Statistical measures for quantifying task and machine heterogeneities

  • Authors:

  • Abdulla M. Al-Qawasmeh;Anthony A. Maciejewski;Haonan Wang;Jay Smith;Howard Jay Siegel;Jerry Potter

  • Affiliations:

  • Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, USA;Department of Statistics, Colorado State University, Fort Collins, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, USA and DigitalGlobe Inc., Longmont, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, USA and Department of Computer Science, Colorado State University, Fort Collins, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, USA

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2011

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Abstract

We study heterogeneous computing (HC) systems that consist of a set of different machines that have varying capabilities. These machines are used to execute a set of heterogeneous tasks that vary in their computational complexity. Finding the optimal mapping of tasks to machines in an HC system has been shown to be, in general, an NP-complete problem. Therefore, heuristics have been used to find near-optimal mappings. The performance of allocation heuristics can be affected significantly by factors such as task and machine heterogeneities. In this paper, we identify different statistical measures used to quantify the heterogeneity of HC systems, and show the correlation between the performance of the heuristics and these measures through simple mapping examples and synthetic data analysis. In addition, we illustrate how regression trees can be used to predict the most appropriate heuristic for an HC system based on its heterogeneity.