Barbara Cantalupo

Barbara Canatalupo

Research Engineer
Computer Science Department, University of Turin
Parallel Computing group
Via Pessinetto 12, 10149 Torino – Italy 
E-mail: barbara.cantalupo AT unito.it

Short Bio

Barbara Cantalupo is a Research Engineer at the Computer Science Department of the University of Torino. She received her master’s degree in Computer Science from the University of Pisa (1994), and she has been a researcher in the parallel computing field at University and at the Italian National Research Council (CNR). Afterwards, she has worked for 15 years in several private companies in the area of supercomputing, mobile networks and space, acquiring knowledge on different application fields.

Fields of interest:

  • Workflows, cloud and containers for HPC.

Working on the following European Research project:

Publications

2021

  • I. Colonnelli, B. Cantalupo, R. Esposito, M. Pennisi, C. Spampinato, and M. Aldinucci, “HPC Application Cloudification: The StreamFlow Toolkit,” in 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), Dagstuhl, Germany, 2021, p. 5:1–5:13. doi:10.4230/OASIcs.PARMA-DITAM.2021.5
    [BibTeX] [Abstract] [Download PDF]

    Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud infrastructure can be offloaded to HPC environments to speed them up. We introduce StreamFlow, a novel Workflow Management System that supports such a design pattern and makes it possible to run the steps of a standard workflow model on independent processing elements with no shared storage. We validated the proposed approach’s effectiveness on the CLAIRE COVID-19 universal pipeline, i.e. a reproducible workflow capable of automating the comparison of (possibly all) state-of-the-art pipelines for the diagnosis of COVID-19 interstitial pneumonia from CT scans images based on Deep Neural Networks (DNNs).

    @inproceedings{colonnelli_et_al:OASIcs.PARMA-DITAM.2021.5,
    abstract = {Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud infrastructure can be offloaded to HPC environments to speed them up. We introduce StreamFlow, a novel Workflow Management System that supports such a design pattern and makes it possible to run the steps of a standard workflow model on independent processing elements with no shared storage. We validated the proposed approach's effectiveness on the CLAIRE COVID-19 universal pipeline, i.e. a reproducible workflow capable of automating the comparison of (possibly all) state-of-the-art pipelines for the diagnosis of COVID-19 interstitial pneumonia from CT scans images based on Deep Neural Networks (DNNs).},
    address = {Dagstuhl, Germany},
    annote = {Keywords: cloud computing, distributed computing, high-performance computing, streamflow, workflow management systems},
    author = {Colonnelli, Iacopo and Cantalupo, Barbara and Esposito, Roberto and Pennisi, Matteo and Spampinato, Concetto and Aldinucci, Marco},
    booktitle = {12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021)},
    doi = {10.4230/OASIcs.PARMA-DITAM.2021.5},
    editor = {Bispo, Jo\~{a}o and Cherubin, Stefano and Flich, Jos\'{e}},
    isbn = {978-3-95977-181-8},
    issn = {2190-6807},
    keywords = {deephealth, hpc4ai},
    pages = {5:1--5:13},
    publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
    series = {Open Access Series in Informatics (OASIcs)},
    title = {{HPC Application Cloudification: The StreamFlow Toolkit}},
    url = {https://drops.dagstuhl.de/opus/volltexte/2021/13641/pdf/OASIcs-PARMA-DITAM-2021-5.pdf},
    urn = {urn:nbn:de:0030-drops-136419},
    volume = {88},
    year = {2021},
    bdsk-url-1 = {https://drops.dagstuhl.de/opus/volltexte/2021/13641/pdf/OASIcs-PARMA-DITAM-2021-5.pdf},
    bdsk-url-2 = {https://doi.org/10.4230/OASIcs.PARMA-DITAM.2021.5}
    }

2020

  • I. Colonnelli, B. Cantalupo, I. Merelli, and M. Aldinucci, “Streamflow: cross-breeding cloud with HPC,” IEEE Transactions on Emerging Topics in Computing, 2020. doi:10.1109/TETC.2020.3019202
    [BibTeX] [Abstract] [Download PDF]

    Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single cell transcriptomic data analysis workflow.

    @article{20Lstreamflow:tect,
    abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single cell transcriptomic data analysis workflow.},
    author = {Iacopo Colonnelli and Barbara Cantalupo and Ivan Merelli and Marco Aldinucci},
    date-added = {2020-08-27 09:29:49 +0200},
    date-modified = {2020-08-27 09:36:33 +0200},
    doi = {10.1109/TETC.2020.3019202},
    journal = {{IEEE} {T}ransactions on {E}merging {T}opics in {C}omputing},
    keywords = {deephealth, hpc4ai, streamflow},
    title = {StreamFlow: cross-breeding cloud with {HPC}},
    url = {https://arxiv.org/pdf/2002.01558},
    year = {2020},
    bdsk-url-1 = {https://arxiv.org/pdf/2002.01558},
    bdsk-url-2 = {https://doi.org/10.1109/TETC.2020.3019202}
    }