Best bang for your buck: GPU nodes for GROMACS biomolecular simulations.
Best bang for your buck: GPU nodes for GROMACS biomolecular simulations.
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DOI：
10.1002/jcc.24030
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发表时间：
2015-10-05
影响因子：
3
通讯作者：
Grubmueller, Helmut
中科院分区：
文献类型：
--
作者：
Kutzner, Carsten;Pall, Szilard;Fechner, Martin;Esztermann, Ansgar;de Groot, Bert L.;Grubmueller, Helmut
分子动力学模拟包GROMACS可以在从商用工作站到高性能计算集群的各种硬件上高效运行。硬件功能通过单指令多数据、多线程和基于消息传递接口（MPI）的单程序多数据/多程序多数据并行性的组合得到充分利用，而图形处理单元（GPU）可用作加速器来计算从CPU卸载的交互。在这里，我们评估哪种硬件以最经济的方式使用GROMACS 4.6或5.0生成轨迹。我们已经组装了具有各种CPU/GPU组合的计算节点并对其进行了基准测试，以确定原始轨迹生产率、性能价格比、能源效率和其他几个标准方面的最佳组合。虽然硬件价格自然会受到趋势和波动的影响，但总的趋势是显而易见的。添加任何类型的GPU都可以显著提升节点的模拟性能。对于廉价的消费级GPU，这种改进同样反映在性能价格比上。虽然消费级GPU中的内存问题可能会被忽视，因为这些卡不支持错误检查和纠正内存，但不可靠的GPU可以通过内存检查工具进行分类。除了硬件费用和原始性能等成本效率的明显决定因素外，节点的能耗是一个主要的成本因素。在典型的硬件寿命期间，直到几年的更换，用于电力和冷却的成本可能变得大于硬件本身的成本。考虑到这一点，CPU和消费级GPU资源比例平衡的节点在其生命周期内产生最大量的GROMACS轨迹。© 2015作者。计算化学杂志由Wiley Periodicals，Inc.出版。
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.
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