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-purpos...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-learningMD(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 longduration problems which were impossible/impractical to compute before.展开更多
基金supported by the National Natural Science Foundation of China(62474058 and 61804049)the Yuelushan Center for Industrial Innovation(2023YCII0104)+2 种基金the Huxiang High Level Talent Gathering Project(2019RS1023)the Technology Innovation and Entrepreneurship Funds of Hunan Province,P.R.China(2019GK5029)the Fund for Distinguished Young Scholars of Changsha(kq1905012).
文摘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-learningMD(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 longduration problems which were impossible/impractical to compute before.
