Rapid development of high-performance linear algebra libraries

  • Authors:

  • Paolo Bientinesi;John A. Gunnels;Fred G. Gustavson;Greg M. Henry;Margaret Myers;Enrique S. Quintana-Ortí;Robert A. van de Geijn

  • Affiliations:

  • Department of Computer Sciences, The University of Texas, Austin;IBM's T.J. Watson Research Center;IBM's T.J. Watson Research Center;Intel Corp.;Department of Computer Sciences, The University of Texas, Austin;Universidad Jaume I, Spain;Department of Computer Sciences, The University of Texas, Austin

  • Venue:
  • PARA'04 Proceedings of the 7th international conference on Applied Parallel Computing: state of the Art in Scientific Computing
  • Year:
  • 2004

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Abstract

We present a systematic methodology for deriving and implementing linear algebra libraries. It is quite common that an application requires a library of routines for the computation of linear algebra operations that are not (exactly) supported by commonly used libraries like LAPACK. In this situation, the application developer has the option of casting the operation into one supported by an existing library, often at the expense of performance, or implementing a custom library, often requiring considerable effort. Our recent discovery of a methodology based on formal derivation of algorithm allows such a user to quickly derive proven correct algorithms. Furthermore it provides an API that allows the so-derived algorithms to be quickly translated into high-performance implementations.