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Towards Dynamic Autotuning of SpMV in CUSP Library

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F24%3A00137324" target="_blank" >RIV/00216224:14610/24:00137324 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/IPDPSW63119.2024.00012" target="_blank" >http://dx.doi.org/10.1109/IPDPSW63119.2024.00012</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/IPDPSW63119.2024.00012" target="_blank" >10.1109/IPDPSW63119.2024.00012</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Towards Dynamic Autotuning of SpMV in CUSP Library

  • Original language description

    Sparse matrix-vector product (SpMV) is a central operation in many iterative methods for solving linear systems and, as such, is an attractive candidate for acceleration on the GPU. However, the performance of the SpMV kernel can vary depending both on the target architecture as well as on the sparsity pattern of the matrix. Thus, to achieve optimal performance, the implementation might need to be adjusted for each particular matrix and architecture. This can be achieved through dynamic autotuning, a method that can optimize a source code during program runtime. In this paper, we present a dynamic autotuning of SpMV kernel included in a production-quality CUSP library. We identify and implement tuning parameters and use the Kernel Tuning Toolkit framework for autotuning of SpMV working with the DIA and ELL sparse matrix formats. The dynamic autotuning integration is fully transparent to the library users - it can be activated just by re-compiling software using our tunable version of the CUSP. The proposed autotuned library is evaluated by comparing it with the original CUSP kernels on a set of representative matrices and by examining the contribution of autotuning. The results show that the autotuned kernels can reach up to about 16.9 × speedup compared to a fixed implementation.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LM2023054" target="_blank" >LM2023054: e-Infrastructure CZ</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2024

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

  • ISBN

    9798350364613

  • ISSN

  • e-ISSN

  • Number of pages

    9

  • Pages from-to

    14-22

  • Publisher name

    IEEE

  • Place of publication

    San Francisco, USA

  • Event location

    Mauritius

  • Event date

    Jan 1, 2024

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001284697300084