An Evaluation of Parallel Job Scheduling for ASCI Blue-Pacific

Abstract

In this paper we analyze the behavior of a gang-scheduling system that we are developing for the ASCI Blue-Pacific machines. Starting with a real workload obtained from job logs of one of the ASCI machines, we generate a statistical model of this workload using Hyper Erlang distributions. We then vary the parameters of those distributions to generate various workloads, representative of different operating points of the machine. Through simulation we obtain performance characteristics for three different scheduling strategies: (i) first-come first-serve, (ii) gang-scheduling, and (iii) backfilling. Our results show that both backfilling and gang-scheduling with moderate multiprogramming levels are much more effective than simple first-come first-serve scheduling. In addition, we show that gang-scheduling can display better performance characteristics than backfilling, particularly for large production jobs.