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hipMAGMA v2.0.0

Cade Brown; Ahmad Abdelfattah; Stanimire Tomov; Jack Dongarra


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  <identifier identifierType="DOI">10.5281/zenodo.3928667</identifier>
  <creators>
    <creator>
      <creatorName>Cade Brown</creatorName>
      <affiliation>University of Tennessee</affiliation>
    </creator>
    <creator>
      <creatorName>Ahmad Abdelfattah</creatorName>
      <affiliation>University of Tennessee</affiliation>
    </creator>
    <creator>
      <creatorName>Stanimire Tomov</creatorName>
      <affiliation>University of Tennessee</affiliation>
    </creator>
    <creator>
      <creatorName>Jack Dongarra</creatorName>
      <affiliation>University of Tennessee</affiliation>
    </creator>
  </creators>
  <titles>
    <title>hipMAGMA v2.0.0</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>dense linear algebra</subject>
    <subject>GPU computing</subject>
    <subject>linear algebra</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-07-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3928667</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3928666</relatedIdentifier>
  </relatedIdentifiers>
  <version>2.0.0</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;The goal of the MAGMA project is to create a new generation of linear algebra libraries that achieves the fastest possible time to an accurate solution on heterogeneous architectures, starting with current multicore + multi-GPU systems. To address the complex challenges stemming from these systems&amp;#39; heterogeneity, massive parallelism, and the gap between compute speed and CPU-GPU communication speed, MAGMA&amp;#39;s research is based on the idea that optimal software solutions will themselves have to hybridize, combining the strengths of different algorithms within a single framework. Building on this idea, the goal is to design linear algebra algorithms and frameworks for hybrid multicore and multi-GPU systems that can enable applications to fully exploit the power that each of the hybrid components offers.&lt;/p&gt;</description>
  </descriptions>
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