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Molecular dynamics simulations with many-body potentials on multiple GPUs - the implementation, package and performance

机译:分子动力学模拟与多体的多体势   GpU - 实现,包和性能

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摘要

Molecular dynamics (MD) is an important research tool extensively applied inmaterials science. Running MD on a graphics processing unit (GPU) is anattractive new approach for accelerating MD simulations. Currently, GPUimplementations of MD usually run in a one-host-process-one-GPU (OHPOG) scheme.This scheme may pose a limitation on the system size that an implementation canhandle due to the small device memory relative to the host memory. In thispaper, we present a one-host-process-multiple-GPU (OHPMG) implementation of MDwith embedded-atom-model or semi-empirical tight-binding many-body potentials.Because more device memory is available in an OHPMG process, the system sizethat can be handled is increased to a few million or more atoms. In comparisonwith the CPU implementation, in which Newton's third law is applied to improvethe computational efficiency, our OHPMG implementation has achieved a28.9x~86.0x speedup in double precision, depending on the system size, thecut-off ranges and the number of GPUs. The implementation can also handle agroup of small boxes in one run by combining the small boxes into a large box.This approach greatly improves the GPU computing efficiency when a large numberof MD simulations for small boxes are needed for statistical purposes.
机译:分子动力学(MD)是广泛应用于材料科学的重要研究工具。在图形处理单元(GPU)上运行MD是加速MD仿真的一种有吸引力的新方法。目前,MD的GPU实现通常以一个单进程一GPU(OHPOG)的方式运行,由于设备内存相对于主机内存较小,此方案可能会限制实现可以处理的系统大小。在本文中,我们提出了一种MD的单主机多GPU(OHPMG)实现,该MD具有嵌入式原子模型或半经验紧密绑定的多体势能。可以处理的系统大小增加到几百万个或更多个原子。与采用牛顿第三定律来提高计算效率的CPU实现相比,我们的OHPMG实现根据系统大小,截止范围和GPU数量实现了28.9x〜86.0x倍的双精度加速。该实现还可以通过将小盒子组合成一个大盒子来一次处理一组小盒子。当出于统计目的需要对小盒子进行大量MD仿真时,这种方法大大提高了GPU的计算效率。

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