资源列表
MPI
- 高斯全主元消去MPI并行算法，主从式，C++，运行速度很快(Gauss total principal element elimination MPI parallel algorithm)
Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers
- HPL is a software package that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers. It can thus be regarded as a portable as well as freely available implementation of the High Performance Co
Mutilayer
- 通过传递矩阵理论计算多层膜结构的反射及透射率(Calculation of reflection and transmittance of multilayer structures by transfer matrix theory)
LBM_D2Q5
- 这是用LBM方法的简单的代码，利用到D2Q5方式得到温度扩散的过程。(This is a simple code for diffusion equation solution by LBM model)
codes
- first,IrisFx,London Breakout EA-V4.0EA,Moving Average,universalMACrossEA
SPH-CPPCODE-master
- c++编写的采用sph方法计算水运动的程序(## Ignore Visual Studio temporary files, build results, and ## files generated by popular Visual Studio add-ons. # User-specific files *.suo *.user *.userosscache *.sln.docstates # User-specific files (MonoDevelop/Xamarin Stud
MPI
- 上课时候的作业和一些老师的程序 用MPI编写，需要的可以看看 里面附有使用说明(Homework in class and some teacher's procedures Write with C's MPI, you can see what you need Instructions for use are attached)
shiyan1
- 利用多GPU进行分布式训练,用的是tensorflow的Keras平台(Distributed Training Using Multi-GPU)
OpenStack云计算实战 钟小平 2019.8
- 针对OpenStack的云计算编程，包括并行计算的算法研究，适合云计算研发人员进阶使用，内容新颖，紧跟时代脉搏(Cloud computing programming for openstack, including parallel computing algorithm research, is suitable for advanced use of cloud computing developers, with novel content and keeping up with the
LBM_Linux20051208
- 格子Boltzmann方法 格子Boltzmann方法是为了保留格子气自动机方法的优点，克服其缺点而发展起来的方法。 特别是1992年，钱跃弘、陈十一等的开创性工作(提出LBGK模型方法)，使该方法广泛地应用到计算流体力学(单相流、多相流、多孔介质流、热对流、磁流体、反应-扩散等)。 这是“格子模型”的并行处理，在LINUX下调试通过-lattice Boltzmann method lattice Boltzmann method is to retain the lattice
mop_5_27_modified_a
- 用改进蚁群算法求解一类连续空间优化问题的matlab实现-improved ant colony algorithm for solving a class of continuous space optimization problems achieving Matlab
MyGA-master
- 将节点按内存情况排序 将任务按内存需求情况排序 规则：将内存需求最小的节点先行分配在内存剩余最多的节点上(Sort nodes by memory and tasks by memory requirements Rule: the node with the least memory requirement is allocated to the node with the most remaining memory)