High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Pinghui Mo, Yujia Zhang, Zhuoying Zhao, Hanhan Sun, Junhua Li, Dawei Guan, Xi Ding, Xin Zhang, Bo Chen, Mengchao Shi, Duo Zhang, Denghui Lu, Yinan Wang, Jianxing Huang, Fei Liu, Xinyu Li, Mohan Chen, Jun Cheng, Bin Liang, Weinan E, Jiayu Dai, Linfeng Zhang, Han Wang, Jie Liu
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Abstract

Molecular dynamics (MD) is an indispensable atomistic-scale computational tool widely-used in various disciplines. In the past decades, nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units (CPU/GPU), which are well-known to suffer from their intrinsic “memory wall” and “power wall” bottlenecks. Consequently, nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming, imposing serious restrictions on the MD simulation size and duration. To solve this problem, here we propose a special-purpose MD processing unit (MDPU), which could reduce MD time and power consumption by about 103 times (109 times) compared to state-of-the-art machine-learning MD (ab initio MD) based on CPU/GPU, while keeping ab initio accuracy. With significantly-enhanced performance, the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or long-duration problems which were impossible/impractical to compute before.

Abstract Image

高速、低功耗分子动力学处理单元 (MDPU),具有原子序数精度
分子动力学(MD)是各学科广泛使用的不可或缺的原子尺度计算工具。在过去几十年中,几乎所有的原子动力学 MD 和机器学习 MD 都是基于通用中央处理器/图形处理器(CPU/GPU),而众所周知,CPU/GPU 本身存在 "内存墙 "和 "功耗墙 "瓶颈。因此,目前具有原子序数精度的 MD 计算非常耗时耗电,严重限制了 MD 模拟的规模和持续时间。为了解决这个问题,我们提出了一种特殊用途的 MD 处理单元(MDPU),与基于 CPU/GPU 的最先进机器学习 MD(ab initio MD)相比,它可以在保持 ab initio 精度的前提下将 MD 计算时间和功耗减少约 103 倍(109 倍)。由于性能大幅提升,所提出的 MDPU 可为以前无法计算/不切实际的大尺寸和/或长持续时间问题的原子尺度精确分析铺平道路。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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