Exploring Memory Hierarchy to Improve Scientific Data Read Performance

Wenzhao Zhang, Houjun Tang, Xiaocheng Zou, Steven Harenberg, Qing Liu, Scott Klasky, Nagiza F. Samatova

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

7 Scopus citations


Improving read performance is one of the major challenges with speeding up scientific data analytic applications. Utilizing the memory hierarchy is one major line of researches to address the read performance bottleneck. Related methods usually combine solide-state-drives(SSDs) with dynamic random-access memory(DRAM) and/or parallel file system(PFS) to mitigate the speed and space gap between DRAM and PFS. However, these methods are unable to handle key performance issues plaguing SSDs, namely read contention that may cause up to 50% performance reduction. In this paper, we propose a framework that exploits the memory hierarchy resource to address the read contention issues involved with SSDs. The framework employs a general purpose online read algorithm that able to detect and utilize memory hierarchy resource to relieve the problem. To maintain a near optimal operating environment for SSDs, the framework is able to orchastrate data chunks across different memory layers to facilitate the read algorithm. Compared to existing tools, our framework achieves up to 50% read performance improvement when tested on datasets from real-world scientific simulations.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781467365987
StatePublished - Oct 26 2015
Externally publishedYes
EventIEEE International Conference on Cluster Computing, CLUSTER 2015 - Chicago, United States
Duration: Sep 8 2015Sep 11 2015

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)1552-5244


OtherIEEE International Conference on Cluster Computing, CLUSTER 2015
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Signal Processing


  • Memory hierarchy
  • Read contention
  • SSD
  • Scientific data


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