barbara Cantalupo and Roberto Esposito and Alberto Riccardo Martinelli and Doriana Medić and Marco Aldinucci},
editor = {Filomena Bufano and Simone Riggi and Eva Sciacca and Francesco Schillirò},
url = {https://iris.unito.it/retrieve/3ac66baa-9d9a-4e9f-94a5-13700694d8aa/ML4Astro.pdf},
doi = {10.1007/978-3-031-34167-0_39},
isbn = {978-3-031-34167-0},
year = {2023},
date = {2023-01-01},
booktitle = {Astrophysics and Space Science Proceedings},
volume = {60},
pages = {193–199},
publisher = {Springer},
address = {Catania, Italy},
abstract = {HPC and AI are fated to meet for several reasons. This article will discuss some of them and argue why this will happen through the set of methods and technologies that underpin cloud computing. As a paradigmatic example, we present a new federated learning system that collaboratively trains a deep learning model in different supercomputing centers. The system is based on the StreamFlow workflow manager designed for hybrid cloud-HPC infrastructures.},
howpublished = {Machine Learning for Astrophysics (ML4ASTRO)},
note = {Keynote talk},
keywords = {across, eupilot, streamflow},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
2022
Iacopo Colonnelli, Marco Aldinucci
Hybrid Workflows For Large - Scale Scientific Applications Proceedings Article
In: Sixth EAGE High Performance Computing Workshop, pp. 1–5, European Association of Geoscientists & Engineers , Milano, Italy, 2022, ISSN: 2214-4609.
Abstract | Links | BibTeX | Tags: across, eupex
@inproceedings{22:eage-hpc-workshop,
title = {Hybrid Workflows For Large - Scale Scientific Applications},
author = {Iacopo Colonnelli and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/d79ddabb-f9d7-4a55-9f84-1528b1533ba3/Extended_Abstract.pdf},
doi = {10.3997/2214-4609.2022615029},
issn = {2214-4609},
year = {2022},
date = {2022-09-01},
booktitle = {Sixth EAGE High Performance Computing Workshop},
pages = {1–5},
publisher = {European Association of Geoscientists & Engineers },
address = {Milano, Italy},
abstract = {Large-scale scientific applications are facing an irrevrsible transition from monolithic, high-performance oriented codes to modular and polyglot deployments of specialised (micro-)services. The reasons behind this transition are many: coupling of standard solvers with Deep Learning techniques, offloading of data analysis and visualisation to Cloud, and the advent of specialised hardware accelerators. Topology-aware Workflow Management Systems (WMSs) play a crucial role. In particular, topology-awareness allows an explicit mapping of workflow steps onto heterogeneous locations, allowing automated executions on top of hybrid architectures (e.g., cloud+HPC or classical+quantum). Plus, topology-aware WMSs can offer nonfunctional requirements OOTB, e.g. components' life-cycle orchestration, secure and efficient data transfers, fault tolerance, and cross-cluster execution of urgent workloads. Augmenting interactive Jupyter Notebooks with distributed workflow capabilities allows domain experts to prototype and scale applications using the same technological stack, while relying on a feature-rich and user-friendly web interface. This abstract will showcase how these general methodologies can be applied to a typical geoscience simulation pipeline based on the Full Wavefront Inversion (FWI) technique. In particular, a prototypical Jupyter Notebook will be executed interactively on Cloud. Preliminary data analyses and post-processing will be executed locally, while the computationally demanding optimisation loop will be scheduled on a remote HPC cluster.},
keywords = {across, eupex},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Iacopo Colonnelli, Marco Aldinucci, Barbara Cantalupo, Luca Padovani, Sergio Rabellino, Concetto Spampinato, Roberto Morelli, Rosario Di Carlo, Nicolò Magini, Carlo Cavazzoni
Distributed workflows with Jupyter Journal Article
In: Future Generation Computer Systems, vol. 128, pp. 282–298, 2022, ISSN: 0167-739X.
Abstract | Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow
@article{21:FGCS:jupyflow,
title = {Distributed workflows with Jupyter},
author = {Iacopo Colonnelli and Marco Aldinucci and Barbara Cantalupo and Luca Padovani and Sergio Rabellino and Concetto Spampinato and Roberto Morelli and Rosario Di Carlo and Nicolò Magini and Carlo Cavazzoni},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X21003976},
doi = {10.1016/j.future.2021.10.007},
issn = {0167-739X},
year = {2022},
date = {2022-01-01},
journal = {Future Generation Computer Systems},
volume = {128},
pages = {282–298},
abstract = {The designers of a new coordination interface enacting complex workflows have to tackle a dichotomy: choosing a language-independent or language-dependent approach. Language-independent approaches decouple workflow models from the host code's business logic and advocate portability. Language-dependent approaches foster flexibility and performance by adopting the same host language for business and coordination code. Jupyter Notebooks, with their capability to describe both imperative and declarative code in a unique format, allow taking the best of the two approaches, maintaining a clear separation between application and coordination layers but still providing a unified interface to both aspects. We advocate the Jupyter Notebooks' potential to express complex distributed workflows, identifying the general requirements for a Jupyter-based Workflow Management System (WMS) and introducing a proof-of-concept portable implementation working on hybrid Cloud-HPC infrastructures. As a byproduct, we extended the vanilla IPython kernel with workflow-based parallel and distributed execution capabilities. The proposed Jupyter-workflow (Jw) system is evaluated on common scenarios for High Performance Computing (HPC) and Cloud, showing its potential in lowering the barriers between prototypical Notebooks and production-ready implementations.},
keywords = {across, deephealth, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {article}
}
The designers of a new coordination interface enacting complex workflows have to tackle a dichotomy: choosing a language-independent or language-dependent approach. Language-independent approaches decouple workflow models from the host code's business logic and advocate portability. Language-dependent approaches foster flexibility and performance by adopting the same host language for business and coordination code. Jupyter Notebooks, with their capability to describe both imperative and declarative code in a unique format, allow taking the best of the two approaches, maintaining a clear separation between application and coordination layers but still providing a unified interface to both aspects. We advocate the Jupyter Notebooks' potential to express complex distributed workflows, identifying the general requirements for a Jupyter-based Workflow Management System (WMS) and introducing a proof-of-concept portable implementation working on hybrid Cloud-HPC infrastructures. As a byproduct, we extended the vanilla IPython kernel with workflow-based parallel and distributed execution capabilities. The proposed Jupyter-workflow (Jw) system is evaluated on common scenarios for High Performance Computing (HPC) and Cloud, showing its potential in lowering the barriers between prototypical Notebooks and production-ready implementations.