Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation

Conference · Thu Jan 01 04:00:00 EST 2015

DOI:https://doi.org/10.1145/2858788.2688541· OSTI ID:1311257

Jin, Ye ^[1]; Ma, Xiaosong ^[1]; Liu, Qing Gary ^[2]; Liu, Mingliang ^[1]; Logan, Jeremy S ^[2]; Podhorszki, Norbert ^[2]; Choi, Jong Youl ^[2]; Klasky, Scott A ^[2]

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1. North Carolina State University (NCSU), Raleigh
2. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: DOE Office of Science; USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1311257
• Country of Publication: United States
• Language: English