Codelet Scheduling by Genetic Algorithm

Codelet model is a fine-grained, event-driven hybrid parallel model inspired by dataflow, whose performance depends on the scheduling policy. How to design optimal codelet scheduling policy based on the features of tasks is important to the codelet-based system performance. In this paper, we propose an adaptive codelet scheduling policy by combing "pure" genetic algorithm for tasks with complex dependencies. It is verified that the policy is effective based on bunches of experimental results.

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