Scheduling for large parallel systems such as clusters and grids presents new challenges due to multiprogramming/polyprocessing [1]. In such systems, several jobs (each consisting of a number of parallel tasks) of multiple users may run at the same time. Processors are allocated to the different jobs either statically or dynamically; further, a processor may be taken away from a task of one job and be reassigned to a task of another job. Thus, the number of processors available to a job varies with time. Although several approaches have been proposed in the past for scheduling tasks on multiprocessors, they assume a dedicated availability of processors. Consequently, the existing scheduling approaches are not suitable for multiprogrammed systems. In this paper, we present a novel probabilistic approach for scheduling parallel tasks on multiprogrammed parallel systems. The key characteristic of the proposed scheme is its self-adaptive nature, i.e., it is responsive to systemic parameters such as number of processors available. Self-adaptation helps achieve better load balance between the different processors and helps reduce the synchronization overhead (number of allocation points). Experimental results show the effectiveness of our technique. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Girkar, M., Kejariwal, A., Tian, X., Saito, H., Nicolau, A., Veidenbaum, A., & Polychronopoulos, C. (2006). Probablistic self-scheduling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4128 LNCS, pp. 253–264). Springer Verlag. https://doi.org/10.1007/11823285_26
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