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StreamFlow: cross-breeding cloud with HPC

Iacopo Colonnelli; Barbara Cantalupo; Ivan Merelli; Marco Aldinucci


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  <identifier identifierType="DOI">10.5281/zenodo.3902872</identifier>
  <creators>
    <creator>
      <creatorName>Iacopo Colonnelli</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9290-2017</nameIdentifier>
      <affiliation>Department of Computer Science, University of Torino, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Barbara Cantalupo</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7575-3902</nameIdentifier>
      <affiliation>Department of Computer Science, University of Torino, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Ivan Merelli</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3587-3680</nameIdentifier>
      <affiliation>Biomedical Technologies (ITB) of the Italian National Research Council (CNR)</affiliation>
    </creator>
    <creator>
      <creatorName>Marco Aldinucci</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8788-0829</nameIdentifier>
      <affiliation>Department of Computer Science, University of Torino, Italy</affiliation>
    </creator>
  </creators>
  <titles>
    <title>StreamFlow: cross-breeding cloud with HPC</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Workflow</subject>
    <subject>High-Performance Computing</subject>
    <subject>Cloud</subject>
    <subject>Bioinformatics</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-02-26</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Working paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3902872</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3902871</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/deephealth</relatedIdentifier>
  </relatedIdentifiers>
  <version>v2</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825111/">825111</awardNumber>
      <awardTitle>Deep-Learning and HPC to Boost Biomedical Applications for Health</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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