A Study on the Influence of Caching: Sequences of Dense Linear Algebra Kernels

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8969)

Abstract

It is universally known that caching is critical to attain high-performance implementations: In many situations, data locality (in space and time) plays a bigger role than optimizing the (number of) arithmetic floating point operations. In this paper, we show evidence that at least for linear algebra algorithms, caching is also a crucial factor for accurate performance modeling and performance prediction.

References

  1. 1.
    Peise, E., Bientinesi, P.: Performance modeling for dense linear algebra. In: Proceedings of the 3rd International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS12), November 2012Google Scholar
  2. 2.
    Whaley, R.: Empirically tuning lapack’s blocking factor for increased performance. In: 2008 International Multiconference on Computer Science and Information Technology, IMCSIT 2008, pp. 303–310, October 2008Google Scholar
  3. 3.
    Lam, M.D., Rothberg, E.E., Wolf, M.E.: The cache performance and optimizations of blocked algorithms. In: Proceedings of the Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS IV, pp. 63–74. ACM, New York (1991)Google Scholar
  4. 4.
    Iakymchuk, R., Bientinesi, P.: Modeling performance through memory-stalls. ACM SIGMETRICS Perform. Eval. Rev. 40(2), 86–91 (2012)CrossRefGoogle Scholar
  5. 5.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.AICES, RWTH AachenAachenGermany

Personalised recommendations