Using pattern-models to guide SSD deployment for Big Data applications in HPC systems

Junjie Chen, Philip C. Roth, Yong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Flash-memory based Solid State Drives (SSDs) embrace higher performance and lower power consumption compared to traditional storage devices (HDDs). These benefits are needed in HPC systems, especially with the growing demand of supporting Big Data applications. In this paper, we study placement and deployment strategies of SSDs in HPC systems to maximize the performance improvement, given a practical fixed hardware budget constraint. We propose a pattern-model approach to guide SSD deployment for HPC systems through two steps; characterizing workload and mapping deployment strategy. The first step is responsible for characterizing the access patterns of the workload and the second step contributes the actual deployment recommendation for Parallel File System (PFS) configuration combining with an analytical model. We have carried out initial experimental tests and the results confirmed that the proposed approach can guide placement of SSDs in HPC systems for accelerating data accesses. Our research will be helpful in guiding designs and developments for Big Data applications in current and projected HPC systems including exascale systems.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
PublisherIEEE Computer Society
Pages332-337
Number of pages6
ISBN (Print)9781479912926
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: Oct 6 2013Oct 9 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Conference

Conference2013 IEEE International Conference on Big Data, Big Data 2013
Country/TerritoryUnited States
CitySanta Clara, CA
Period10/6/1310/9/13

Keywords

  • Big Data
  • Exascale Systems
  • High Performance Computing
  • Hybrid Storage Systems
  • Solid State Drives

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