格子波尔兹曼方法的GPU并行计算.doc

摘要 本文图形处理单元（GPU）的格玻尔兹曼方法（LBM）一。格玻尔兹曼（LB）格玻尔兹曼 Abstract Based on graphics processing unit (GPU) Lattice Boltzmann method (LBM), the paper implements some classic flow field calculation . Procedure consists of two iterative function , collision function and transfer function. Collision functions performed within the shared memory, while transfer function within the device memory . In order to optimize the application performance, boundary conditions will be performed within the collision function , which lengthen the collision stream process,thus reduce device memory access . By setting up different iteration steps, we found that for the same GPU lattice boltzmann (LB) program ,with the increase of iteration steps, the program execution time will keep linear growth in N steps, then the growth accelerate, and return linear growth at last, while the corresponding CPU program kept always linear growth, resulting in accelerate ratio will fell sharply from some step. By setting different lattice for the same flow ,we found the GPU lattice boltzmann (LB) procedures remains unchanged speed up ratio relative to the CPU’s of the same lattice. Through imposing different Block structure on the same program, we found that with Threads in Blocks increasing , iteration time shortened linearly first, when the Threads reaches a certain value, the iteration time will drastically reduce. Key words: lattice Boltzmann parallel computing GPU  目录 一 绪论 1 1.引言 1 2.格子Boltzmann方法概述 2 3.GPU概述 5 4.本文的主要工作 6 二 格子Boltzmann方法的基本原理 7 1. Boltzmann方程 7 2.格子Boltzmann方程 9 3.格子Boltzmann方法的实现 15 4.基本的边界条件 17 5.本章小结 19 三 CUDA技术 21 1. CUDA 编程模型 21 2.CUDA硬件 22 3.优化准则 23 4.本章小结 24 四 算法实现 25 1.算法方面 25 2. LBM的GPU实现 26 3. 本章小结 27 五 结果分析 28 1. 泊肃叶流 28 2.圆柱绕流 29 3.椭圆绕流 33 4.方腔流 34 5.本章小结 35 六 总结与展望 36  一 绪论 1.引言 近半个多世纪以来，随着科学技术水平的不断提高，人们需要处理的科学及工程问题也越来越复杂。面对这些问题，传统的实验和理论方法越来越难以满足实际需要，时代呼吁新的科学手段的出现。 20世纪50年代计算机的诞生使这种设想成为可能。随着计算机和计算方法的飞速发展,一种以计算方法为基础、运用计算机对科学及工程实际问题进行数值模拟的科学手段