Enabling fair pricing on HPC systems with node sharing
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Applications that are co-scheduled on a multicore compete for shared resources, such as cache capacity and memory bandwidth. The performance degradation resulting from this contention can be substantial, which makes it important to effectively manage these shared resources. This, however, requires insight into how applications are impacted by such contention. In this paper we present a quantitative method to measure applications' sensitivities to different degrees of contention for off-chip memory bandwidth on real hardware. This method is then used to demonstrate the varying contention sensitivity across a selection of benchmarks, and explains why some of them experience substantial slowdowns long before the overall memory bandwidth saturates.