Model-based MPI-IO tuning with Periscope tuning framework

Weifeng Liu, Michael Gerndt, Bin Gong

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Summary For many parallel applications, I/O performance is a major bottleneck. MPI-IO, defined by the MPI forum, can help parallel applications overcome the performance and portability limitations of existing parallel I/O interfaces. Although autotuning has been used to improve the performance of computing kernels, MPI-IO autotuning has rarely been studied. To automate MPI-IO performance tuning, we designed and implemented an automatic tuner. The tuner relies on the Periscope tuning framework for transparently passing hints to the MPI-IO library and for automatically collecting performance data. Unlike computational code, each MPI-IO function takes a relatively long time to complete. Thus, exhaustively searching through the entire parameter space is impractical. So we developed a performance model that can direct us to shorten the tuning time.

Original languageEnglish
Pages (from-to)3-20
Number of pages18
JournalConcurrency and Computation: Practice and Experience
Volume28
Issue number1
DOIs
StatePublished - 1 Jan 2016

Keywords

  • MPI-IO
  • automatic tuning
  • high-performance computing
  • parallel I/O
  • performance model

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