Performance analysis of job scheduling policies in parallel supercomputing environments

The authors analyze three general classes of scheduling policies under a workload typical of large-scale scientific computing. These policies differ in the manner in which processors are partitioned among the jobs as well as the way in which jobs are prioritized for execution on the partitions. The results indicate that existing static schemes to not perform well under varying workloads. Adaptive policies tend to make better scheduling decisions, but their ability to adjust to workload changes is limited. Dynamic partitioning policies, on the other hand, yield the best performance and can be tuned to provide desired performance differences among jobs with varying resource demands.

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