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

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Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation

Content Provider	ACM Digital Library
Author	Choi, Jong Youl Jin, Ye Ma, Xiaosong Liu, Qing Klasky, Scott Liu, Mingliang Logan, Jeremy Podhorszki, Norbert
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 their relative performance across platforms. Also, the result benchmarks, already released online, are much more compact and easy-to-port compared to the original applications.
Starting Page	309
Ending Page	320
Page Count	12
File Format	PDF
ISBN	9781450334860
DOI	10.1145/2745844.2745876
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Phase identification Traces Asynchronous i/o Markov chain model Benchmark generation Hpc applications
Content Type	Text
Resource Type	Article

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