Self-Adapting Linear Algebra Algorithms and Software
Demmel, J.
Dongarra, J.
Eijkhout, V.
Fuentes, E.
Petitet, A.
Vuduc, R.
Whaley, R.C.
Yelick, K.
Electr. Eng. & Comput. Sci. Dept., Univ. of California, Berkeley, CA, USA;
This paper appears in: Proceedings of the IEEE
Publication Date: Feb. 2005
Volume: 93,
Issue: 2
On page(s): 293-312
ISSN: 0018-9219
INSPEC Accession Number: 8261068
Digital Object Identifier: 10.1109/JPROC.2004.840848
Current Version Published: 2005-06-27
Abstract
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 kernels 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.
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