This paper describes the Automatically Tuned Linear Algebra Software (ATLAS) project, as well as the fundamental principles that underly it. ATLAS is an instantiation of a new paradigm in high performance library production and maintenance, which we term automated empirical optimization of software; this style of library management has been created in order to allow software to keep pace with the incredible rate of hardware advancement inherent in Moore’s Law. ATLAS is the application of this new paradigm to linear algebra software, with the present emphasis on the basic linear algebra subprograms, a widely used, performance-critical, linear algebra kernel library

This software is also referenced in ORMS.

References in zbMATH (referenced in 195 articles , 1 standard article )

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  7. Vuduc, Richard; Demmel, James W.; Bilmes, Jeff: Statistical models for automatic performance tuning (2001)
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  11. Wang, Weichung; O’Leary, Dianne P.: Adaptive use of iterative methods in predictor-corrector interior point methods for linear programming (2000)
  12. Xiao, Li; Zhang, Xiaodong; Kubricht, Stefan A.: Improving memory performance of sorting algorithms (2000)
  13. Tisseur, Françoise; Dongarra, Jack: A parallel divide and conquer algorithm for the symmetric eigenvalue problem on distributed memory architectures (1999)
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