The science of deriving dense linear algebra algorithms

  • Authors:
  • Paolo Bientinesi;John A. Gunnels;Margaret E. Myers;Enrique S. Quintana-Ortí;Robert A. van de Geijn

  • Affiliations:
  • The University of Texas at Austin, Austin, TX;IBM T.J. Watson Research Center, Yorktown Heights, NY;The University of Texas at Austin, Austin, TX;Universidad Jaume I, Spain;The University of Texas at Austin, Austin, TX

  • Venue:
  • ACM Transactions on Mathematical Software (TOMS)
  • Year:
  • 2005

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Abstract

In this article we present a systematic approach to the derivation of families of high-performance algorithms for a large set of frequently encountered dense linear algebra operations. As part of the derivation a constructive proof of the correctness of the algorithm is generated. The article is structured so that it can be used as a tutorial for novices. However, the method has been shown to yield new high-performance algorithms for well-studied linear algebra operations and should also be of interest to those who wish to produce best-in-class high-performance codes.