【发布时间】：2021-11-20 18:54:41
【问题描述】：
我一直在使用传热代码。基本上，这段代码确定了立方体及其所有面的初始条件。这六个面从不同的温度开始，然后代码将计算由于它们之间的热传递而导致所有面的温度如何变化。现在，我一直在尝试使用 OpenMP 指令卸载到 NVIDIA GPU。此代码使用三重指针初始化面部条件，它是一种数组数组。读了一点关于这个问题，我开始知道 3D 架构不容易卸载到 GPU 上。所以我的问题是是否可以将这个三重指针数组卸载到 GPU，或者我是否必须使用更扁平的数组形式。
我留下代码，它仍在 CPU 上运行。代码的并行版本。
#include <omp.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define N 25 //This defines the number of points per dimension (Cube = N*N*N)
#define NUM_STEPS 6000 //This is the number of simulations time steps
/*writeFile: this function writes simulation results into a file.
* A file is created for each iteration that's passed to the function
* as a parameter. It also takes the triple pointer to the simulation
* data*/
void writeFile(int iteration, double*** data){
char filename[50];
char itr[12];
sprintf(itr, "%d", iteration);
strcpy(filename, "heat_");
strcat(filename, itr);
strcat(filename, ".txt");
//printf("Filename is %s\n", filename);
FILE *fp;
fp = fopen(filename, "w");
fprintf(fp, "x,y,z,T\n");
for(int i=0; i<N; i++){
for(int j=0;j<N; j++){
for(int k=0; k<N; k++){
fprintf(fp,"%d,%d,%d,%f\n", i,j,k,data[i][j][k]);
}
}
}
fclose(fp);
}
void compute_heat_transfer(double ***arrayOld, double ***arrayNew){
int i,j,k;
/*Compute steady-state solution*/
for(int nsteps=0; nsteps < NUM_STEPS; nsteps++){
/*if(nsteps % 100 == 0){
writeFile(nsteps, arrayOld);
}*/
#pragma omp parallel shared(arrayNew, arrayOld) private(i,j,k)
{
#pragma omp for
for(i=1; i<N-1; i++){
for(j=1; j<N-1; j++){
for(k=1;k<N-1;k++){
//This is the 6-neighbor stencil computation
arrayNew[i][j][k] = (arrayOld[i-1][j][k] + arrayOld[i+1][j][k] + arrayOld[i][j-1][k] + arrayOld[i][j+1][k] +
arrayOld[i][j][k-1] + arrayOld[i][j][k+1])/6.0;
}
}
}
#pragma omp for
for(i=1; i<N-1; i++){
for(j=1; j<N-1; j++){
for(k=1; k<N-1; k++){
arrayOld[i][j][k] = arrayNew[i][j][k];
}
}
}
}
}
}
int main (int argc, char *argv[]) {
int i,j,k,nsteps;
double mean;
double ***arrayOld; //Variable that will hold the data of the past iteration
double ***arrayNew; //Variable where newly computed data will be stored
arrayOld = (double***)malloc(N*sizeof(double**));
arrayNew = (double***)malloc(N*sizeof(double**));
if(arrayOld== NULL){
fprintf(stderr, "Out of memory");
exit(0);
}
for(i=0; i<N;i++){
arrayOld[i] = (double**)malloc(N*sizeof(double*));
arrayNew[i] = (double**)malloc(N*sizeof(double*));
if(arrayOld[i]==NULL){
fprintf(stderr, "Out of memory");
exit(0);
}
for(int j=0;j<N;j++){
arrayOld[i][j] = (double*)malloc(N*sizeof(double));
arrayNew[i][j] = (double*)malloc(N*sizeof(double));
if(arrayOld[i][j]==NULL){
fprintf(stderr,"Out of memory");
exit(0);
}
}
}
/*Set boundary values and compute mean boundary values*/
mean = 0.0;
for(i=0; i<N; i++){
for(j=0;j<N;j++){
arrayOld[i][j][0] = 100.0;
mean += arrayOld[i][j][0];
}
}
for(i=0; i<N; i++){
for(j=0;j<N;j++){
arrayOld[i][j][N-1] = 100.0;
mean += arrayOld[i][j][N-1];
}
}
for(j=0; j<N; j++){
for(k=0;k<N;k++){
arrayOld[0][j][k] = 100.0;
mean += arrayOld[0][j][k];
}
}
for(j=0; j<N; j++){
for(k=0;k<N;k++){
arrayOld[N-1][j][k] = 100.0;
mean += arrayOld[N-1][j][k];
}
}
for(i=0; i<N; i++){
for(k=0;k<N;k++){
arrayOld[i][0][k] = 100.0;
mean += arrayOld[i][0][k];
}
}
for(i=0; i<N; i++){
for(k=0;k<N;k++){
arrayOld[i][N-1][k] = 0.0;
mean += arrayOld[i][N-1][k];
}
}
mean /= (6.0 * (N*N));
/*Initialize interior values*/
for(i=1; i<N-1; i++){
for(j=1; j<N-1; j++){
for(k=1; k<N-1;k++){
arrayOld[i][j][k] = mean;
}
}
}
double tdata = omp_get_wtime();
compute_heat_transfer(arrayOld, arrayNew);
tdata = omp_get_wtime()-tdata;
printf("Execution time was %f secs\n", tdata);
for(i=0; i<N;i++){
for(int j=0;j<N;j++){
free(arrayOld[i][j]);
free(arrayNew[i][j]);
}
free(arrayOld[i]);
free(arrayNew[i]);
}
free(arrayOld);
free(arrayNew);
return 0;
}
【问题讨论】：
标签： c gcc gpu openmp offloading