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Heterogeneous Computing Workshop (1999)
San Juan, Puerto Rico
Apr. 12, 1999 to Apr. 12, 1999
ISSN: 1097-5209
ISBN: 0-7695-0107-9
pp: 15
Tracy D. Braun , Purdue University
Howard Jay Siegel , Purdue University
Noah Beck , Purdue University
Ladislau L. Bölóni , Purdue University
Albert I. Reuther , Purdue University
Mitchell D. Theys , Purdue University
Bin Yao , Purdue University
Richard F. Freund , Purdue University
Muthucumaru Maheswaran , University of Manitoba
Debra Hensgen , Naval Postgraduate School
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 (de_ned 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 circumstances where one technique will outperform another. The evaluation procedure is specified, the heuristics are defined, and then selected results are compared.

L. L. Bölóni et al., "A Comparison Study of Static Mapping Heuristics for a Class of Meta-Tasks on Heterogeneous Computing Systems," Heterogeneous Computing Workshop(HCW), San Juan, Puerto Rico, 1999, pp. 15.
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