Auto-tuning Dense Vector and Matrix-vector Operations for Fermi GPUs

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    Abstract

    In this paper, we consider the automatic performance tuning of dense vector and matrix-vector operations on GPUs. Such operations form the backbone of level 1 and level 2 routines in the Basic Linear Algebra Subroutines (BLAS) library and are therefore of great importance in many scientific applications. As examples, we develop single-precision CUDA kernels for the Euclidian norm (SNRM2) and the matrix-vector multiplication (SGEMV). The target hardware is the most recent Nvidia Tesla 20-series (Fermi architecture). We show that auto-tuning can be successfully applied to achieve high performance for dense vector and matrix-vector operations by appropriately utilizing the fine-grained parallelism of the GPU. Our tuned kernels display between 25-100% better performance than the current CUBLAS 3.2 library.
    Original languageEnglish
    Title of host publicationParallel Processing and Applied Mathematics : 9th International Conference, PPAM 2011
    EditorsRoman Wyrzykowski, Jack Dongarra, Konrad Karczewski, Jerzy Wasniewski
    PublisherSpringer
    Publication date2012
    Pages619-629
    DOIs
    Publication statusPublished - 2012
    EventParallel Processing and Applied Mathematics. 9th International Conference, PPAM 2011 - Torun, Poland
    Duration: 11 Sep 201114 Sep 2011
    http://ppam.pl/

    Conference

    ConferenceParallel Processing and Applied Mathematics. 9th International Conference, PPAM 2011
    CountryPoland
    CityTorun
    Period11/09/201114/09/2011
    Internet address
    SeriesLecture Notes in Computer Science
    Volume7203
    ISSN0302-9743

    Keywords

    • GPU
    • BLAS
    • Dense linear algebra
    • Parallel algorithms

    Cite this

    Sørensen, H. H. B. (2012). Auto-tuning Dense Vector and Matrix-vector Operations for Fermi GPUs. In R. Wyrzykowski, J. Dongarra, K. Karczewski, & J. Wasniewski (Eds.), Parallel Processing and Applied Mathematics: 9th International Conference, PPAM 2011 (pp. 619-629). Springer. Lecture Notes in Computer Science, Vol.. 7203 https://doi.org/10.1007/978-3-642-31464-3_63