Adaptive scheduling of parallel computations for SPMD tasks

Authors:
Mikhail Panshenskov;Alexander Vakhitov
Affiliations:
Saint Petersburg State University, Department of Mathematics and Mechanics, Saint Petersburg, Russia;Saint Petersburg State University, Department of Mathematics and Mechanics, Saint Petersburg, Russia
Venue:
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
Year:
2007

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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.