ATLAS

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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  1. Matsuoka, S.; Itou, S.: Towards performance evaluation of high-performance computing on multiple Java platforms (2001)
  2. Nishimura, Seiji; Takahashi, Daisuke; Shigehara, Takaomi; Mizoguchi, Hiroshi; Mishima, Taketoshi: A performance study on a single processing node of the HITACHI SR8000 (2001)
  3. Püschel, Markus; Singer, Bryan; Veloso, Manuela; Moura, José M. F.: Fast automatic generation of DSP algorithms (2001)
  4. Strout, Michelle Mills; Carter, Larry; Ferrante, Jeanne: Rescheduling for locality in sparse matrix computations (2001)
  5. Tinetti, Fernando; Quijano, Antonio; De Giusti, Armando; Luque, Emilio: Heterogeneous networks of workstations and the parallel matrix multiplication (2001)
  6. Vadhiyar, Sathish S.; Fagg, Graham E.; Dongarra, Jack J.: Towards an accurate model for collective communications (2001)
  7. Vuduc, Richard; Demmel, James W.; Bilmes, Jeff: Statistical models for automatic performance tuning (2001)
  8. Boisvert, Ronald F.: Mathematical software: Past, present, and future (2000)
  9. Bridson, Robert; Tang, Wei-Pai: Refining an approximate inverse (2000)
  10. Siek, Jeremy; Lumsdaine, Andrew: A modern framework for portable high-performance numerical linear algebra (2000)
  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)
  14. -: Biographies (1996)
  15. Lewis, Ted G.; El-Rewini, Hesham; Kim, In-Kyu: Introduction to parallel computing (1992)

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