用OpenMP实现pi值计算.doc

计算Pi值程序清单：Monte Carlo方法串行程序：#include stdafx.h#include#include#includeint _tmain(int argc, _TCHAR* argv)long long max=10000000; long long i,count=0; double x,y,bulk,starttime,endtime; time_t t; starttime=clock();/ 产生以当前时间开始的随机种子 srand(unsigned) time(&t); for(i=0;imax;i+) x=rand(); x=x/32767; y=rand(); y=y/32767; if(x*x+y*y)=1) count+; bulk=4*(double(count)/max); endtime= clock(); printf(Pi is %f n, bulk); printf(Running time is %f n, endtime-starttime);return 0;并行程序：#include stdafx.h#include#include#include#include#define NUM_THREADS 4int _tmain(int argc, _TCHAR* argv)long long max=10000000; long long i,count=0; double x,y,bulk,starttime,endtime; time_t t; starttime=clock();/ 产生以当前时间开始的随机种子 srand(unsigned) time(&t);omp_set_num_threads(NUM_THREADS); #pragma omp parallel for reduction(+:count) private(x,y) for(i=0;imax;i+) x=rand(); x=x/32767; y=rand(); y=y/32767; if(x*x+y*y)=1) count+; bulk=4*(double(count)/max); endtime= clock(); printf(pi is %f n, bulk); printf(Running time is %f n, endtime-starttime);return 0;积分法串行程序：#include stdafx.h#include#include#includestatic long num_steps = 100000000; double step; int _tmain(int argc, _TCHAR* argv)int i; double x, pi, starttime,endtime,sum = 0.0; step = 1.0/(double) num_steps;time_t t; starttime=clock(); srand(unsigned) time(&t);for (i=0;i num_steps; i+) x = (i+0.5)*step; sum = sum + 4.0/(1.0+x*x); pi = step * sum;endtime= clock(); printf(Pi is %f n, pi); printf(Running time is %f n, endtime-starttime);return 0;并行程序：#include stdafx.h#include#include#include#includestatic long num_steps = 100000000; double step; #define NUM_THREADS 4 int _tmain(int argc, _TCHAR* argv)int i; double x, pi, starttime,endtime,sum = 0.0; step = 1.0/(double) num_steps;time_t t; starttime=clock(); srand(unsigned) time(&t);omp_set_num_threads(NUM_THREADS) ;#pragma omp parallel for reduction(+:sum) private(x) for (i=0;i num_steps; i+) x = (i+0.5)*step; sum = sum + 4.0/(1.0+x*x); pi = step * sum;endtime= clock(); printf(Pi is %f n, pi); printf(Running time is %f n, endtime-starttime);return 0;程序设计思想：OpenMP不需要创建线程，不需要处理线程同步、负载平衡、以及销毁工作；只需要加入编译制导语句，给编译器一个指示，通过编译制导语句来显式地指导并行化。本程序中使用了#pragma omp parallel for实现并行化。对共享变量count 、x 、y的处理：#pragma omp parallel for reduction(+:count) private(x,y) reduction子句使用指定的操作对其列表中出现的变量进行规约，初始时，每个线程都保留一份私有拷贝，在结构尾部根据指定的操作对线程中的相应变量进行规约，并更新该变量的全局值。private子句表示它列出的变量对于每个线程是局部的。实验运行结果：Monte Carlo方法串行程序并行程序并行程序中，修改测试点数max，观察pi值精度max=100000时：max=1000000时：max=10000