More bang for your buck: Improved use of GPU nodes for GROMACS 2018

We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more toward the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift toward GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware. © 2019 Wiley Periodicals, Inc.     

基于GPU并行计算的浅水波运动数值模拟

利用有限体积法求解描述水流运动的二维浅水方程组,模拟洪水波运动传播过程,并通过GPU并行计算技术对程序进行加速,建立了浅水运动高效模拟方法。数值模拟结果表明,基于本文提出的GPU并行策略以及通用并行计算架构(CUDA)支持,能够实现相比CPU单核心最高112倍的加速比,为利用单机实现快速洪水预测以及防灾减灾决策提供有效支撑。此外,对基于GPU并行计算的浅水模拟计算精度进行了论证,并对并行性能优化进行了分析。利用所建模型模拟了溃坝洪水在三维障碍物间的传播过程。     

A GPU-accelerated adaptive discontinuous Galerkin method for level set equation

This paper presents a GPU-accelerated nodal discontinuous Galerkin method for the solution of two- and three-dimensional level set (LS) equation on unstructured adaptive meshes. Using adaptive mesh refinement, computations are localised mostly near the interface location to reduce the computational cost. Small global time step size resulting from the local adaptivity is avoided by local time-stepping based on a multi-rate Adams–Bashforth scheme. Platform independence of the solver is achieved with an extensible multi-threading programming API that allows runtime selection of different computing devices (GPU and CPU) and different threading interfaces (CUDA, OpenCL and OpenMP). Overall, a highly scalable, accurate and mass conservative numerical scheme that preserves the simplicity of LS formulation is obtained. Efficiency, performance and local high-order accuracy of the method are demonstrated through distinct numerical test cases.     

Fast parallel Grad–Shafranov solver for real-time equilibrium reconstruction in EAST tokamak using graphic processing unit

To achieve real-time control of tokamak plasmas, the equilibrium reconstruction has to be completed sufficiently quickly. For the case of an EAST tokamak experiment, real-time equilibrium reconstruction is generally required to provide results within 1ms. A graphic processing unit(GPU) parallel Grad–Shafranov(G-S) solver is developed in P-EFIT code,which is built with the CUDA? architecture to take advantage of massively parallel GPU cores and significantly accelerate the computation. Optimization and implementation of numerical algorithms for a block tri-diagonal linear system are presented. The solver can complete a calculation within 16 μs with 65×65 grid size and 27 μs with 129×129 grid size, and this solver supports that P-EFIT can fulfill the time feasibility for real-time plasma control with both grid sizes.     

基于GPU的液晶自适应光学波前重构计算

利用GPU进行液晶自适应光学波前重构的加速计算.介绍了液晶自适应光学的Zernike模式波前重构算法,详细论述了GPU的通用架构和GPU实现波前重构的方法,给出了GPU与CPU的实验对比结果.结果表明,GPU计算波前重构不但可以准确无误地计算出液晶波前校正器的灰度级分布,计算速度更是传统CPU波前计算的几十倍.     

基于FTM算法的GPU加速

为了解决FTM(Front Tracking Method)算法在计算机中计算耗时长的问题,利用CUDA(Compute Unified Device Architecture)来实现FTM算法在GPU中的并行计算。结合GPU并行计算架构的特性以及FTM算法的特点,本文通过共享内存的引入、线程块划分和线程块共享内存边界元素的纳入、迭代方法的改进和迭代过程中存储结构的变换等方法,提出了将FTM算法中的网格计算以及界面标记点处理方法在GPU中的实现方式。最后,通过模拟单气泡在静止液体中的自由上升运动,验证了FTM在GPU中计算的可行性与计算效率的提升。     

基于Open VG云电子书系统的多级优化框架设计

针对电子书应用存在的文件格式、性能效率低下和图像失真等问题,设计了一种应用于云电子书系统的多级优化框架,优化框架主要体现在如下三个方面。第一,对向量图形类库的性能进行描述,并提出了一种优化算法,减少了类库的时间复杂度。第二,在嵌入式GPU上并行进行坐标系统的计算。利用GPU在并行计算方面的优势,云电子书在向量图形类库方面获取了显著的性能提升。第三,云电子书将文件转化功能转嫁给Hadoop云平台,节省了移动设备的能量消耗和计算时间。同时为了对Hadoop调度过程中的数据位置问题进行优化,将位置感知调度器运用到提出的系统。实验结果表明：云电子书系统与最初的Open VG类库相比,性能提升了约70%,而且云电子书系统与连续服务器平台相比,计算时间减小了约60%。     

KSSOLV-GPU: an Efficient GPU-Enabled MATLAB Toolbox for Solving the Kohn-Sham Equations within Density Functional Theory in Plane-Wave Basis Set?

KSSOLV (Kohn-Sham Solver) is a MATLAB (Matrix Laboratory) toolbox for solving the Kohn-Sham density functional theory (KS-DFT) with the plane-wave basis set. In the KS-DFT calculations, the most expensive part is commonly the diagonalization of Kohn-Sham Hamiltonian in the self-consistent field (SCF) scheme. To enable a personal computer to perform medium-sized KS-DFT calculations that contain hundreds of atoms, we present a hybrid CPU-GPU implementation to accelerate the iterative diagonalization algorithms implemented in KSSOLV by using the MATLAB built-in Parallel Computing Toolbox. We compare the performance of KSSOLV-GPU on three types of GPU, including RTX3090, V100, and A100, with conventional CPU implementation of KSSOLV respectively and numerical results demonstrate that hybrid CPU-GPU implementation can achieve a speedup of about 10 times compared with sequential CPU calculations for bulk silicon systems containing up to 128 atoms.     

3D-power图的快速生成方法

3D-power图在图形学和流体仿真等领域应用广泛。为解决已有的3D-power图计算方法时间性能较差的问题,提出了基于GPU的power图构造算法,给出了一种用于计算power图各区域之间的面积估值方法,使基于GPU的构造算法与Lloyd算法、牛顿法相结合,生成满足约束条件的3D质心容量限制power图（3D-centroidal capacity constrained power diagram,3D-CCCPD）。结果表明,本文算法的时间性能较已有的3D-power图构造方法提高了几个数量级。     

GPU accelerated MFiX-DEM simulations of granular and multiphase flows

In this research, a Graphical Processing Unit (GPU) accelerated Discrete Element Method (DEM) code was developed and coupled with the Computational Fluid Dynamic (CFD) software MFiX to simulate granular and multiphase flows with heat transfers and chemical reactions. The Fortran-based CFD solver was coupled with the CUDA/C++ based DEM solver through inter-process pipes. The speedup to the CPU version of MFiX-DEM is about 130–243 folds in the simulation of particle packings. In fluidized bed simulations, the DEM computation time is reduced from 91% to 17% with a speedup of 78 folds. The simulation of Geldart A particle fluidization revealed a similar level of importance of both fluid and particle coarse-graining. The filtered drag derived from the two-fluid model is suitable for Euler-Lagrangian simulations with both fluid and particle coarse-graining. It overcorrects the influence of sub-grid structures if used for simulations with only fluid coarse-graining.