Multi‐communication layered HPL model and its application to GPU clusters

High‐performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities of computing systems and has been used as a standard to compare the performance of computing systems since the early 1980s. In the initial system‐design stage, it is critical to estimate the capabilities of a system quickly and accurately. However, the original HPL mathematical model based on a single core and single communication layer yields varying accuracy for modern processors and accelerators comprising large numbers of cores. To reduce the performance‐estimation gap between the HPL model and an actual system, we propose a mathematical model for multi‐communication layered HPL. The effectiveness of the proposed model is evaluated by applying it to a GPU cluster and well‐known systems. The results reveal performance differences of 1.1% on a single GPU. The GPU cluster and well‐known large system show 5.5% and 4.1% differences on average, respectively. Compared to the original HPL model, the proposed multi‐communication layered HPL model provides performance estimates within a few seconds and a smaller error range from the processor/accelerator level to the large system level