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).

在高性能计算领域，众所周知，保留少量未使用的 CPU 内核是有益的，这种做法称为订阅不足。然而，在高性能计算的科学应用中很少实现订阅不足的情况。我们通过 1844 个基准的汇总结果证明了计算流体动力学模拟中校准订阅不足的重要性。这些基准测试测量了三种硬件配置和五种不同的 CFD 模型。平均而言，性能提高了 14%（按节点数加权）。在节点数较多时，性能改进最为显着，尤其是在接近负可扩展性状态时。我们发现订阅不足可以将最大性能提高高达 50%；随着节点数量的减少，这一优势逐渐减弱，但单个节点的优势仍高达 13%。在某些情况下，可以通过大量空闲核心（在一种情况下接近一半的核心）来实现最大性能。根据我们的数据集进行回归，我们普遍预测空闲核心的最佳数量作为每个核心单元的函数。此回归平均速度提高了 15%（再次按节点数加权）。