GPU引发的计算化学革命

综述了图形处理器(GPU)在计算化学中的应用和进展.首先简单介绍了GPU在科学计算中应用的发展,然后分别详细讲述了迄今几个使用GPU和CUDA(compute unified device architecture,显卡厂商Nvidia推出的计算平台)开发工具设计的量子化学计算和分子动力学(MD)模拟的算法和程序,尤其对目前唯一完全使用GPU技术开发的量子化学计算软件TeraChem做了完备的介绍,包括算法、实现的细节和程序目前的功能.此外,本文还对GPU在计算化学上将会发挥的作用做出了极为乐观的展望.     

Fast calculation of DNMR spectra on CUDA-enabled graphics card

During the past few years, general-purpose graphics processing units (GPGPUs) have become rather popular in the high performance computing community. In this study, we present an implementation of the simulation of dynamic nuclear magnetic resonance (DNMR) spectra. The algorithm is based on the kinetic Monte Carlo method and therefore can benefit from the multithreaded architecture of the GPGPU. By careful optimization of the algorithm a 30-100-fold speed increase could be achieved.     

Acceleration of a QM/MM‐QMC simulation using GPU

We accelerated an ab initio molecular QMC calculation by using GPGPU. Only the bottle‐neck part of the calculation is replaced by CUDA subroutine and performed on GPU. The performance on a (single core CPU + GPU) is compared with that on a (single core CPU with double precision), getting 23.6 (11.0) times faster calculations in single (double) precision treatments on GPU. The energy deviation caused by the single precision treatment was found to be within the accuracy required in the calculation, ～10^?5 hartree. The accelerated computational nodes mounting GPU are combined to form a hybrid MPI cluster on which we confirmed the performance linearly scales to the number of nodes. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

A GPU solvent–solvent interaction calculation accelerator for biomolecular simulations using the GROMOS software

During the past few years, graphics processing units (GPUs) have become extremely popular in the high performance computing community. In this study, we present an implementation of an acceleration engine for the solvent–solvent interaction evaluation of molecular dynamics simulations. By careful optimization of the algorithm speed‐ups up to a factor of 54 (single‐precision GPU vs. double‐precision CPU) could be achieved. The accuracy of the single‐precision GPU implementation is carefully investigated and does not influence structural, thermodynamic, and dynamic quantities. Therefore, the implementation enables users of the GROMOS software for biomolecular simulation to run the solvent–solvent interaction evaluation on a GPU, and thus, to speed‐up their simulations by a factor 6–9. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

A low-cost-memory CUDA implementation of the conjugate gradient method applied to globally supported radial basis functions implicits

Hermitian radial basis functions implicits is a method capable of reconstructing implicit surfaces from first-order Hermitian data. When globally supported radial functions are used, a dense symmetric linear system must be solved. In this work, we aim at exploring and computing a matrix-free implementation of the Conjugate Gradients Method on the GPU in order to solve such linear system. The proposed method parallelly rebuilds the matrix on demand for each iteration. As a result, it is able to compute the Hermitian-based interpolant for datasets that otherwise could not be handled due to the high memory demanded by their linear systems.     

Molecular dynamics simulation of complex multiphase flow on a computer cluster with GPUs

Compute Unified Device Architecture (CUDA) was used to design and implement molecular dynamics (MD) simulations on graphics processing units (GPU). With an NVIDIA Tesla C870, a 20–60 fold speedup over that of one core of the Intel Xeon 5430 CPU was achieved, reaching up to 150 Gflops. MD simulation of cavity flow and particle-bubble interaction in liquid was implemented on multiple GPUs using a message passing interface (MPI). Up to 200 GPUs were tested on a special network topology, which achieves good scalability. The capability of GPU clusters for large-scale molecular dynamics simulation of meso-scale flow behavior was, therefore, uncovered. Supported by the National Natural Science Foundation of China (Grant Nos. 20336040, 20221603 and 20490201), and the Chinese Academy of Sciences (Grant No. Kgcxz-yw-124)     

Acceleration of coarse grain molecular dynamics on GPU architectures

Coarse grain (CG) molecular models have been proposed to simulate complex systems with lower computational overheads and longer timescales with respect to atomistic level models. However, their acceleration on parallel architectures such as graphic processing units (GPUs) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance. To achieve this, we implemented a GPU‐accelerated version of a CG molecular dynamics simulator, to which we applied specific optimizations for CG models, such as dedicated data structures to handle different bead type interactions, obtaining a maximum speed‐up of $14$ on the NVIDIA GTX480 GPU with Fermi architecture. We provide a complete characterization and evaluation of algorithmic and simulated system features of CG models impacting the achievable speed‐up and accuracy of results, using three different GPU architectures as case studies. © 2012 Wiley Periodicals, Inc.     

Parallelization and improvements of the generalized born model with a simple sWitching function for modern graphics processors

Two fundamental challenges of simulating biologically relevant systems are the rapid calculation of the energy of solvation and the trajectory length of a given simulation. The Generalized Born model with a Simple sWitching function (GBSW) addresses these issues by using an efficient approximation of Poisson–Boltzmann (PB) theory to calculate each solute atom's free energy of solvation, the gradient of this potential, and the subsequent forces of solvation without the need for explicit solvent molecules. This study presents a parallel refactoring of the original GBSW algorithm and its implementation on newly available, low cost graphics chips with thousands of processing cores. Depending on the system size and nonbonded force cutoffs, the new GBSW algorithm offers speed increases of between one and two orders of magnitude over previous implementations while maintaining similar levels of accuracy. We find that much of the algorithm scales linearly with an increase of system size, which makes this water model cost effective for solvating large systems. Additionally, we utilize our GPU‐accelerated GBSW model to fold the model system chignolin, and in doing so we demonstrate that these speed enhancements now make accessible folding studies of peptides and potentially small proteins. © 2016 Wiley Periodicals, Inc.     

基于GPU加速的磁共振血管造影图像的并行分割与追踪算法

在应用磁共振血管造影图像进行临床诊断时,临床医生往往需要提取感兴趣区域(Region Of Interest,ROI)的部分血管.这个工作传统上需要手工进行,费时费力.该文提出一种并行的血管分割与追踪算法,利用现代图形处理器(Graphics Processing Unit,GPU)所具备的大规模并行计算能力进行快速的血管分割.首先将三维图像网格化为共面的立方体,并行处理每个立方体,确定立方体中哪些表面有血管通过,以及立方体中哪些体素包含血管.之后再将该结果用于串行的全局分割与血管追踪处理.实验结果表明,利用这种先并行后串行的方法,可以在1 s之内完成全脑血管的分割,分割的结果也更准确.