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Task assignment in heterogeneous computing systems using an effective iterated greedy algorithm
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DOI：10.1016/j.jss.2011.01.051.png)
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Abstract
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A fundamental issue affecting the performance of a parallel application running on a heterogeneous computing system is the assignment of tasks to the processors in the system. The task assignment problem for more than three processors is known to be NP-hard, and therefore satisfactory suboptimal solutions obtainable in an acceptable amount of time are generally sought. This paper proposes a simple and effective iterative greedy algorithm to deal with the problem with goal of minimizing the total sum of execution and communication costs. The main idea in this algorithm is to improve the quality of the assignment in an iterative manner using results from previous iterations. The algorithm first uses a constructive heuristic to find an initial assignment and iteratively improves it in a greedy way. Through simulations over a wide range of parameters, we have demonstrated the effectiveness of our algorithm by comparing it with recent competing task assignment algorithms in the literature. (C) 2011 Elsevier Inc. All rights reserved.
Keywords:
Iterated greedy algorithm
Task assignment
Task interaction graph
Heterogeneous computing
Meta-heuristics
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