Abstract
The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well‐exploited with a combination of single instruction multiple data, multithreading, and message passing interface (MPI)‐based single program multiple data/multiple program multiple data parallelism while graphics processing units (GPUs) can be used as accelerators to compute interactions off‐loaded from the CPU. Here, we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance‐to‐price ratio, energy efficiency, and several other criteria. Although hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer‐class GPUs this improvement equally reflects in the performance‐to‐price ratio. Although memory issues in consumer‐class GPUs could pass unnoticed as these cards do not support error checking and correction memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost‐efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well‐balanced ratio of CPU and consumer‐class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.
Highlights
Many research groups in the field of molecular dynamics (MD) simulation and computing centers need to make decisions on how to set up their compute clusters for running the MD codes
Hardware features are well exploited with a combination of single instruction multiple data (SIMD), multi-threading, and MPI-based SPMD / multiple program multiple data (MPMD) parallelism, while graphics processing units (GPUs) can be used as accelerators to compute interactions offloaded from the CPU
We focus on GROMACS, which is among the fastest ones, and provide a comprehensive test intended to identify optimal hardware in terms of MD trajectory production per investment
Summary
Many research groups in the field of molecular dynamics (MD) simulation and computing centers need to make decisions on how to set up their compute clusters for running the MD codes. Beginning with version 4.6, the compute-intensive calculation of short-range non-bonded forces can be offloaded to graphics processing units (GPUs), while the CPU concurrently computes all remaining forces such as longrange electrostatics, bonds, etc., and updates the particle positions.[9] through multiple program multiple data (MPMD) task-decomposition the long-range electrostatics calculation can be offloaded to a separate set of MPI ranks for better parallel performance This multi-level heterogeneous parallelization has been shown to achieve strong scaling to as little as 100 particles per core, at the same time reaching high absolute application performance on a wide range of homogeneous and heterogeneous hardware platforms.[10,11]
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