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hipMAGMA v2.0.0

Cade Brown; Ahmad Abdelfattah; Stanimire Tomov; Jack Dongarra


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3928667", 
  "language": "eng", 
  "title": "hipMAGMA v2.0.0", 
  "issued": {
    "date-parts": [
      [
        2020, 
        7, 
        2
      ]
    ]
  }, 
  "abstract": "<p>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&#39; heterogeneity, massive parallelism, and the gap between compute speed and CPU-GPU communication speed, MAGMA&#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.</p>", 
  "author": [
    {
      "family": "Cade Brown"
    }, 
    {
      "family": "Ahmad Abdelfattah"
    }, 
    {
      "family": "Stanimire Tomov"
    }, 
    {
      "family": "Jack Dongarra"
    }
  ], 
  "version": "2.0.0", 
  "type": "article", 
  "id": "3928667"
}
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