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OpenMP和reduce()_C_Openmp_Numerical Methods - Fatal编程技术网

OpenMP和reduce()

OpenMP和reduce(),c,openmp,numerical-methods,C,Openmp,Numerical Methods,我有3个简单的函数,一个是控制函数,接下来的2个函数使用OpenMP以稍微不同的方式完成。但是函数thread1比thread2和control给出了另一个分数,我不知道为什么 #include <stdio.h> #include <stdlib.h> #include <math.h> #include <omp.h> float function(float x){ return pow(x,pow(x,sin(x))); }

我有3个简单的函数,一个是控制函数,接下来的2个函数使用OpenMP以稍微不同的方式完成。但是函数thread1比thread2和control给出了另一个分数,我不知道为什么

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <omp.h>

float function(float x){
    return pow(x,pow(x,sin(x)));
}



 float integrate(float begin, float end, int count){
    float score = 0 , width = (end-begin)/(1.0*count), i=begin, y1, y2;


    for(i = 0; i<count; i++){
            score += (function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0;
 }
    return score; 
 }




 float thread1(float begin, float end, int count){
    float score = 0 , width = (end-begin)/(1.0*count), y1, y2;

    int i;
    #pragma omp parallel for reduction(+:score) private(y1,i) shared(count)
    for(i = 0; i<count; i++){
        y1 = ((function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0);
       score = score + y1;
    }

    return score;
 }


 float thread2(float begin, float end, int count){
    float score = 0 , width = (end-begin)/(1.0*count), y1, y2;

    int i;
    float * tab = (float*)malloc(count * sizeof(float));

    #pragma omp parallel for
    for(i = 0; i<count; i++){
            tab[i] = (function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0;
    }

    for(i=0; i<count; i++)
            score += tab[i];
    return score;
  }


  unsigned long long int rdtsc(void){
     unsigned long long int x;
     unsigned a, d;

    __asm__ volatile("rdtsc" : "=a" (a), "=d" (d));

    return ((unsigned long long)a) | (((unsigned long long)d) << 32);
   }






   int main(int argc, char** argv){
        unsigned long long counter = 0;


    //test
       counter = rdtsc();
       printf("control: %f \n ",integrate (atof(argv[1]), atof(argv[2]), atoi(argv[3])));
       printf("control count: %lld \n",rdtsc()-counter);
        counter = rdtsc();
       printf("thread1: %f \n ",thread1(atof(argv[1]), atof(argv[2]), atoi(argv[3])));
       printf("thread1 count: %lld \n",rdtsc()-counter);
        counter = rdtsc();
       printf("thread2: %f \n ",thread2(atof(argv[1]), atof(argv[2]), atoi(argv[3])));
       printf("thread2 count: %lld \n",rdtsc()-counter);

       return 0;
      }
更新: 好的,我试着更快地完成这项工作,并且不计算周期值两次

double thread3(double begin, double end, int count){
     double score = 0 , width = (end-begin)/(1.0*count), yp, yk;    
     int i,j, k;

     #pragma omp parallel private (yp,yk) 
     {
       int thread_num = omp_get_num_threads();
       k = count / thread_num;

    #pragma omp for private(i) reduction(+:score) 
    for(i=0; i<thread_num; i++){
        yp = function(begin + i*k*width);
        yk = function(begin + (i*k+1)*width);
        score += (yp + yk) * width / 2.0;
        for(j=i*k +1; j<(i+1)*k; j++){
            yp = yk;
            yk = function(begin + (j+1)*width);
            score  += (yp + yk) * width / 2.0;
        }
    }

  #pragma omp for private(i) reduction(+:score) 
  for(i = k*thread_num; i<count; i++)
    score += (function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0;
 }  
   return score;
 }
double thread3(双起点、双终点、整数计数){
双倍分数=0,宽度=(结束-开始)/(1.0*计数),yp,yk;
int i,j,k;
#pragma omp并行专用(yp,yk)
{
int thread_num=omp_get_num_threads();
k=计数/线程数;
#pragma omp用于私人(i)减少(+:分数)

对于(i=0;i你在积分一个非常强的峰值函数-x(xsin(x))-它覆盖了积分范围内的7个数量级。这大约是32位浮点数的极限,因此会有一些问题,取决于对数字求和的顺序。这不是OpenMP的问题,只是数字敏感性的问题

例如,考虑这个完全串行代码执行相同的积分:

#include <stdio.h>
#include <math.h>

float function(float x){
    return pow(x,pow(x,sin(x)));
}

int main(int argc, char **argv) {

    const float begin=3., end=13.;
    const int count = 100000;
    const float width=(end-begin)/(1.*count);

    float integral1=0., integral2=0., integral3=0.;

    /* left to right */
    for (int i=0; i<count; i++) {
         integral1 += (function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0;
    }

    /* right to left */
    for (int i=count-1; i>=0; i--) {
         integral2 += (function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0;
    }

    /* centre outwards, first right-to-left, then left-to-right */
    for (int i=count/2; i<count; i++) {
         integral3 += (function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0;
    }
    for (int i=count/2-1; i>=0; i--) {
         integral3 += (function(begin+(i*width)) + function(begin+(i+1)*width)) * width/2.0;
    }

    printf("Left to right: %lf\n", integral1);
    printf("Right to left: %lf\n", integral2);
    printf("Centre outwards: %lf\n", integral3);

    return 0;
}
--和你看到的一样,用两个线程求和必然会改变求和的顺序,因此你的答案也会改变

这里有几个选项。如果这只是一个测试问题,而此函数实际上并不表示要积分的内容,那么您可能已经很好了。否则,使用不同的数值方法可能会有所帮助

但这里也有一个简单的解决方案——数字的范围超过了
浮点数的范围,使得答案对求和顺序非常敏感,但舒适地符合
双精度的范围,使得问题不那么严重。请注意,更改为
双精度
并不是解决所有问题的神奇方法g、 在某些情况下,它只是推迟了问题或允许您掩盖数值方法中的缺陷。但在这里,它实际上很好地解决了根本问题。将上面所有的
浮点值更改为
双精度
s可提供:

$ ./reduce
Left to right: 5407589.272885
Right to left: 5407589.272885
Centre outwards: 5407589.272885

另一方面,如果需要在范围(18,23)内集成此函数,即使是双精度也无法节省您的时间。

添加了数值方法标记,因为这是这里的最终问题。顺便说一句,您可以使用
omp\u get\u wtime()
omp\u get\u wtick()
而不是
rdtsc()
$ ./reduce
Left to right: 5407308.500000
Right to left: 5407430.000000
Centre outwards: 5407335.500000
$ ./reduce
Left to right: 5407589.272885
Right to left: 5407589.272885
Centre outwards: 5407589.272885