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Bringing High Performance Computing to Big Data Algorithms

  • H. Anzt
  • J. Dongarra
  • M. Gates
  • J. KurzakEmail author
  • P. Luszczek
  • S. Tomov
  • I. Yamazaki
Chapter

Abstract

Many ideas of High Performance Computing are applicable to Big Data problems. The more so now, that hybrid, GPU computing gains traction in mainstream computing applications. This work discusses the differences between the High Performance Computing software stack and the Big Data software stack and then focuses on two popular computing workloads, the Alternating Least Squares algorithm and the Singular Value Decomposition, and shows how their performance can be maximized using hybrid computing techniques.

Keywords

Singular Value Decomposition Thread Block Latent Semantic Indexing Alternate Little Square Implicit Feedback 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • H. Anzt
    • 1
  • J. Dongarra
    • 1
  • M. Gates
    • 1
  • J. Kurzak
    • 1
    Email author
  • P. Luszczek
    • 1
  • S. Tomov
    • 1
  • I. Yamazaki
    • 1
  1. 1.Innovative Computing LaboratoryUniversity of TennesseeKnoxvilleUSA

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