IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3580-d930707.html
   My bibliography  Save this article

Estimating the Performance of Computing Clusters without Accelerators Based on TOP500 Results

Author

Listed:
  • Vladimir O. Rybintsev

    (Moscow Power Engineering Institute, National Research University, 111250 Moscow, Russia)

Abstract

Based on an analysis of TOP500 results, a functional dependence of the performance of clusters without accelerators according to the Linpack benchmark on their parameters was determined. The comparison of calculated and tested results showed that the estimation error does not exceed 2% for processors of different generations and manufacturers (Intel, AMD, Fujitsu) with different technologies of a system interconnect. The achieved accuracy of the calculation allows successful prediction of the performance of a cluster when its parameters (node performance, number of nodes, number of network interfaces, network technology, remote direct memory access, or remote direct memory access over converged Ethernet mode) are changed without resorting to a complex procedure of real testing.

Suggested Citation

  • Vladimir O. Rybintsev, 2022. "Estimating the Performance of Computing Clusters without Accelerators Based on TOP500 Results," Mathematics, MDPI, vol. 10(19), pages 1-8, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3580-:d:930707
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3580/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/19/3580/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3580-:d:930707. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.