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生物分子经典分子动力学模拟的加速器。

Accelerators for Classical Molecular Dynamics Simulations of Biomolecules.

机构信息

Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States.

Global Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States.

出版信息

J Chem Theory Comput. 2022 Jul 12;18(7):4047-4069. doi: 10.1021/acs.jctc.1c01214. Epub 2022 Jun 16.

Abstract

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

摘要

原子分子动力学 (MD) 模拟为研究人员提供了一种能力,使其能够对生物分子结构(如蛋白质)及其与类药物小分子的相互作用进行建模,其时空分辨率比使用实验方法更高。MD 模拟是一项非常昂贵的计算工作,传统上需要在专用硬件上进行大量投资,才能达到与生物学相关的时空尺度。我们的目标是总结文献中使用的基本算法,然后重点介绍影响加速器在实践中实现的挑战。我们考虑了三种广泛的加速器:图形处理单元 (GPU)、现场可编程门阵列 (FPGA) 和专用集成电路 (ASIC)。对这些类别进行比较研究,有助于讨论它们的相对权衡,并为当前的最新技术水平提供背景。最后,我们提供了对新兴硬件平台和 MD 算法的潜力的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54c1/9281402/98c2ebebac4f/ct1c01214_0001.jpg

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