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The allocation of jobs to nodes and cores in industrial clusters is often based on queue-system standard settings, guesses or perceived fairness between different users and projects. Unfortunately, hard empirical data is often lacking and jobs are scheduled and co-scheduled for no apparent reason. In this case-study, we evaluate the performance impact of coscheduling jobs using three types of applications and an existing 450+ node cluster at a company doing large-scale parallel industrial simulations. We measure the speedup when co-scheduling two applications together, sharing two nodes, compared to running the applications on separate nodes. Our results and analyses show that by enabling coscheduling we improve performance in the order of 20% both in throughput and in execution times, and improve the execution times even more if the cluster is running with low utilization. We also find that a simple reconfiguration of the number of threads used in one of the applications can lead to a performance increase of 35-48% showing that there is a potentially large performance increase to gain by changing current practice in industry.

Node Sharing for Increased Throughput and Shorter Runtimes - an Industrial Co-Scheduling Case Study