Improving parallel I/O autotuning with performance modeling

Babak Behzad, Surendra Byna, Stefan M. Wild, Prabhat, Marc Snir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Various layers of the parallel I/O subsystem offer tunable parameters for improving I/O performance on large-scale computers. However, searching through a large parameter space is challenging. We are working towards an autotuning framework for determining the parallel I/O parameters that can achieve good I/O performance for different data write patterns. In this paper, we characterize parallel I/O and discuss the development of predictive models for use in effectively reducing the parameter space. Applying our technique on tuning an I/O kernel derived from a large-scale simulation code shows that the search time can be reduced from 12 hours to 2 hours, while achieving 54X I/O performance speedup.

Original languageEnglish (US)
Title of host publicationHPDC 2014 - Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing
PublisherAssociation for Computing Machinery
Pages253-256
Number of pages4
ISBN (Print)9781450327480
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event23rd ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2014 - Vancouver, BC, Canada
Duration: Jun 23 2014Jun 27 2014

Publication series

NameHPDC 2014 - Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing

Other

Other23rd ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2014
CountryCanada
CityVancouver, BC
Period6/23/146/27/14

Keywords

  • Autotuning
  • Parallel I/O
  • Performance modeling
  • Performance optimization

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Improving parallel I/O autotuning with performance modeling'. Together they form a unique fingerprint.

Cite this