在多个fpga上执行完整的分子动力学模拟

C. Pascoe, Lawrence C. Stewart, B. W. Sherman, Vipin Sachdeva, Martin C. Herbordt
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引用次数: 5

摘要

我们修改了开源分子动力学(MD)模拟代码OpenMM[1],以增加对在fpga集群上运行完整的MD时间步的支持。应用程序的总体结构如图1所示。MD通过计算单个粒子上的力并对这些力进行积分来更新每个时间步的速度/位置。各种各样的力作用于每个粒子,我们根据计算要求将它们细分为三类:范围有限(RL),远程(LR)和绑定。RL相互作用包括伦纳德琼斯力和静电力之间的所有粒子对在径向截止。LR相互作用包括超过RL截止的静电力,其中成对计算将过于昂贵。我们使用平滑粒子网格Ewald (PME)方法计算LR力,该方法使用3D快速傅里叶变换(FFTs)来加速计算。键相互作用是未来工作的重点。为了便于硬件开发和应用程序集成,内核采用OpenCL编码。该设计混合使用定点和单/双精度浮点运算,以保持与CPU和GPU实现相同的精度水平。该项目的最终目标是在药物发现的背景下对生物相关系统进行MD模拟(即50,000-100,000个粒子的周期系统,密度约为每10立方1个原子Å),具有比gpu等其他技术更强的缩放性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Execution of Complete Molecular Dynamics Simulations on Multiple FPGAs
We have modified the open source molecular dynamics (MD) simulation code OpenMM [1] to add support for running complete MD timesteps on a cluster of FPGAs. The overall structure of the application is shown in Figure 1. MD proceeds by calculating forces on individual particles and integrating those forces to update velocities/positions on a per timestep basis. A variety of forces apply to each particle and we subdivide them into three categories based on the computation requirements: range limited (RL), long range (LR), and bonded. RL interactions comprise Lennard Jones and electrostatic forces between all particle pairs within a radial cutoff. LR interactions comprise electrostatic forces beyond the RL cutoff, where pairwise computation would be too costly. We calculate LR forces using the Smooth Particle Mesh Ewald (PME) method, which uses 3D Fast Fourier Transforms (FFTs) to accelerate computation. Bonded interactions are the focus of future work. Kernels are coded in OpenCL for ease of hardware development and application integration. The design uses a mix of fixedpoint and single-/double-precision floating-point arithmetic where needed to maintain the same level of accuracy as CPU and GPU implementations. The ultimate goal of this project is to perform MD simulation of biologically-relevant systems within the context of drug discovery (i.e., periodic systems of 50,000–100,000 particles with approximate density of 1 atom per 10 cubic Å) with strong scaling performance greater than possible with other technologies such as GPUs.
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