Open-Source Multi-GPU-Accelerated QM/MM Simulations with AMBER and QUICK
Open-Source Multi-GPU-Accelerated QM/MM Simulations with AMBER and QUICK
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DOI：
10.1021/acs.jcim.1c00169
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发表时间：
2021-04-29
影响因子：
5.6
通讯作者：
Gotz, Andreas W.
中科院分区：
文献类型：
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作者：
Cruzeiro, Vinicius Wilian D.;Manathunga, Madushanka;Gotz, Andreas W.
量子力学/分子力学（QM/MM）方法是计算化学中一个重要的和完善的工具，已被广泛应用于无数的生物分子问题的文献。在本出版物中，我们报告了量子相互作用计算内核（QUICK）程序的集成，作为一个引擎，在QM/MM模拟中使用AMBER进行电子结构计算。这种集成可以通过基于文件的接口（FBI）或应用程序编程接口（API）实现。由于QUICK是一个具有多GPU并行化的开源GPU加速代码，因此用户可以在QM/MM模拟中利用“免费”GPU加速。在这项工作中，我们将讨论实现细节，并给出使用示例。我们还研究了在微正则系综下进行的典型QM/MM模拟中的能量守恒。最后，两个代表性的系统在散装水，N-甲基乙酰胺（NMA）分子和光敏黄蛋白（PYP）的基准测试结果，显示了QM/MM模拟与QUICK和AMBER使用不同数量的CPU内核和GPU的性能。我们的结果突出了从单个或多个GPU获得的加速;我们观察到单个GPU与单个CPU核心之间的加速比高达53倍，而将四个GPU与单个GPU进行比较时，加速比高达2.6倍。结果还显示，当使用API而不是FBI时，加速高达3.5倍。
The quantum mechanics/molecular mechanics (QM/MM) approach is an essential and well-established tool in computational chemistry that has been widely applied in a myriad of biomolecular problems in the literature. In this publication, we report the integration of the QUantum Interaction Computational Kernel (QUICK) program as an engine to perform electronic structure calculations in QM/MM simulations with AMBER. This integration is available through either a file-based interface (FBI) or an application programming interface (API). Since QUICK is an open-source GPU-accelerated code with multi-GPU parallelization, users can take advantage of "free of charge" GPU-acceleration in their QM/MM simulations. In this work, we discuss implementation details and give usage examples. We also investigate energy conservation in typical QM/MM simulations performed at the microcanonical ensemble. Finally, benchmark results for two representative systems in bulk water, the N-methylacetamide (NMA) molecule and the photoactive yellow protein (PYP), show the performance of QM/MM simulations with QUICK and AMBER using a varying number of CPU cores and GPUs. Our results highlight the acceleration obtained from a single or multiple GPUs; we observed speedups of up to 53x between a single GPU vs a single CPU core and of up to 2.6x when comparing four GPUs to a single GPU. Results also reveal speedups of up to 3.5x when the API is used instead of FBI.