openmp | 易学教程

Tensorflow的StreamExecutor编程

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首先了解一下结构化编译器前端Clang。背景与概览 Low Level Virtual Machine (LLVM) 是一个开源的编译器架构，它已经被成功应用到多个应用领域。Clang ( 发音为 /klæŋ/) 是 LLVM 的一个编译器前端，它目前支持 C, C++, Objective-C 以及 Objective-C++ 等编程语言。Clang 对源程序进行词法分析和语义分析，并将分析结果转换为 Abstract Syntax Tree ( 抽象语法树 ) ，最后使用 LLVM 作为后端代码的生成器。 Clang 的开发目标是提供一个可以替代 GCC 的前端编译器。与 GCC 相比，Clang 是一个重新设计的编译器前端，具有一系列优点，例如模块化，代码简单易懂，占用内存小以及容易扩展和重用等。由于 Clang 在设计上的优异性，使得 Clang 非常适合用于设计源代码级别的分析和转化工具。Clang 也已经被应用到一些重要的开发领域，如 Static Analysis 是一个基于 Clang 的静态代码分析工具。本文将简单介绍 Clang 的背景知识和功能特性，并通过一个小例子介绍如何使用 Clang 的库来编写一个小程序来统计源代码中的函数。 Clang 的开发背景由于 GNU 编译器套装 (GCC) 系统庞大，而且 Apple 大量使用的 Objective-C

Getting started with openMP. install on windows

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问题 I want to write parallel program in C++ using OpenMP, so I am getting started with OpenMP. On the other words I am a beginner and I need good OpenMP guide telling how to install it. Does someone know how to install OpenMP on Windows, then compile and run the program? 回答1: OpenMP is not something that you install. It comes with your compiler. You just need a decent compiler that supports OpenMP and you need to know how to enable OpenMP support since it is usually disabled by default. The

Padding array manually

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问题 I am trying to understand 9 point stencil's algorithm from this book , the logic is clear to me , but the calculation of WIDTHP macro is what i am unable to understand, here is the breif code (original code is more than 300 lines length!!): #define PAD64 0 #define WIDTH 5900 #if PAD64 #define WIDTHP ((((WIDTH*sizeof(REAL))+63)/64)*(64/sizeof(REAL))) #else #define WIDTHP WIDTH #endif #define HEIGHT 10000 REAL *fa = (REAL *)malloc(sizeof(REAL)*WIDTHP*HEIGHT); REAL *fb = (REAL *)malloc(sizeof

Eigen 3.3 Conjugate Gradient is slower when multi-threaded with GCC compiler optimization

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问题 I've been using the ConjugateGradient solver in Eigen 3.2 and decided to try upgrading to Eigen 3.3.3 with the hope of benefiting from the new multi-threading features. Sadly, the solver seems slower (~10%) when I enable -fopenmp with GCC 4.8.4. Looking at xosview, I see that all 8 cpus are being used, yet performance is slower... After some testing, I discovered that if I disable compiler optimization (use -O0 instead of -O3 ), then -fopenmp does speed up the solver by ~50%. Of course, it's

Eigen 3.3 Conjugate Gradient is slower when multi-threaded with GCC compiler optimization

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How can I install openMP on my new MacBook Pro (with Mac OS Catalina)?

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问题 I installed Xcode (and also the command line tools) but terminal says (when I'm compiling): gcc -o task -fopenmp task.c clang: error: unsupported option '-fopenmp' I tried to install openmp via brew but people say that it's not available anymore on homebrew, they suggest to try brew instal llvm But I get the same error. I tried also in the boneyard brew install homebrew/boneyard/clang-omp but the repository doesn't exist anymore. Could you help me? I just need to learn openMP, I don't think

OpenMP--parallel computing 并行计算

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B站,OpenMP介绍 B站-Introduction to OpenMP OpenMP 1、OpenMp是并已被广泛接受的，用于共享内存并行系统的多处理器程序设计的一套指导性的编译处理方案。 2、OpenMP支持的编程语言包括C语言、C++和Fortran； 3、OpenMp提供了对并行算法的高层的抽象描述，程序员通过在源代码中加入专用的pragma来指明自己的意图，由此编译器可以自动将程序进行并行化，并在必要之处加入同步互斥以及通信。当选择忽略这些pragma，或者编译器不支持OpenMp时，程序又可退化为通常的程序(一般为串行)，代码仍然可以正常运作，只是不能利用多线程来加速程序执行。参见 OpenMP使用这是一篇比较好的OpenMP简介添加链接描述为什么需要编写并行程序？通常我们传统单核处理器上编写的程序无法利用多核处理器，我们需要使得程序充分利用处理器更快的运行程序，更加及时与逼真的模拟现实世界。为了达到这一目的，就需要软件开发工程师将串行程序改写为并行程序。怎么样编写并行程序？广泛采用的两种方式：任务并行和数据并行。任务并行：是指将有待解决的问题需要执行的任务分配到各个核上完成。数据并行：是指将有待解决的问题所需要处理的数据分配到各个核上完成，每个核在所分配的大致相当的数据集上执行相同操作。这里我们举个例子来说明什么是任务并行，什么是数据并行

Pybind11: Accessing python object with OpenMP using for-loop

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问题 I am trying to operate a c++ function on all elements of a python dictionary. For this I use a for loop in c++ over all elements of the dictionary. In this case and as far as I understand, this can be sped up using the #pragma omp parallel for simd clause. However, when I run it, I get the error: GC Object already Tracked Process finished with exit code 139 (interrupted by signal 11: SIGSEGV) Edit I have read on this post that the problem comes from the way of accessing a Python object in c++

How to manage shared variable in OpenMp

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问题 I am trying to write a OpenMp program. I have a for loop which iterates 100 times. I have divided that into 10 threads. Each thread runs 10 iterations and generates some count based on some condition. So as per this logic each thread will generate its own count. All I want is to copy this count to a variable which will hold the sum of all counts from all threads. If we make this variable(shared) to write in the loop, I guess it will serialize the threads. I just want to copy the last count of

ScalaMP ---- 模仿 OpenMp 的一个简单并行计算框架

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1、前言这个项目是一次课程作业，老师要求写一个并行计算框架，本人本身对openmp比较熟，加上又是scala 的爱好者，所以想了许久，终于想到了用scala来实现一个类似openmp的一个简单的并行计算框架。项目github地址： ScalaMp 2、框架简介该并行计算框架是受 openmp 启发，以 scala 语言实现的一个模仿 openmp 基本功能的简单并行计算框架，该框架的设计目标是，让用户可以只需关心并行的操作的实现而无需考虑线程的创建和管理。本框架实现了最基本的并行代码块和并行循环两个功能。接下来会介绍框架的接口设计和具体的技术实现细节。然后会以 3 个具体的例子来演示框架的使用方法，和验证框架的正确性，更多的例子详见github上的example.Main.scala文件。 3 个具体的并行计算问题包括： 1 、梯形积分法 2 、计算 pi 值 3 、多线程分段下载文件（图片、 mp3 ） 3、框架接口设计与技术实现 3.1、接口设计该框架主要是模仿了 openmp 的“ omp parallel ”和“ omp parallel for ”两条并行命令，以 scala 语言实现了自己的版本。在介绍接口设计之前首先我们可以分析一下以上五个问题的做一下抽象，把相同的可并行的部分抽象出来。并行这五个问题，抽象出来可以看成是给定一个任务

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