Hogs and slackers: Using operations balance in a genetic algorithm to optimize sparse algebra computation on distributed architectures

Una May O'Reilly, Eric Robinson, Sanjeev Mohindra, Julie Mullen, Nadya Bliss

Research output: Contribution to journalArticlepeer-review

Abstract

We present a framework for optimizing the distributed performance of sparse matrix computations. These computations are optimally parallelized by distributing their operations across processors in a subtly uneven balance. Because the optimal balance point depends on the non-zero patterns in the data, the algorithm, and the underlying hardware architecture, it is difficult to determine. The Hogs and Slackers genetic algorithm (GA) identifies processors with many operations - hogs, and processors with few operations - slackers. Its intelligent operation-balancing mutation operator swaps data blocks between hogs and slackers to explore new balance points. We show that this operator is integral to the performance of the genetic algorithm and use the framework to conduct an architecture study that varies network specifications. The Hogs and Slackers GA is itself a parallel algorithm with near linear speedup on a large computing cluster.

Original languageEnglish (US)
Pages (from-to)635-644
Number of pages10
JournalParallel Computing
Volume36
Issue number10-11
DOIs
StatePublished - Oct 2010
Externally publishedYes

Keywords

  • Genetic algorithm
  • Simulation-based optimization
  • Sparse matrix

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence

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