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 197 articles , 1 standard article )

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  1. Cai, Zhenning; Fan, Yuwei; Wang, Yanli: Burnett spectral method for the spatially homogeneous Boltzmann equation (2020)
  2. Jonasson, Kristjan; Sigurdsson, Sven; Yngvason, Hordur Freyr; Ragnarsson, Petur Orri; Melsted, Pall: Algorithm 1005: Fortran subroutines for reverse mode algorithmic differentiation of BLAS matrix operations (2020)
  3. Van Zee, Field G.: Implementing high-performance complex matrix multiplication via the 1M method (2020)
  4. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Tomov, Stanimire; Yamazaki, Ichitaro: The singular value decomposition: anatomy of optimizing an algorithm for extreme scale (2018)
  5. Barchini, L.; Zierau, R.: Characteristic cycles of highest weight Harish-Chandra modules for (\mathrmSp(2n,\mathbbR)) (2017)
  6. Kelefouras, Vasilios: A methodology pruning the search space of six compiler transformations by addressing them together as one problem and by exploiting the hardware architecture details (2017)
  7. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the Lighthouse framework (2016)
  8. Low, Tze Meng; Igual, Francisco D.; Smith, Tyler M.; Quintana-Orti, Enrique S.: Analytical modeling is enough for high-performance BLIS (2016)
  9. Martinsson, Per-Gunnar; Voronin, Sergey: A randomized blocked algorithm for efficiently computing rank-revealing factorizations of matrices (2016)
  10. 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)
  11. 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)
  12. van den Burg, Gerrit J. J.; Groenen, Patrick J. F.: GenSVM: a generalized multiclass support vector machine (2016)
  13. Nelson, Thomas; Belter, Geoffrey; Siek, Jeremy G.; Jessup, Elizabeth; Norris, Boyana: Reliable generation of high-performance matrix algebra (2015)
  14. Van Zee, Field G.; van de Geijn, Robert A.: BLIS: a framework for rapidly instantiating BLAS functionality (2015)
  15. 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)
  16. Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)
  17. Di Napoli, Edoardo; Fabregat-Traver, Diego; Quintana-Ortí, Gregorio; Bientinesi, Paolo: Towards an efficient use of the BLAS library for multilinear tensor contractions (2014)
  18. Ho, Kenneth L.; Greengard, Leslie: A fast semidirect least squares algorithm for hierarchically block separable matrices (2014)
  19. Ivanenko, P. A.; Doroshenko, A. Yu.: Method of automated generation of autotuners for parallel programs (2014) ioport
  20. Kirby, Robert C.: High-performance evaluation of finite element variational forms via commuting diagrams and duality (2014)

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