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 179 articles , 1 standard article )

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  1. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the lighthouse framework (2016)
  2. Martinsson, Per-Gunnar; Voronin, Sergey: A randomized blocked algorithm for efficiently computing rank-revealing factorizations of matrices (2016)
  3. Neale, Michael C.; Hunter, Michael D.; Pritikin, Joshua N.; Zahery, Mahsa; Brick, Timothy R.; Kirkpatrick, Robert M.; Estabrook, Ryne; Bates, Timothy C.; Maes, Hermine H.; Boker, Steven M.: OpenMX 2.0: extended structural equation and statistical modeling (2016)
  4. Aparicio, Juan; Lopez-Espin, Jose J.; Martinez-Moreno, Raul; Pastor, Jesus T.: Benchmarking in data envelopment analysis: an approach based on genetic algorithms and parallel programming (2014)
  5. Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)
  6. Di Napoli, Edoardo; Fabregat-Traver, Diego; Quintana-Ortí, Gregorio; Bientinesi, Paolo: Towards an efficient use of the BLAS library for multilinear tensor contractions (2014)
  7. Ho, Kenneth L.; Greengard, Leslie: A fast semidirect least squares algorithm for hierarchically block separable matrices (2014)
  8. Ivanenko, P.A.; Doroshenko, A.Yu.: Method of automated generation of autotuners for parallel programs (2014)
  9. Parikh, Neal; Boyd, Stephen: Block splitting for distributed optimization (2014)
  10. Schatz, Martin D.; Low, Tze Meng; van de Geijn, Robert A.; Kolda, Tamara G.: Exploiting symmetry in tensors for high performance: multiplication with symmetric tensors (2014)
  11. Velichka, M.D.; Jacobson, M.J. jun.; Stein, A.: Computing discrete logarithms in the Jacobian of high-genus hyperelliptic curves over even characteristic finite fields (2014)
  12. Abed, Khalid H.; Morris, Gerald R.: Improving performance of codes with large/irregular stride memory access patterns via high performance reconfigurable computers (2013)
  13. Bouchard-C^oté, Alexandre: A note on probabilistic models over strings: the linear algebra approach (2013)
  14. Castaldo, Anthony M.; Whaley, R.Clint; Samuel, Siju: Scaling LAPACK panel operations using parallel cache assignment (2013)
  15. Kouya, Tomonori: Performance evaluation of multiple and mixed precision iterative refinement method and its application to high-order implicit Runge-Kutta method (2013)
  16. Poulson, Jack; Marker, Bryan; van de Geijn, Robert A.; Hammond, Jeff R.; Romero, Nichols A.: Elemental, a new framework for distributed memory dense matrix computations (2013)
  17. Vannieuwenhoven, Nick; Meerbergen, Karl: IMF: an incomplete multifrontal $LU$-factorization for element-structured sparse linear systems (2013)
  18. Du, Peng; Weber, Rick; Luszczek, Piotr; Tomov, Stanimire; Peterson, Gregory; Dongarra, Jack: From CUDA to opencl: towards a performance-portable solution for multi-platform GPU programming (2012)
  19. Hartley, Timothy D.R.; Saule, Erik; Çatalyürek, Ümit V.: Improving performance of adaptive component-based dataflow middleware (2012)
  20. Ho, Kenneth L.; Greengard, Leslie: A fast direct solver for structured linear systems by recursive skeletonization (2012)

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