NDLI: Performance Modeling and Comparative Analysis of the MILC Lattice QCD Application su3_rmd

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Publisher's Information

Performance Modeling and Comparative Analysis of the MILC Lattice QCD Application su3_rmd

Content Provider	ACM Digital Library
Author	Bauer, Greg Gottlieb, Steven Hoefler, Torsten
Abstract	Application performance modeling is an essential part of application and system development as HPC moves into the petascale and prepares for the exascale. However, performance modeling of parallel systems is a difficult task due to natural variations in measurements and noise effects. In this paper, we give a detailed example for a semi-analytical performance-modeling method applied to the ubiquitous HPC application su3 rmd from the lattice Quantum Chromo dynamics field on a variety of parallel computing platforms. We apply statistical techniques that are well known in natural sciences to model the variance in the input system. Using a simple analytical model to capture the main characteristics of the code, such as numbers and sizes of passed messages and invocation counts of serial code blocks in conjunction with statistically sound curve fitting methods, we develop an accurate performance model and use it to characterize application performance on various target architectures. Our fitting techniques allow us to characterize the variance of different performance observations on a given system and show the influence of noise from different sources. The techniques we developed can be applied to a wide class of bulk-synchronous applications. With this detailed example, we aim to motivate the scientific computing community to develop and use similar performance models for software development and maintenance.
Starting Page	652
Ending Page	659
Page Count	8
File Format	PDF
ISBN	9780769546919
DOI	10.1109/CCGrid.2012.123
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-05-13
Access Restriction	Subscribed
Subject Keyword	Performance analysis milc semi-analytical modeling
Content Type	Text
Resource Type	Article

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