A Comparison Study of Static Mapping Heuristics for a Class of Meta-tasks on Heterogeneous Computing Systems (1999)
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@MISC{Braun99acomparison,
author = {Tracy D. Braun and Howard Jay Siegel and Noah Beck and Ladislau L. Bölöni and Muthucumaru Maheswaran and Albert I. Reuther and James P. Robertson and Mitchell D. Theys and Bin Yao and Debra Hensgen and Richard F. Freund},
title = {A Comparison Study of Static Mapping Heuristics for a Class of Meta-tasks on Heterogeneous Computing Systems},
year = {1999}
}
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Abstract
Heterogeneous computing (HC) environments are well suited to meet the computational demands of large, diverse groups of tasks (i.e., a meta-task). The problem of mapping (defined as matching and scheduling) these tasks onto the machines of an HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original studies of each heuristic. Therefore, a collection of eleven heuristics from the literature has been selected, implemented, and analyzed under one set of common assumptions. The eleven heuristics examined are Opportunistic Load Balancing, User-Directed Assignment, Fast Greedy, Min-min, Max-min, Greedy, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, Tabu, and A*. This study provides one even basis for comparison and insights into c...
