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Self-adapting linear algebra algorithms and software

delete2005-02-01
delete106
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OA
AI
J
James Demmel
J
Jack Dongarra
E
Eijkhout, V
E
Erika Fuentes
A
Antoine Petitet
R
Richard Vuduc
W
Whaley, RC
Y
Yelick, Katherine
DOI：10.1109/JPROC.2004.840848delete
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Abstract

Abstract

En 中文
One of the main obstacles to the efficient solution of scientific problems is the problem of tuning software, both to the available architecture and to the user problem at hand. We describe approaches for-obtaining tuned high-performance kernals and for automatically choosing suitable algorithms. Specifically we describe the generation of dense and sparse Basic Linear Algebra Subprograms (BLAS) kernels, and the selection of linear solver algorithms. However the ideas presented here extend beyond these areas, which can be considered proof of concept.
Keywords:
adaptive methods
Basic Linear Algebra Subprograms (BLAS)
dense kernels
iterative methods
linear systems
matrix-matrix product
matrix-vector product
performance optimization
preconditioners
sparse kernels

Journal

Proceedings of the IEEE cover
Proceedings of the IEEE
IF:
25.9
Papers:
9.9K
Citations:
4.5W

Organization

No organization information available