Statistical measures for quantifying task and machine heterogeneities

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...

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Bibliographic Details
Published in:The Journal of supercomputing 2011-07, Vol.57 (1), p.34-50
Main Authors: Al-Qawasmeh, Abdulla M., Maciejewski, Anthony A., Wang, Haonan, Smith, Jay, Siegel, Howard Jay, Potter, Jerry
Format: Article
Language:English
Subjects:
Allocations
Compilers
Computation
Computer Science
Heterogeneity
Heuristic
Interpreters
Mapping
Optimization
Processor Architectures
Programming Languages
Regression
Tasks
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-011-0572-x