MatrixMul_ShareMem

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)

使用CUDA并行语言编写的矩阵乘法，与串行语句的对比试验，主要检测出CPU与GPU运行的时间，通过比较时间，达到比较效果。另外此程序可以随意更改矩阵的维数，增强程序的移植性。(CUDA parallel language use matrix multiplication, and serial statement comparison test, the main test of CPU and GPU to run time, by comparing the time, to compare results. Also this program can change the dimension of the matrix, and enhanced portability of the program.)

并行计算实验，用于求历史最高气温的算法，包含openmp，pthread，mpi，mapreduce等并行计算算法(algorithm includes openmp，pthread，mpi，mapreduce and so on )

mpi的源码，示例程序等，主要用于对mpi并行编程程序的研究。(mpi source code, sample programs, mainly for mpi parallel programming procedures.)

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)

并行计算实验，用于矩阵乘法的计算，canon算法，使用mpi方法(algorithm includes openmp，pthread，mpi，mapreduce and so on )

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.)

LU算法的MPI串行实现，矩阵维数无要求，可在曙光4000A并行机运行(The LU algorithm MPI serial implementation matrix dimension requirements Dawning 4000A parallel machine running)

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.)

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)