openmp

Parallelizing many nested for loops in openMP c++

由 会有一股神秘感。 提交于 2021-01-28 11:14:38
问题 Hi i am new to c++ and i made a code which runs but it is slow because of many nested for loops i want to speed it up by openmp anyone who can guide me. i tried to use ' #pragma omp parallel ' before ip loop and inside this loop i used ' #pragma omp parallel for ' before it loop but it does not works #pragma omp parallel for(int ip=0; ip !=nparticle; ip++){ inf14>>r>>xp>>yp>>zp; zp/=sqrt(gamma2); counter++; double para[7]={0,0,Vz,x0-xp,y0-yp,z0-zp,0}; if(ip>=0 && ip<=43){ #pragma omp parallel

Nested Parallelism : Why only the main thread runs and executes the parallel for loop four times?

由 时光怂恿深爱的人放手 提交于 2021-01-27 10:50:37
问题 My code: #include <cstdio> #include "omp.h" int main() { omp_set_num_threads(4); #pragma omp parallel { #pragma omp parallel for // Adding "parallel" is the cause of the problem, but I don't know how to explain it. for (int i = 0; i < 6; i++) { printf("i = %d, I am Thread %d\n", i, omp_get_thread_num()); } } return 0; } The output that I am getting: i = 0, I am Thread 0 i = 1, I am Thread 0 i = 2, I am Thread 0 i = 0, I am Thread 0 i = 1, I am Thread 0 i = 0, I am Thread 0 i = 1, I am Thread

Nested Parallelism : Why only the main thread runs and executes the parallel for loop four times?

由 守給你的承諾、 提交于 2021-01-27 10:43:39
问题 My code: #include <cstdio> #include "omp.h" int main() { omp_set_num_threads(4); #pragma omp parallel { #pragma omp parallel for // Adding "parallel" is the cause of the problem, but I don't know how to explain it. for (int i = 0; i < 6; i++) { printf("i = %d, I am Thread %d\n", i, omp_get_thread_num()); } } return 0; } The output that I am getting: i = 0, I am Thread 0 i = 1, I am Thread 0 i = 2, I am Thread 0 i = 0, I am Thread 0 i = 1, I am Thread 0 i = 0, I am Thread 0 i = 1, I am Thread

OpenMP vs gcc compiler optimizations

由 醉酒当歌 提交于 2021-01-27 06:14:09
问题 I'm learning openmp using the example of computing the value of pi via quadature. In serial, I run the following C code: double serial() { double step; double x,pi,sum = 0.0; step = 1.0 / (double) num_steps; for (int i = 0; i < num_steps; i++) { x = (i + 0.5) * step; // forward quadature sum += 4.0 / (1.0 + x*x); } pi = step * sum; return pi; } I'm comparing this to an omp implementation using a parallel for with reduction: double SPMD_for_reduction() { double step; double pi,sum = 0.0; step

Is writing std::deque at different memory locations concurrently thread-safe?

由 浪尽此生 提交于 2021-01-26 19:33:47
问题 I have a std::deque<std::pair<CustomObj, int>> that doesn't change in size when starting the concurrent block. The concurrent block reads each CustomObj of the deque and sets the int . I can guarantee that the deque won't change size therefore it won't reallocate, and that each thread will only access a memory chunk of the deque but not the other thread's. Does it lead to undefined behaviour reading and writing concurrently? Should I put the writing and reading in a mutual exclusion zone? 回答1

Is writing std::deque at different memory locations concurrently thread-safe?

由 大憨熊 提交于 2021-01-26 19:31:12
问题 I have a std::deque<std::pair<CustomObj, int>> that doesn't change in size when starting the concurrent block. The concurrent block reads each CustomObj of the deque and sets the int . I can guarantee that the deque won't change size therefore it won't reallocate, and that each thread will only access a memory chunk of the deque but not the other thread's. Does it lead to undefined behaviour reading and writing concurrently? Should I put the writing and reading in a mutual exclusion zone? 回答1

如何用TI DSP TMS320C6678处理器进行TI-IPC多核通信案例

由 泪湿孤枕 提交于 2021-01-25 22:01:22
如何用TMS320C6678处理器进行TI-IPC多核通信案例 本文基于创龙科技TL6678-EasyEVM评估板进行演示。 图1TL6678-EasyEVM评估板 TL6678-EasyEVM是一款基于TI KeyStone架构C6000系列TMS320C6678八核C66x定点/浮点高性能处理器设计的高端多核DSP评估板，由核心板与底板组成。核心板经过专业的PCB Layout和高低温测试验证，稳定可靠，可满足各种工业应用环境。 评估板接口资源丰富，引出双路千兆网口、SRIO、PCIe等高速通信接口，方便用户快速进行产品方案评估与技术预研。 开发案例主要包括：Ø (1) 裸机开发案例 (2) RTOS(SYS/BIOS)开发案例 (3) IPC、OpenMP多核开发案例 (4) SRIO、PCIe、双千兆网口开发案例 (5) 图像处理开发案例 (6) DSP算法开发案例 (7) 串口、网络远程升级开发案例 案例源码、产品资料（用户手册、核心板硬件资料、产品规格书）可点 site.tronlong.com/pfdownload 获取。 ​ 1.1 TI-IPC简介 TI-IPC(Inter-Processor Communication)是组件提供与处理器硬件无关的API，可用于多核处理器核间通信、同一处理器进程间通信和设备间通信。API支持消息传递、流和链接列表

GCC：优化 Linux、互联网和一切

由 ￣綄美尐妖づ 提交于 2021-01-23 09:05:21
软件如果不能被电脑运行，那么它就是无用的。而在处理运行时run-time性能的问题上，即使是最有才华的开发人员也会受编译器的支配 —— 因为如果没有可靠的编译器工具链，就无法构建任何重要的东西。GNU 编译器集合GNU Compiler Collection（GCC）提供了一个健壮、成熟和高性能的工具，以帮助你充分发挥你代码的潜能。经过数十年成千上万人的开发，GCC 成为了世界上最受尊敬的编译器之一。如果你在构建应用程序是没有使用 GCC，那么你可能错过了最佳解决方案。 根据 LLVM.org 的说法，GCC 是“如今事实上的标准开源编译器” [1] ，也是用来构建完整系统的基础 —— 从内核开始。GCC 支持超过 60 种硬件平台，包括 ARM、Intel、AMD、IBM POWER、SPARC、HP PA-RISC 和 IBM Z，以及各种操作环境，包括 GNU、Linux、Windows、macOS、FreeBSD、NetBSD、OpenBSD、DragonFly BSD、Solaris、AIX、HP-UX 和 RTEMS。它提供了高度兼容的 C/C++ 编译器，并支持流行的 C 库，如 GNU C Library（glibc）、Newlib、musl 和各种 BSD 操作系统中包含的 C 库，以及 Fortran、Ada 和 GO 语言的前端。GCC

C66x DSP如何实现程序远程升级

由 自闭症网瘾萝莉.ら 提交于 2021-01-07 19:25:09
前 言 DSP板卡一般通过仿真器进行调试，包括程序的加载与固化。由于众多应用场合对产品体积、产品密封性均有严格要求，或我们根本无法近距离接触产品，因此终端产品很多时候无法预留JTAG接口或通过JTAG接口升级程序。此时，在不拆箱的前提下实现程序的远程升级，则显得尤为重要。 基于以上考虑，为了让嵌入式应用更简单 ，创龙科技(Tronlong)基于TI TMS320C6678平台提供了DSP程序远程升级方案：串口远程升级、网络远程升级。 1 硬件平台 本文基于创龙科技TL6678-EasyEVM评估板进行演示。 TL6678-EasyEVM是一款基于TI KeyStone架构C6000系列TMS320C6678八核C66x定点/浮点高性能处理器设计的高端多核DSP评估板，由核心板与底板组成。核心板经过专业的PCB Layout和高低温测试验证，稳定可靠，可满足各种工业应用环境。 评估板接口资源丰富，引出双路千兆网口、SRIO、PCIe等高速通信接口，方便用户快速进行产品方案评估与技术预研。 开发案例主要包括： (1) 裸机开发案例 (2) RTOS(SYS/BIOS)开发案例 (3) IPC、OpenMP 多核开发案例 (4) SRIO、PCIe、双千兆网口开发案例 (5) 图像处理开发案例 (6) DSP算法开发案例 (7) 串口、网络远程升级开发案例 案例源码、产品资料（用户手册

OpenMP 4.5 won't offload to GPU with target directive

由 ￣綄美尐妖づ 提交于 2021-01-04 06:03:11
问题 I am trying to make a simple GPU offloading program using openMP. However, when I try to offload it still runs on the default device, i.e. my CPU. I have installed a compiler, g++ 7.2.0 that has CUDA support (is in on a cluster that I use). When I run the below code it shows me that it can see the 8 GPUs but when I try to offload it says that it is still on the CPU. #include <omp.h> #include <iostream> #include <stdio.h> #include <math.h> #include <algorithm> #define n 10000 #define m 10000
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