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Title: Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation

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.

Authors:
 [1];  [1];  [2];  [1];  [2];  [2];  [2];  [2]
  1. North Carolina State University (NCSU), Raleigh
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1311257
DOE Contract Number:
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Journal Volume: 50; Journal Issue: 8; Conference: PPoPP 2015 Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Francisco, CA, USA, 20150207, 20150211
Country of Publication:
United States
Language:
English

Citation Formats

Jin, Ye, Ma, Xiaosong, Liu, Qing Gary, Liu, Mingliang, Logan, Jeremy S, Podhorszki, Norbert, Choi, Jong Youl, and Klasky, Scott A. Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation. United States: N. p., 2015. Web. doi:10.1145/2858788.2688541.
Jin, Ye, Ma, Xiaosong, Liu, Qing Gary, Liu, Mingliang, Logan, Jeremy S, Podhorszki, Norbert, Choi, Jong Youl, & Klasky, Scott A. Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation. United States. doi:10.1145/2858788.2688541.
Jin, Ye, Ma, Xiaosong, Liu, Qing Gary, Liu, Mingliang, Logan, Jeremy S, Podhorszki, Norbert, Choi, Jong Youl, and Klasky, Scott A. Thu . "Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation". United States. doi:10.1145/2858788.2688541.
@article{osti_1311257,
title = {Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation},
author = {Jin, Ye and Ma, Xiaosong and Liu, Qing Gary and Liu, Mingliang and Logan, Jeremy S and Podhorszki, Norbert and Choi, Jong Youl and Klasky, Scott A},
abstractNote = {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.},
doi = {10.1145/2858788.2688541},
journal = {},
number = 8,
volume = 50,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
Other availability
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