Delivering Faster Results Through Parallelisation and GPU Acceleration

Matthew Newall, Violeta Holmes, Colin Venters, Paul Lunn

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

The rate of scientific discovery depends on the speed at which accurate results and analysis can be obtained. The use of parallel co-processors such as Graphical Processing Units (GPUs) is becoming more and more important in meeting this demand as improvements in serial data processing speed become increasingly difficult to sustain. However, parallel data processing requires more complex programming compared to serial processing. Here we present our methods for parallelising two pieces of scientific software, leveraging multiple GPUs to achieve up to thirty times speed up.
Original languageEnglish
Title of host publicationIntelligent Systems in Science and Information 2014
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
Place of PublicationSwitzerland
PublisherSpringer
Pages309-320
Number of pages12
Volume591
ISBN (Electronic)978-3-319-14654-6
ISBN (Print)978-3-319-14653-9
DOIs
Publication statusPublished - 14 Feb 2015

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Cham

Keywords

  • GPU
  • CUDA
  • GPU cluster
  • Parallelisation

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  • Cite this

    Newall, M., Holmes, V., Venters, C., & Lunn, P. (2015). Delivering Faster Results Through Parallelisation and GPU Acceleration. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems in Science and Information 2014 (Vol. 591, pp. 309-320). (Studies in Computational Intelligence). Switzerland: Springer. https://doi.org/10.1007/978-3-319-14654-6_19