IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3720450.html
   My bibliography  Save this article

A Heterogeneous Parallel LU Factorization Algorithm Based on a Basic Column Block Uniform Allocation Strategy

Author

Listed:
  • Rongteng Wu
  • Xiaohong Xie

Abstract

Most supercomputers are shipped with both a CPU and a GPU. With the powerful parallel computing capability of GPUs, heterogeneous computing architecture produces new challenges for system software development and application design. Because of the significantly different architectures and programming models of CPUs and GPUs, conventional optimization techniques for CPUs may not work well in a heterogeneous multi-CPU and multi-GPU system. We present a heterogeneous parallel LU factorization algorithm for heterogeneous architectures. According to the different performances of the processors in the system, any given matrix is partitioned into different sizes of basic column blocks. Then, a static task allocation strategy is used to distribute the basic column blocks to corresponding processors uniformly. The idle time is minimized by optimized sizes and the number of basic column blocks. Right-looking ahead technology is also used in systems configured with one CPU core to one GPU to decrease the wait time. Experiments are conducted to test the performance of synchronization and load balancing, communication cost, and scalability of the heterogeneous parallel LU factorization in different systems and compare it with the related matrix algebra algorithm on a heterogeneous system configured with multiple GPUs and CPUs.

Suggested Citation

  • Rongteng Wu & Xiaohong Xie, 2019. "A Heterogeneous Parallel LU Factorization Algorithm Based on a Basic Column Block Uniform Allocation Strategy," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, February.
    • Handle: RePEc:hin:jnlmpe:3720450
    DOI: 10.1155/2019/3720450
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2019/3720450.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2019/3720450.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/3720450?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:3720450. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.