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描述了HL-2A等离子体实时平衡重建的GPU并行化算法,主要包括G-S方程的并行化处理、三对角方程求解、网格边界磁通计算以及一系列矩阵相乘的并行加速.并行后,在129×129的网格下完成一次迭代计算需要约575μs.     

Fast force field-based optimization of protein–ligand complexes with graphics processor

Usually based on molecular mechanics force fields, the post-optimization of ligand poses is typically the most time-consuming step in protein–ligand docking procedures. In return, it bears the potential to overcome the limitations of discretized conformation models. Because of the parallel nature of the problem, recent graphics processing units (GPUs) can be applied to address this dilemma. We present a novel algorithmic approach for parallelizing and thus massively speeding up protein–ligand complex optimizations with GPUs. The method, customized to pose-optimization, performs at least 100 times faster than widely used CPU-based optimization tools. An improvement in Root-Mean-Square Distance (RMSD) compared to the original docking pose of up to 42% can be achieved. © 2012 Wiley Periodicals, Inc.     

Real-time accurate Free-Form Deformation in terms of triangular Bézier surfaces

We implemented accurate FFD in terms of triangular Bézier surfaces as matrix multiplications in CUDA and rendered them via Open GL. Experimental results show that the proposed algorithm is more efficient than the previous GPU acceleration algorithm and tessellation shader algorithms.     

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。     

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

We implement a high-order finite-element application, which performs the numerical simulation of seismic wave propagation resulting for instance from earthquakes at the scale of a continent or from active seismic acquisition experiments in the oil industry, on a large cluster of NVIDIA Tesla graphics cards using the CUDA programming environment and non-blocking message passing based on MPI. Contrary to many finite-element implementations, ours is implemented successfully in single precision, maximizing the performance of current generation GPUs. We discuss the implementation and optimization of the code and compare it to an existing very optimized implementation in C language and MPI on a classical cluster of CPU nodes. We use mesh coloring to efficiently handle summation operations over degrees of freedom on an unstructured mesh, and non-blocking MPI messages in order to overlap the communications across the network and the data transfer to and from the device via PCIe with calculations on the GPU. We perform a number of numerical tests to validate the single-precision CUDA and MPI implementation and assess its accuracy. We then analyze performance measurements and depending on how the problem is mapped to the reference CPU cluster, we obtain a speedup of 20x or 12x.     

A new theoretical derivation of NFFT and its implementation on GPU

An efficient calculation of NFFT (nonequispaced fast Fourier transforms) is always a challenging task in a variety of application areas, from medical imaging to radio astronomy to chemical simulation. In this article, a new theoretical derivation is proposed for NFFT based on gridding algorithm and new strategies are proposed for the implementation of both forward NFFT and its inverse on both CPU and GPU. The GPU-based version, namely CUNFFT, adopts CUDA (Compute Unified Device Architecture) technology, which supports a fine-grained parallel computing scheme. The approximation errors introduced in the algorithm are discussed with respect to different window functions. Finally, benchmark calculations are executed to illustrate the accuracy and performance of NFFT and CUNFFT. The results show that CUNFFT is not only with high accuracy, but also substantially faster than conventional NFFT on CPU.     

Assessment of WENO schemes for multi‐dimensional Euler equations using GPU

This paper presents a detailed study on the implementation of Weighted Essentially Non‐Oscillatory (WENO) schemes on GPU. GPU implementation of up to ninth‐order accurate WENO schemes for the multi‐dimensional Euler equations of gas dynamics is presented. The implementation detail is discussed in the paper. The computational times of different schemes are obtained and the speedups are reported for different number of grid points. Furthermore, the execution times for the main kernels of the code are given and compared with each other. The numerical experiments show the speedups for the WENO schemes are very promising especially for fine grids. Copyright © 2014 John Wiley & Sons, Ltd.     

基于紧致差分的Grad-Shafranov方程快速解法

在等离子体平衡重建迭代计算过程中,需要快速求解Grad-Shafranov方程(G-S方程)。构造了具有四阶精度紧致差分格式的离散方程,采用离散正弦变换技术对其进行快速求解并采用CUDATM实现GPU并行加速,将其应用到EAST等离子体平衡重建PEFIT代码中,实现基于紧致差分格式的快速G-S方程求解。结果表明,在65×65的网格下,给定方程右端项电流分布的前提下,使用GPU求解G-S方程所需时间为大约34μs。