问题描述
我在1个小时前提出了一个问题，但是问得不好，所以我重新提出了一个问题.
I created a question 1 hour ago but it wasn't well asked so I recreated one.
我在C语言中得到了Jacobi松弛代码:
I got a code that is Jacobi relaxation in C :
while ( error > tol && iter < iter_max ) {
error = 0.0;
for( int j = 1; j < n-1; j++)
{
for( int i = 1; i < m-1; i++ )
{
Anew[j][i] = 0.25 * ( A[j][i+1] + A[j][i-1]
+ A[j-1][i] + A[j+1][i]);
error = fmax( error, fabs(Anew[j][i] - A[j][i]));
}
}
for( int j = 1; j < n-1; j++)
{
for( int i = 1; i < m-1; i++ )
{
A[j][i] = Anew[j][i];
}
}
if(iter % 100 == 0) printf("%5d, %0.6f\n", iter, error);
iter++;
}
我用:
- 4096x4096的数组
- iter_max = 1000
- 错误= 1.0e-6
- 16个核心
我已将此代码与OpenACC并行化.现在，我想使用MPI尝试了解其工作原理.但是，对于我进行的第一个实现，我的效果并不理想(新数组构造得不好).我该如何将该代码段与MPI并行化?
I have parallelized this code with OpenACC. Now, I want to use MPI to try to understand how it works. However, for first implementations I made, I haven't good results (new array is not well constructed). How can I parallelize this code section with MPI ?
推荐答案
这是我为类似情况编写的代码，您可以将其用作指导.
Here is a code I had written for a similar case and you can use it for as a guide.
do {
iter++;
MPI_Irecv(&old[1][0], 1, myHelloVector, nbrs[LEFT], 2, MPI_COMM_WORLD, \
&requestFourR[LEFT]);
MPI_Irecv(&old[1][chunkSize[1]+1], 1, myHelloVector, nbrs[RIGHT], 1, \
MPI_COMM_WORLD, &requestFourR[RIGHT]);
MPI_Irecv(&old[0][1], chunkSize[1], MPI_FLOAT, nbrs[UP], 4, \
MPI_COMM_WORLD, &requestFourR[UP]);
MPI_Irecv(&old[chunkSize[0]+1][1], chunkSize[1], MPI_FLOAT, \
nbrs[DOWN], 3, MPI_COMM_WORLD, &requestFourR[DOWN]);
MPI_Issend(&old[1][1], 1, myHelloVector, nbrs[LEFT], 1, \
MPI_COMM_WORLD, &requestFourS[LEFT]);
MPI_Issend(&old[1][chunkSize[1]], 1, myHelloVector, nbrs[RIGHT], 2, \
MPI_COMM_WORLD, &requestFourS[RIGHT]);
MPI_Issend(&old[1][1], chunkSize[1], MPI_FLOAT, nbrs[UP], 3, \
MPI_COMM_WORLD, &requestFourS[UP]);
MPI_Issend(&old[chunkSize[0]][1], chunkSize[1], MPI_FLOAT, nbrs[DOWN], 4, \
MPI_COMM_WORLD, &requestFourS[DOWN]);
calImage(old, new, edge, chunkSize[ROWS], chunkSize[COLS]);
for (itr = 0; itr < 4; itr++) {
MPI_Waitany(4, &requestFourR[0], &index, &status);
switch ( index ) { /* status.MPI_TAG) */
case 0: /* RIGHT */
j = 1;
for (i = 2; i < chunkSize[0]; i++) {
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+ \
old[i][j+1] - edge[i][j]);
}
break;
case 1: /* LEFT */
j = chunkSize[1];
for (i = 2; i < chunkSize[0]; i++) {
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+ \
old[i][j+1] - edge[i][j]);
}
break;
case 2: /* DOWN */
i = 1;
for (j = 2; j < chunkSize[1]; j++) {
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+ \
old[i][j+1] - edge[i][j]);
}
break;
case 3: /* UP */
i = chunkSize[0];
for (j = 2; j < chunkSize[1]; j++) {
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+ \
old[i][j+1] - edge[i][j]);
}
break;
}
}
i = 1; j = 1;
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+old[i][j+1] - \
edge[i][j]);
i = 1; j = chunkSize[1];
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+old[i][j+1] - \
edge[i][j]);
i = chunkSize[0]; j = 1;
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+old[i][j+1] - \
edge[i][j]);
i = chunkSize[0]; j = chunkSize[1];
new[i][j] = 0.25f*(old[i-1][j]+old[i+1][j]+old[i][j-1]+old[i][j+1] - \
edge[i][j]);
MPI_Waitall(4, requestFourS, statusS);
temp = old;
old = new;
new = temp;
} while(your_stopping_condition);
calImage()函数正在执行不依赖于光晕交换操作的计算.
calImage() function is doing the calculations that are not depended at the halo swap operation.
void calImage(float **image, float **newImage, float **edge, \
int rows, int cols) {
int i, j;
for (i = 2; i < rows; i++) {
for (j = 2; j < cols; j++) {
newImage[i][j] = 0.25f * (image[i-1][j] \
+ image[i+1][j] \
+ image[i][j-1] \
+ image[i][j+1] \
- edge[i][j]);
}
}
}
这篇关于MPI中的Jacobi松弛的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！