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 181 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. Mi, Wenhui; Shao, Xuecheng; Su, Chuanxun; Zhou, Yuanyuan; Zhang, Shoutao; Li, Quan; Wang, Hui; Zhang, Lijun; Miao, Maosheng; Wang, Yanchao; Ma, Yanming: ATLAS: a real-space finite-difference implementation of orbital-free density functional theory (2016)
  4. 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)
  5. van den Burg, Gerrit J.J.; Groenen, Patrick J.F.: GenSVM: a generalized multiclass support vector machine (2016)
  6. 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)
  7. Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)
  8. Di Napoli, Edoardo; Fabregat-Traver, Diego; Quintana-Ortí, Gregorio; Bientinesi, Paolo: Towards an efficient use of the BLAS library for multilinear tensor contractions (2014)
  9. Ho, Kenneth L.; Greengard, Leslie: A fast semidirect least squares algorithm for hierarchically block separable matrices (2014)
  10. Ivanenko, P.A.; Doroshenko, A.Yu.: Method of automated generation of autotuners for parallel programs (2014) ioport
  11. Marco-Buzunariz, Miguel Angel: A framework for free, finitely presented and braid groups in Sage (2014)
  12. Parikh, Neal; Boyd, Stephen: Block splitting for distributed optimization (2014)
  13. 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)
  14. 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)
  15. Abed, Khalid H.; Morris, Gerald R.: Improving performance of codes with large/irregular stride memory access patterns via high performance reconfigurable computers (2013) ioport
  16. Bouchard-C^oté, Alexandre: A note on probabilistic models over strings: the linear algebra approach (2013)
  17. Castaldo, Anthony M.; Whaley, R.Clint; Samuel, Siju: Scaling LAPACK panel operations using parallel cache assignment (2013)
  18. Kouya, Tomonori: Performance evaluation of multiple and mixed precision iterative refinement method and its application to high-order implicit Runge-Kutta method (2013)
  19. 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)
  20. Vannieuwenhoven, Nick; Meerbergen, Karl: IMF: an incomplete multifrontal $LU$-factorization for element-structured sparse linear systems (2013)

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