Abstract
A scheduling algorithm is proposed for large-scale, heterogeneous distributed systems working on SPMD tasks with homogeneous input. The new algorithm is based on stochastic optimization using a modified least squares method for the identification of communication and performance parameters. The model of computation involves a server distributing tasks to clients. The goal of the optimization is to reduce execution time by the clients. The costs of getting the task from the server, execution of the task and sending the results back are estimated; and the scheduling is based on adaptive division of work (input for the clients) into blocks. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Panshenskov, M., & Vakhitov, A. (2007). Adaptive scheduling of parallel computations for SPMD tasks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4706 LNCS, pp. 38–50). Springer Verlag. https://doi.org/10.1007/978-3-540-74477-1_4
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