一种Fortran到CUDA C的转换方法

基于GPU的异构计算逐渐成为主流计算方法,但限于科学计算编程的历史发展,大量的数值计算软件仍以Fortran语言实现.为了提高计算速度,大量的软件需要移植为CUDA C,但人工实现程序移植是一项浩繁的工程.若能实现从Fortran到CUDA C的自动转换,可以极大的提高程序开发效率.本文设计了将Fortran转换为CUDA C的算法,并基于正则表达式和shell脚本实现了该算法,编写测试用例进行了验证.实验表明,该算法可靠稳定兼容性好,在大型程序的移植过程中,能够自动筛选并建立变量信息表,生成CUDA相关操作函数,且结果代码可读性较好,转化正确率达80%以上,有效减少了移植的工作量.

基于计算统一设备架物Fortran的直接模拟蒙特卡洛方法并行优化

利用基于图形处理器(GPU)的计算统一设备架构(CUDA)Fortran编程平台,对直接模拟蒙特卡洛(DSMC)方法进行并行优化,并以高超声速气动热计算为例,考察了串行与并行计算速度以及不同仿真分子数对并行效率的影响.结果表明,在保证计算精度不变的情况下,程序取得了4～10倍的加速比,并且加速性能高低与计算规模大小成正比.

海洋环流数值模式POP的GPU并行优化

POP是一种全球海洋环流模式,广泛应用于海洋研究和气候预测。但是,随着模式分辨率的提高,POP对计算能力的需求呈几何级数增长,从而限制了POP模式的发展。本文在分析POP原理和特征的基础上,采用CUDA Fortran编程模型将POP模式移植到GPU平台上,并采用了网格块间并行和网格块内并行相结合的多层次并行实现全局存储器合并访问,减少局部存储器的使用,利用寄存器提高数据重用度和增大GPU端代码以减少CPU与GPU间的通信等优化策略。实验结果表明,与运行在Intel Xeon X56756核处理器上的串行程序和6进程并行程序相比,GPUPOP可以分别获得8.47倍和1.5倍的加速效果。

Typhoon storm surge ensemble forecast based on GPU technique

The accuracy of typhoon forecasts plays an important role in the prediction of storm surges.The uncertainty of a typhoon’s intensity and track means it is necessary to use an ensemble model to predict typhoon storm surges.A hydrodynamic model,which is operational at the National Marine Environmental Forecasting Center,is applied to conduct surge simulations for South China coastal areas using the best track data with parametric wind and pressure models.The results agree well with tidal gauge observations.To improve the calculation efficiency,the hydrodynamic model is modified using CUDA Fortran.The calculation results are almost the same as those from the original model,but the calculation time is reduced by more than 99%.A total of 150 typhoon cases are generated by combining 50 typhoon tracks from the European Centre for Medium-Range Weather Forecasts with three possible typhoon intensity forecasts.The surge ensembles are computed by the improved hydrodynamic model.Based on the simulated storm surges for the different typhoon cases,ensemble and probability forecast products can be provided.The mean ensemble results and probability forecast products are shown to agree well with the observed storm surge caused by Typhoon Mangkhut.The improved model is highly suitable for ensemble numerical forecasts,providing better forecast products for decision-making,and can be easily implemented to run on regular workstations.

基于GPU集群加速的电阻率三维数值模拟

针对三维直流电法正演模拟中大型稀疏线性方程组求解,在GPU(Graphic Process Unit)集群上实现了并行预处理共轭梯度(conjugate gradient,CG)算法。矩阵预处理使用可并行的对称逐次超松弛近似逆预处理(symmetric successive over relaxation approximate inverse preconditioner,SSORAI),与传统的对称逐次超松弛预处理(symmetric successive over relaxation preconditioner,SSOR)相比,避免了串行的三角矩阵回代求解过程,增加了并行性。在GPU集群通信上,使用支持跨节点GPU-GPU直接通信的MVAPICH2,省去了GPU间通信时GPU到CPU的数据中转过程。为了提高数据局部性和重复性,使用RCM算法(reverse Cuthill-Mckee algorithm)对预处理后的线性方程组进行带宽缩减,并利用GPU的计算通信重叠,极大地缩减了计算时间。实验结果表明,这里提出的方法在GPU集群上有很好的可扩展性。同时,程序基于CUDA FORTRAN语言实现,可以容易地与现有FORTRAN程序相结合,提高程序的性能。

Compute Unified Device Architecture Implementation of Euler/Navier-Stokes Solver on Graphics Processing Unit Desktop Platform for 2-D Compressible Flows

Personal desktop platform with teraflops peak performance of thousands of cores is realized at the price of conventional workstations using the programmable graphics processing units(GPUs).A GPU-based parallel Euler/Navier-Stokes solver is developed for 2-D compressible flows by using NVIDIA′s Compute Unified Device Architecture(CUDA)programming model in CUDA Fortran programming language.The techniques of implementation of CUDA kernels,double-layered thread hierarchy and variety memory hierarchy are presented to form the GPU-based algorithm of Euler/Navier-Stokes equations.The resulting parallel solver is validated by a set of typical test flow cases.The numerical results show that dozens of times speedup relative to a serial CPU implementation can be achieved using a single GPU desktop platform,which demonstrates that a GPU desktop can serve as a costeffective parallel computing platform to accelerate computational fluid dynamics(CFD)simulations substantially.