Source3

Hight Performance Computing Demos

cuda资料，对于并行编程很有帮助！该资料描述了cuda的基本信息，以及重要的步骤，让您逐渐上手，轻松掌握cuda并行编程。(cuda information useful for parallel programming! The information describes the cuda s basic information, as well as an important step, so that gradually you get started, easy to master parallel programming cuda.)

清华大学郑仰东CUDA教学课件1，是很好的CUDA入门材料(Tsinghua university ZhengAngDong s CUDA teaching courseware 1,a very good CUDA introductory material)

一些关于并行计算的资料，对初学并行计算的人会有些许帮助(Information about parallel computing, parallel computing for beginners who have a little help)

GPU实现的MapReduce framework,对于学习并行编程和cuda平台的编程方面有着极好的参考价值，里面附带论文。用户要求有NViDIA显卡，并且安装cuda编程环境。(We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth, but are harder to program since their architectures are designed as a special-purpose co-processor and their programming interfaces are typically for graphics applications.)

MPI编程－Nbody问题并行程序（计算1000个天体的移动，天体数据由文件sample_input.in读入，结果输出到文件result1000.data）(MPI Programming-Nbody Parallel (1000 calculated the movement of celestial bodies, Objects sample_input.in data from the document read into, Results output to a file result1000.data))

OpenCL中文教程(AMD),最好的OpenCL教程，介绍了AMD显卡的OpenCL编程，其中有大量例程，并介绍了OpenCL优化方法(OpenCL Chinese tutorial (AMD), the best OpenCL tutorial introduces the AMD OpenCL graphics programming, including a large number of routines, and introduces OpenCL optimization method)

CUDA平行的優化，以矩陣乘法為基礎。透過ShareMemory做加速(CUDA ShareMem)

说明：  计算π值 C++ 并行算法 分别使用方法左矩形分解、中矩形分解以及梯形分解(π C++ MPI π C++ MPI π C++ MPI π C++ MPI)

CUDA代码，主要是要实现一个具体ADD操作，并且可以高速在GPU上运行(An Algorithm based on UKF)