openmp

Idle threads while new threads can be assigned to a nested loop

早过忘川 提交于 2020-03-03 09:29:27
问题 I have two nested loops: !$omp parallel !$omp do do i=1,4 ... !$omp parallel !$omp do do j=1,4 call job(i,j) My computer can run four threads in parallel. For the outer loop such four threads are created. The first three finish quickly since for i=4 , the job is four times more expensive. Now I expect that in the inner parallel region, new threads share the work. But this doesn't happen: The CPU load stays at 1/4, just as if the 4th thread works serially on the inner loop. How can I allocate

Mac安装lightgbm导入import时报错的解决方案

こ雲淡風輕ζ 提交于 2020-02-27 03:50:44
OSError : dlopen(/Users/user/anaconda3/lib/python3.7/site-packages/lightgbm/lib_lightgbm.so, 6): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib Referenced from: /Users/user/anaconda3/lib/python3.7/site-packages/lightgbm/lib_lightgbm.so Reason: image not found 尝试了 https://stackoverflow.com/questions/29910217/homebrew-installation-on-mac-os-x-failed-to-connect-to-raw-githubusercontent-com 的方法,无效 继续花半小时排坑 https://blog.csdn.net/weixin_32087115/article/details/81489627 的方法,无效 后查阅官方文档—— For macOS users: Starting from version 2.2.1, the library file in distribution wheels is built by the

OpenMP 中的线程任务调度

三世轮回 提交于 2020-02-14 17:12:07
OpenMP中任务调度主要针对并行的for循环,当循环中每次迭代的计算量不相等时,如果简单地给各个线程分配相同次数的迭代,则可能会造成各个线程计算负载的不平衡,影响程序的整体性能。 如下面的代码中,如果每个线程执行的任务数量平均分配,有的线程会结束早,有的线程结束晚: 1 #include<stdio.h> 2 #include<omp.h> 3 4 int main(){ 5 int a[100][100] = {0}; 6 #pragma omp parallel for 7 for (int i =0; i < 100; i++){ 8 for(int j = i; j < 100; j++ ) 9 a[i][j] = ((i%7)*(j%13)%23); 10 } 11 return 0; 12 } 为此,OpenMP提供了schedule子句来实现任务的调度。 schedule子句:   schedule(type[, size]),   参数type是指调度的类型,可以取值为static,dynamic,guided,runtime四种值。其中runtime允许在运行时确定调度类型,因此实际调度策略只有前面三种。   参数size表示每次调度的迭代数量,必须是整数。该参数是可选的。当type的值是runtime时,不能够使用该参数。 1.静态调度static  

OpenMP并行开发(C++)

ε祈祈猫儿з 提交于 2020-02-07 15:07:00
https://zhuanlan.zhihu.com/p/51173703 最近,有个课设关于提取SIFT特征,老师要求不能使用OpenCV,从底层实现SIFT特征,在实现的过程中,参考了很多人的思路,其中有一个是对代码进行并行优化,引起了我的兴趣,所以找了一些资料来详细认识下OpenMP的使用~~ 参考文章: OpenMP并行程序设计(二) - 周伟明的多核、测试专栏 - CSDN博客 ​blog.csdn.net 标准并行模式执行代码的基本思想是,程序开始时只有一个主线程,程序中的串行部分都由主线程执行,并行的部分是通过派生其他线程来执行,但是如果并行部分没有结束时是不会执行串行部分的~ 开发环境:VS2015,注意使用时要将OpenMP打开,并且#include "omp" 在C++中,OpenMP的指令格式为:#pragma omp指令[子句[子句]…] 例如: #pragma omp parallel private(i, j) parallel 就是指令, private是子句 1. OpenMP的指令 OpenMP的指令有以下一些:(常用的已标黑) parallel,用在一个代码段之前,表示这段代码将被多个线程并行执行 for,用于for循环之前,将循环分配到多个线程中并行执行,必须保证每次循环之间无相关性。 parallel for, parallel 和

Fortran + OpenMP + polymorphism: what exactly is not supported?

隐身守侯 提交于 2020-02-03 08:21:29
问题 I am aware that the OpenMP 4.5 standard says that in Fortran "polymorphic entities" are not supported. What exactly does this mean? Does this only exclude calls to type-bound procedures that have a PASS attribute, but I can still use an instance of a user-defined type that has type-bound procedures in other ways (e.g. accessing its components)? Does this limitation only apply to the OMP PARALLEL block, or also to procedures called from this block, or to the entire compilation unit? Would be

Can C++ attributes be used to replace OpenMP pragmas?

风流意气都作罢 提交于 2020-02-03 04:06:51
问题 C++ attributes provide a convenient and standardized way to markup code with extra information to give to the compiler and/or other tools. Using OpenMP involves adding a lot of #pragma omp... lines into the source (such as to mark a loop for parallel processing). These #pragma lines seem to be excellent candidates for a facility such as generalized attributes. For example, #pragma omp parallel for might become [[omp::parallel(for)]] . The often inaccurate cppreference.com uses such an

Why openMP does not support reduction for arrays in C?

久未见 提交于 2020-01-25 18:10:08
问题 In OpenMP 3.0 in Fortran reduction is supported with the special construct, while in C/C++ it is delegated to a programmer. I was wondering if there is a special reason for that, because OpenMP 3.0 came out in 2008, so I thought it was enough time to implement it for C/C++ also. Is there any particular technical reason associated with C/C++, why it is still not supported for C/C++? 回答1: As was mentioned in the comments the reason for OpenMP not supporting reduction by default for arrays is

OpenMP 4.0 for accelerators: Nvidia GPU target

◇◆丶佛笑我妖孽 提交于 2020-01-25 18:08:26
问题 I'm trying to use openMP for accelerators (openMP 4.0) in Visual Studio 2012, using the Intel C++ 15.0 compiler. My accelerator is an Nvidia GeForce GTX 670. This code does not compile: #include <stdio.h> #include<iostream> #include <omp.h> using namespace std; int main(){ #pragma omp target #pragma omp parallel for for (int i=0; i<1000; i++) cout<<"Hello world, i am number "<< i <<endl; } Of course, everything goes fine when I comment the #pragma omp target line. I get the same problem when

OpenMP 4.0 for accelerators: Nvidia GPU target

自闭症网瘾萝莉.ら 提交于 2020-01-25 18:06:06
问题 I'm trying to use openMP for accelerators (openMP 4.0) in Visual Studio 2012, using the Intel C++ 15.0 compiler. My accelerator is an Nvidia GeForce GTX 670. This code does not compile: #include <stdio.h> #include<iostream> #include <omp.h> using namespace std; int main(){ #pragma omp target #pragma omp parallel for for (int i=0; i<1000; i++) cout<<"Hello world, i am number "<< i <<endl; } Of course, everything goes fine when I comment the #pragma omp target line. I get the same problem when

Having thread-local arrays in cython so that I can resize them?

情到浓时终转凉″ 提交于 2020-01-25 02:46:04
问题 I have an interval-treeish algorithm I would like to run in parallel for many queries using threads. Problem is that then each thread would need its own array, since I cannot know in advance how many hits there will be. There are other questions like this, and the solution suggested is always to have an array of size (K, t) where K is output length and t is number of threads. This does not work for me as K might be different for each thread and each thread might need to resize the array to