Using MPI file caching to improve parallel write performance for large-scale scientific applications

  • Authors:

  • Wei-keng Liao;Avery Ching;Kenin Coloma;Arifa Nisar;Alok Choudhary;Jacqueline Chen;Ramanan Sankaran;Scott Klasky

  • Affiliations:

  • Northwestern University, Evanston, Illinois;Northwestern University, Evanston, Illinois;Northwestern University, Evanston, Illinois;Northwestern University, Evanston, Illinois;Northwestern University, Evanston, Illinois;Sandia National Laboratories, Livermore, California;Oak Ridge National Laboratory, Oak Ridge, Tennessee;Oak Ridge National Laboratory, Oak Ridge, Tennessee

  • Venue:
  • Proceedings of the 2007 ACM/IEEE conference on Supercomputing
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Typical large-scale scientific applications periodically write checkpoint files to save the computational state throughout execution. Existing parallel file systems improve such write-only I/O patterns through the use of client-side file caching and write-behind strategies. In distributed environments where files are rarely accessed by more than one client concurrently, file caching has achieved significant success; however, in parallel applications where multiple clients manipulate a shared file, cache coherence control can serialize I/O. We have designed a thread based caching layer for the MPI I/O library, which adds a portable caching system closer to user applications so more information about the application's I/O patterns is available for better coherence control. We demonstrate the impact of our caching solution on parallel write performance with a comprehensive evaluation that includes a set of widely used I/O benchmarks and production application I/O kernels.