Prediction model of performance–energy trade-off for CFD codes on AMD-based cluster

Abstract

This work explores the importance of performance–energy correlation for CFD codes, highlighting the need for sustainable and efficient use of clusters. The prime goal includes the optimisation of selecting and predicting the optimal number of computational nodes to reduce energy consumption and/or improve calculation time. In this work, the utilisation cost of the cluster, measured in core-hours, is used as a crucial factor in energy consumption and selecting the optimal number of computational nodes. The work is conducted on the cluster with AMD EPYC Milan-based CPUs and OpenFOAM application using the Urban Air Pollution model. In order to investigate performance–energy correlation on the cluster, the CVOPTS (Core VOlume Points per TimeStep) metric is introduced, which allows a direct comparison of the parallel efficiency for applications in modern HPC architectures. This metric becomes essential for evaluating and balancing performance with energy consumption to achieve cost-effective hardware configuration. The results were confirmed by numerous tests on a 40-node cluster, considering representative grid sizes. Based on the empirical results, a prediction model was derived that takes into account both the computational and communication costs of the simulation. The research reveals the impact of the AMD EPYC architecture on superspeedup, where performance increases superlinearly with the addition of more computational resources. This phenomenon enables a priori the prediction of performance–energy trade-offs (computing-faster or energy-save setups) for a specific application scenario, through the utilisation of varying quantities of computing nodes.