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Efficient molecular dynamics simulations with many-body potentials on graphics processing units

机译:高效的分子动力学模拟与图形处理单元的许多身体电位

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摘要

Graphics processing units have been extensively used to accelerate classical molecular dynamics simulations. However, there is much less progress on the acceleration of force evaluations for many-body potentials compared to pairwise ones. In the conventional force evaluation algorithm for many-body potentials, the force, virial stress, and heat current for a given atom are accumulated within different loops, which could result in write conflict between different threads in a CUDA kernel. In this work, we provide a new force evaluation algorithm, which is based on an explicit pairwise force expression for many-body potentials derived recently (Fan et al., 2015). In our algorithm, the force, virial stress, and heat current for a given atom can be accumulated within a single thread and is free of write conflicts. We discuss the formulations and algorithms and evaluate their performance. A new open-source code, GPUMD, is developed based on the proposed formulations. For the Tersoff many-body potential, the double precision performance of GPUMD using a Tesla K40 card is equivalent to that of the LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) molecular dynamics code running with about 100 CPU cores (Intel Xeon CPU X5670 @ 2.93 GHz). (C) 2017 Elsevier B.V. All rights reserved.
机译:图形处理单元已广泛用于加速经典分子动力学模拟。然而,与成对的相比,对许多身体电位的加速度的加速度取得了很少的进展。在传统的力评估算法中,对于许多身体电位,给定原子的力,病毒应力和热电流累积在不同的环上,这可能导致CUDA内核中的不同线程之间的写入冲突。在这项工作中,我们提供了一种新的力量评估算法,该算法基于最近衍生的许多身体电位的显式成对力表达式(Fan等人,2015)。在我们的算法中,可以在单个线程内累积给定原子的力,病毒应力和热电流,并且没有写入冲突。我们讨论配方和算法并评估其性能。新的开源代码GPUMD是基于所提出的配方开发的。对于Tersoff许多身体潜力,使用Tesla K40卡的GPumd的双精度性能等同于与大约100个CPU内核运行的LAMMPS(大规模原子/分子量平行平行模拟器)分子动力学代码(英特尔Xeon CPU X5670 @ 2.93 GHz)。 (c)2017 Elsevier B.v.保留所有权利。

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