A Genetic Algorithms Approach to Modeling the Performance of Memory-bound Computations (2007)
| Citations: | 10 - 3 self |
BibTeX
@MISC{Tikir07agenetic,
author = {Mustafa M Tikir and Laura Carrington and Erich Strohmaier and Allan Snavely},
title = { A Genetic Algorithms Approach to Modeling the Performance of Memory-bound Computations},
year = {2007}
}
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Abstract
Benchmarks that measure memory bandwidth, such as STREAM, Apex-MAPS and MultiMAPS, are increasingly popular due to the "Von Neumann" bottleneck of modern processors which causes many calculations to be memory-bound. We present a scheme for predicting the performance of HPC applications based on the results of such benchmarks. A Genetic Algorithm approach is used to "learn" bandwidth as a function of cache hit rates per machine with MultiMAPS as the fitness test. The specific results are 56 individual performance predictions including 3 full-scale parallel applications run on 5 different modern HPC architectures, with various CPU counts and inputs, predicted within 10 % average difference with respect to independently verified runtimes.
