pi

OpenCL1.2版本的扩展功能用户手册。(OpenCL1.2 version of the Expansion Function User' s Manual.)

优化matlab,toolbox,可以用于各种优化问题(Optimization matlab, toolbox, can be used for a variety of optimization problems)

--概述： 变形两个图像的位图或影片剪辑的序列作为输出结果。 硬件要求： 此示例需要 DirectX 11 能够卡，如果没有检测到示例将使用 DirectX 11 参考模拟器。 -软件要求： 从 http://msdn.microsoft.com 安装 Visual Studio 2012

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)

西门子MPI协议分析，用于S7-300/400等PLC上(Siemens MPI Protocol, used for S7-300.400 PLCs)

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))

说明：  数值模拟空心高斯光束在自由空间中的传输特性(NUMERICAL SIMULATION OF PROPAGATION CHARACTERISTICS OF HOLLOW GAUSSIAN BEAMS IN FREE SPACE)

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)

设计MPI程序模拟广播（MPI_Bcast）操作：将每个MPI进程按照所在节点名称建立node通信子域分组；再将各个node子通信域的0号进程再次组成一个名为head的通信域；在进行广播时，首先，由root进程将消息在head通信子域内广播，然后，再由head子域内各进程在其所在的node子域内进行广播。(Design the MPI program MPI_Bcast operation: Each MPI process establishes a node communication sub-domain grouping according to the node name; then, each process of the 0 node of each node sub-communication domain is again formed into a communication domain named head; At first, the root process broadcasts the message in the head communication sub-domain, and then the processes in the head sub-domain broadcast in the node sub-domain where it is)

一本将基于近邻传播算法的半监督聚类的算方法书.对于聚类研究的很有帮助(Abstract: A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this paper. AP takes as input measures of similarity between pairs of data points. AP is an efficient and fast clustering algorithm for large dataset compared with the existing clustering algorithms, such as K-center clustering. But for the datasets with complex cluster structures, it cannot produce good clustering results. It can improve the clustering performance of AP by using the priori known labeled data or pairwise constraints to adjust the similarity matrix. Experimental results show that such method indeed reaches its goal for complex datasets, and this method outperforms the comparative methods when there are a large number of pairwise constraints. )