code-of-ugupdate-for-Isight-in-MDO

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.)

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)

采用改进的三维Hasin准则，考虑基体和纤维损伤，以及复合材料分层损伤( U91C7 u7539 u659 u8F09 u7F4 u7R4 u7354 u03 U6599 u5206 u5C42 u635F u4F24)

进行相关运算时必须要是用的一些步骤,可以解决相关算法的一些问题(Related operations must be used in a number of steps can be solved a number of issues related to algorithm)

多核编程，比 OPENCL 简单 可以通过类似 OPENMP 的方式，简洁的实现异构编程，使用 GPU 计算(Multicore programming than simple OPENCL)

用CUDA的SAR面目标回波生成的代码。(Multiple points SAR echo simulation Algorithm based on GPU via CUDA)

fluent udf 实现动态边界条件 并行及串行程序(parallel and series udf based on facets loop)

CUDA并行计算的讲义，NVDIA培训专用，最全资料。(CUDA parallel)

AMD显卡编程的一些官方参考资料,与nVidia的cuda编程相似(reference materials on AMD APP programming)

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)