LACIO: A new collective I/O strategy for parallel I/O systems

Yong Chen, Xian He Sun, Rajeev Thakur, Philip C. Roth, William D. Gropp

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

30 Scopus citations

Abstract

Parallel applications benefit considerably from the rapid advance of processor architectures and the available massive computational capability, but their performance suffers from large latency of I/O accesses. The poor I/O performance has been attributed as a critical cause of the low sustained performance of parallel systems. Collective I/O is widely considered a critical solution that exploits the correlation among I/O accesses from multiple processes of a parallel application and optimizes the I/O performance. However, the conventional collective I/O strategy makes the optimization decision based on the logical file layout to avoid multiple file system calls and does not take the physical data layout into consideration. On the other hand, the physical data layout in fact decides the actual I/O access locality and concurrency. In this study, we propose a new collective I/O strategy that is aware of the underlying physical data layout. We confirm that the new Layout-Aware Collective I/O (LACIO) improves the performance of current parallel I/O systems effectively with the help of noncontiguous file system calls. It holds promise in improving the I/O performance for parallel systems.

Original languageEnglish
Title of host publicationProceedings - 25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011
Pages794-804
Number of pages11
DOIs
StatePublished - 2011
Event25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011 - Anchorage, AK, United States
Duration: May 16 2011May 20 2011

Publication series

NameProceedings - 25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011

Conference

Conference25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011
CountryUnited States
CityAnchorage, AK
Period05/16/1105/20/11

Keywords

  • Collective I/O
  • Data Intensive Computing
  • High Performance Computing
  • Parallel Applications
  • Parallel File Systems
  • Parallel I/O
  • Storage Systems

Cite this