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 197 articles , 1 standard article )

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  1. -: Bibliography of “Algorithms for memory hierarchies. Advanced lectures” (2003)
  2. Karniadakis, George Em; Kirby, Robert M. II: Parallel scientific computing in C++ and MPI. A seamless approach to parallel algorithms and their implementation. With CD-ROM (2003)
  3. Knijnenburg, P. M. W.; Kisuki, T.; O’Boyle, M. F. P.: Combined selection of tile sizes and unroll factors using iterative compilation (2003)
  4. Kowarschik, Markus; Weiß, Christian: An overview of cache optimization techniques and cache-aware numerical algorithms (2003)
  5. Popoola, Ben; Gough, Paul: Evaluating the performance of space plasma simulations using FPGA’s (2003)
  6. Quintana-Ortí, Enrique S.; van de Geijn, Robert A.: Formal derivation of algorithms: the triangular sylvester equation (2003)
  7. Wen, Tong; Edelman, Alan; Gorsich, David: A fast projected conjugate gradient algorithm for training support vector machines. (2003)
  8. Andersen, Bjarne S.; Gunnels, John A.; Gustavson, Fred; Waśniewski, Jerzy: A recursive formulation of the inversion of symmetric positive definite matrices in packed storage data format (2002)
  9. Chauhan, Arun; Kennedy, Ken: Reducing and vectorizing procedures for telescoping languages (2002)
  10. Chung, Yoojin: Parallel information retrieval with query expansion (2002)
  11. Dumas, Jean-Guillaume; Gautier, Thierry; Pernet, Clément: Finite field linear algebra subroutines (2002)
  12. Ehold, Harald J.; Gansterer, Wilfried N.; Kvasnicka, Dieter F.; Ueberhuber, Christoph W.: Optimizing Local Performance in HPF (2002)
  13. Fagg, Graham E.; Dongarra, Jack J.: HARNESS fault tolerant MPI design, usage and performance issues. (2002)
  14. Gustavson, Fred G.: New generalized data structures for matrices lead to a variety of high performance algorithms (2002)
  15. Irony, D.; Shklarski, G.; Toledo, S.: Parallel and fully recursive multifrontal supernodal sparse Cholesky (2002)
  16. Jonsson, Isak; Kågström, Bo: Parallel two-sided Sylvester-type matrix equation solvers for SMP systems using recursive blocking (2002)
  17. Korch, Matthias; Rauber, Thomas; Rünger, Gudula: Pipelining for locality improvement in RK methods (2002)
  18. Kowarschik, Markus; Rüde, Ulrich; Thürey, Nils; Weiß, Christian: Performance optimization of 3D multigrid on hierarchical memory architectures (2002)
  19. Kowarschik, M.; Rüde, U.; Weiß, C.: Data layout optimizations for variable coefficient multigrid (2002)
  20. Kuroda, Hisayasu; Katagiri, Takahiro; Kanada, Yasumasa: Knowledge discovery in auto-tuning parallel numerical library (2002)

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