Combining phase identification and statistic modeling for automated parallel benchmark generation

Ye Jin, Mingliang Liu, Xiaosong Ma, Qing Liu, Jeremy Logan, Norbert Podhorszki, Jong Youl Choi, Scott Klasky

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

1 Citation (Scopus)

Abstract

Parallel application benchmarks are indispensable for evaluating/optimizing HPC software and hardware. However, it is very challenging and costly to obtain high-fidelity benchmarks reflecting the scale and complexity of state-of-the-art parallel applications. Hand-extracted synthetic benchmarks are time- and labor-intensive to create. Real applications themselves, while offering most accurate performance evaluation, are expensive to compile, port, reconfigure, and often plainly inaccessible due to security or ownership concerns. This work contributes APPRIME, a novel tool for trace-based automatic parallel benchmark generation. Taking as input standard communication-I/O traces of an application's execution, it couples accurate automatic phase identification with statistical regeneration of event parameters to create compact, portable, and to some degree reconfigurable parallel application benchmarks. Experiments with four NAS Parallel Benchmarks (NPB) and three real scientific simulation codes confirm the fidelity of APPRIME benchmarks. They retain the original applications' performance characteristics, in particular the relative performance across platforms.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP
PublisherAssociation for Computing Machinery
Pages269-270
Number of pages2
Volume2015-January
ISBN (Print)9781450332057
DOIs
Publication statusPublished - 24 Jan 2015
Event20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2015 - San Francisco, United States
Duration: 7 Feb 201511 Feb 2015

Other

Other20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2015
CountryUnited States
CitySan Francisco
Period7/2/1511/2/15

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Keywords

  • Automatic benchmark generation
  • HPC applications
  • Phase identification
  • Statistical profiling
  • Trace

ASJC Scopus subject areas

  • Software

Cite this

Jin, Y., Liu, M., Ma, X., Liu, Q., Logan, J., Podhorszki, N., Choi, J. Y., & Klasky, S. (2015). Combining phase identification and statistic modeling for automated parallel benchmark generation. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP (Vol. 2015-January, pp. 269-270). Association for Computing Machinery. https://doi.org/10.1145/2688500.2688541