When Fewer Cores Is Faster: A Parametric Study of Undersubscription in High-Performance Computing.

In the world of high-performance computing, it is known that it can be beneficial to leave a small number of CPU cores unused, a practice termed undersubscription. However, undersubscription is rarely implemented in scientific applications of high-performance computing. We demonstrate the importance of calibrated undersubscription in Computational Fluid Dynamics simulations through the aggregated results of 1844 benchmarks. These benchmarks measured three hardware configurations and five different CFD models. On average, performance increased by 14% (weighted by node count). Performance improvements were most significant at large node counts, particularly when nearing a regime of negative scalability. We found that undersubscription could increase maximum performance by up to 50%; this advantage diminished as node count decreased but remained as much as 13% with a single node. In some cases, maximum performance was achieved with large numbers of free cores—nearly half of the cores in one case. Producing a regression from our dataset, we universally predict the optimal number of free cores as a function of cells per core. This regression achieves a 15% speed increase on average (again weighted by node count). [ABSTRACT FROM AUTHOR]