Extending Skel to Support the Development and Optimization of Next Generation I/O Systems

Jeremy Logan, Jong Youl Choi, Matthew Wolf, George Ostrouchov, Lipeng Wan, Norbert Podhorszki, William Godoy, Scott Klasky, Erich Lohrmann, Greg Eisenhauer, Chad Wood, Kevin Huck

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

6 Scopus citations

Abstract

As the memory and storage hierarchy get deeper and more complex, it is important to have new benchmarks and evaluation tools that allow us to explore the emerging middleware solutions to use this hierarchy. Skel is a tool aimed at automating and refining this process of studying HPC I/O performance. It works by generating application I/O kernel/benchmarks as determined by a domain-specific model. This paper provides some techniques for extending Skel to address new situations and to answer new research questions. For example, we document use cases as diverse as using Skel to troubleshoot I/O performance issues for remote users, refining an I/O system model, and facilitating the development and testing of a mechanism for runtime monitoring and performance analytics. We also discuss data oriented extensions to Skel to support the study of compression techniques for Exascale scientific data management.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages563-571
Number of pages9
ISBN (Electronic)9781538623268
DOIs
StatePublished - Sep 22 2017
Event2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 - Honolulu, United States
Duration: Sep 5 2017Sep 8 2017

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2017-September
ISSN (Print)1552-5244

Conference

Conference2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
Country/TerritoryUnited States
CityHonolulu
Period09/5/1709/8/17

Funding

Support was also provided by the National Science Foundation under Grant Number 1265403. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Program Manager Dr. Lucy Nowell, under Award Number DE-SC0012537. Also, this research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility.

Keywords

  • Adios
  • Data Compression
  • Generative Programming
  • High Performance I/O
  • I/O benchmarking
  • I/O performance
  • Mini Applications
  • Runtime performance analytics
  • Runtime performance monitoring
  • Scientific Data Management
  • Skel

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