GROMODEX: Optimisation of GROMACS Performance through a Design of Experiment Approach

Abstract

We introduce GROMODEX, a novel tool designed to optimise GROMACS molecular dynamics (MD) simulations using a structured Design of Experiments (DoE) approach. GROMACS, though efficient, requires extensive tuning of parameters to perform optimally on different hardware and molecular systems. Manual tuning is tedious and prone to errors, especially for high-performance computing (HPC) environments. GROMODEX automates this process, systematically exploring performance parameters to find the best configurations for each system. By using statistical techniques, it evaluates multiple parameters simultaneously, optimising for CPU and GPU architectures, and reducing simulation times while ensuring efficient resource use. We evaluated GROMODEX on four computational platforms spanning a wide range of cost and hardware complexity using four molecular systems from the NVIDIA GROMACS benchmark suite. Quite surprisingly, our results show that the performance does not always correlate with the hardware complexity and cost in a system-dependent manner. Furthermore, a fine system-dependent tuning assures substantial performance gains and time reductions, highlighting GROMODEXs ability to improve the performance of MD simulations significantly.