MPI-program-design

并行计算导论，作为基础入门的指导书 张林波出版(Introduction to parallel computing, as a basis for entry guide book)

实现单GPU二维弹性波全波形反演，采用c++和CUDA c编写，VS2013开发。(2D elastic Full waveform Inversion using a single GPU card, based on C++/CUDA c language, developed by VS2013.)

采用cuda对dijkstra算法进行并行实现，性能提升7倍多。( U91C7 u75286 u5B09 u5B09 u7)

本程序为简易的CUDA编程，两个程序都是计算矩阵的乘法。其中Matrix1和Matrix2的区别是Matrix2使用了shared memory。每个程序都使用了串行和并行两种方式计算乘法，最后将并行的运算结果与串行运算结果对比，验证计算的正确性。同时，程序中利用CUDA计时模块统计了并行运算的耗时。因此可以得出使用shared memory后改善的运行时间。

研究多目标遗传算法的优化，包含遗传算法过程中涉及的的各个步骤，非常适合于研究遗传算法及对遗传算法进行多目标优化的同学

说明：  本文介绍的基于多线程和图像分块实现加速运算，因为调用了opencv库，比较容易编写和实现。OpenCV全称是是Open Source Computer Vision Library，它是由Intel微处理器研究实验室的视觉交互组开发，是一种用于数字图像处理和计算机视觉的开源函数库，采用的开发语言是C++。(This paper introduces an acceleration algorithm based on multi-threading and image partitioning, because it is easy to write and implement by calling the opencv library. OpenCV is called Open Source Computer Vision Library. It is developed by the Visual Interaction Group of Intel Microprocessor Research Laboratory. It is an open source library for digital image processing and computer vision. The development language used is C++)

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：  N体问题，它是指在已知N个物体的初始位置、速度和质量的情况下，在牛顿经典力学情况下研究它们的运动，包括轨迹预测等。(Back to the specific N-body problem, it refers to the study of the motion of n-bodies in Newtonian classical mechanics, including trajectory prediction, when the initial position, velocity and mass of n-bodies are known.)

基于FPGA的深度学习加速器设计与实现，帮助你增加对深度学习的理解，而且作为中文，很适合国内学者。(Design and implementation of deep learning accelerator based on FPGA)

使用mpi并行实现n体问题普通算法以及简化算法(Using mpi parallel n-body algorithm to achieve common problems and simplify algorithms)