A genetic algorithms approach to modeling the performance of memory-bound computations

  • Authors:
  • Mustafa M Tikir;Laura Carrington;Erich Strohmaier;Allan Snavely

  • Affiliations:
  • San Diego Supercomputer Center, La Jolla, CA;San Diego Supercomputer Center, La Jolla, CA;Lawrence Berkeley National Laboratory, One Cyclotron Road, CA;San Diego Supercomputer Center, La Jolla, CA

  • Venue:
  • Proceedings of the 2007 ACM/IEEE conference on Supercomputing
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.