Benefits of Speedup Knowledge in Memory-Constrained Multiprocessor Scheduling

this paper, we investigate the benefit of having speedup knowledge about individual jobs in making such a choice. We find that if memory sizes and speedup characteristics of jobs are uncorrelated, then there may be moderate benefits in having speedup information, but if large-sized jobs tend to have better speedup than small-sized ones, as might occur in real systems, then much more significant benefits can be obtained. We propose scheduling strategies that exploit speedup information to improve performance, and evaluate them under a variety of workloads. 1 Introduction Large-scale multiprocessor scheduling has been studied extensively for several years, but only recently has the issue of memory requirements been taken into consideration. Even with large memory capacities (1--2GB per node) becoming more common, the resource demands of large-scale scientific applications are expected to continue to strain the resources of systems [Ast93,AK