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. Marco-Buzunariz, Miguel Angel: A framework for free, finitely presented and braid groups in Sage (2014)
  2. Parikh, Neal; Boyd, Stephen: Block splitting for distributed optimization (2014)
  3. Sanchez, Eduardo J.; Paolini, Christopher P.; Castillo, Jose E.: The mimetic methods toolkit: an object-oriented API for mimetic finite differences (2014)
  4. 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)
  5. 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)
  6. Abed, Khalid H.; Morris, Gerald R.: Improving performance of codes with large/irregular stride memory access patterns via high performance reconfigurable computers (2013) ioport
  7. Bouchard-Côté, Alexandre: A note on probabilistic models over strings: the linear algebra approach (2013)
  8. Castaldo, Anthony M.; Whaley, R. Clint; Samuel, Siju: Scaling LAPACK panel operations using parallel cache assignment (2013)
  9. Kouya, Tomonori: Performance evaluation of multiple and mixed precision iterative refinement method and its application to high-order implicit Runge-Kutta method (2013)
  10. 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)
  11. Vannieuwenhoven, Nick; Meerbergen, Karl: IMF: an incomplete multifrontal (LU)-factorization for element-structured sparse linear systems (2013)
  12. 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) ioport
  13. Ghysels, P.; Kłosiewicz, P.; Vanroose, W.: Improving the arithmetic intensity of multigrid with the help of polynomial smoothers. (2012)
  14. Hartley, Timothy D. R.; Saule, Erik; Çatalyürek, Ümit V.: Improving performance of adaptive component-based dataflow middleware (2012) ioport
  15. Ho, Kenneth L.; Greengard, Leslie: A fast direct solver for structured linear systems by recursive skeletonization (2012)
  16. Klöckner, Andreas; Pinto, Nicolas; Lee, Yunsup; Catanzaro, Bryan; Ivanov, Paul; Fasih, Ahmed: PyCUDA and PyOpenCL: a scripting-based approach to GPU run-time code generation (2012) ioport
  17. López-Espín, Jose J.; Vidal, Antonio M.; Giménez, Domingo: Two-stage least squares and indirect least squares algorithms for simultaneous equations models (2012)
  18. Lu, Qingda; Gao, Xiaoyang; Krishnamoorthy, Sriram; Baumgartner, Gerald; Ramanujam, J.; Sadayappan, P.: Empirical performance model-driven data layout optimization and library call selection for tensor contraction expressions (2012) ioport
  19. Ozaki, Katsuhisa; Ogita, Takeshi; Oishi, Shin’ichi; Rump, Siegfried M.: Error-free transformations of matrix multiplication by using fast routines of matrix multiplication and its applications (2012)
  20. Pauderis, Colton; Storjohann, Arne: Deterministic unimodularity certification (2012)

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