Performance Modeling of HPC Applications on Overcommitted Systems

Recently, the use of interactive jobs in addition to traditional batch jobs are getting common in supercomputer systems. We expect overcommitting scheduling, in which multiple HPC jobs share one computational resource, to accept them in a single system and to make effective use of resources. On the other hand, the performance of a job must be affected when it is in an overcommitted state. Therefore, we need to investigate the impact of the overcommitted state. We evaluated the performance change when a single computational resource was shared by multiple HPC jobs, and built a model to predict the performance change using performance counters as input variables. The model could predict the performance in the overcommitted state with good accuracy, and the fatal mispredictions occupied only 1.4% of the total cases.