Home > Programming, Technology > OpenCL GPU Matrix multiplication program

## OpenCL GPU Matrix multiplication program

Here is the code to multiply two matrices using heterogeneous system programming language OpenCL. The reason being called OpenCL as heterogeneous is that written a code in OpenCL can be ported to CPU or GPU or Cell processor.

```
//Author: Vasanth Raja
//Program to multiply two matrices using OpenCL in GPU

#include "stdafx.h"

#include < stdio.h >
#include < stdlib.h >
#include < time.h >
#include < ctime >

#define widthA 128
#define heightA 128

#define widthB heightA
#define heightB 128

#define widthC widthA
#define heightC heightB

#ifdef __APPLE__
#include < OpenCL/opencl.h >
#else
#include < CL/cl.h >
#endif

#define MEM_SIZE (128)
#define MAX_SOURCE_SIZE (0x100000)

int main()
{
float * A = (float *)malloc(sizeof(float)*widthA*heightA);
float * B = (float *)malloc(sizeof(float)*widthB*heightB);
float * C = (float *)malloc(sizeof(float)*widthC*heightC);
float * Res = (float *)malloc(sizeof(float)*widthC*heightC);
float * D= (float *)malloc(sizeof(float)*widthC*heightC);

FILE * fp1 = fopen("matAdata.txt", "w");
if (!fp1) {
exit(1);
}

for(int i = 0;i < widthA; i++)
{
for(int j=0;j		{
float p=(rand()%100)/7.0;
*(A+i*heightA+j)=rand()%100 + p;
fprintf(fp1, "%f ",*(A+i*heightA+j));
}
fprintf(fp1, "\n");
}
fclose(fp1);

fp1 = fopen("matBdata.txt", "w");
if (!fp1) {
exit(1);
}

for(int i = 0;i < widthB; i++)
{
for(int j=0; j		{
float p=(rand()%100)/7.0;
*((B+i*heightB+j))=rand()%100 + p;
fprintf(fp1, "%f ",*(B+i*heightA+j));
}
fprintf(fp1, "\n");
}
fclose(fp1);

cl_device_id device_id = NULL;
cl_context context = NULL;
cl_command_queue command_queue = NULL;
cl_mem memobjA = NULL;
cl_mem memobjB = NULL;
cl_mem memobjC = NULL;
cl_mem rowA = NULL;
cl_mem colC = NULL;
cl_program program = NULL;
cl_kernel kernel = NULL;
cl_platform_id platform_id = NULL;
cl_uint ret_num_devices;
cl_uint ret_num_platforms;
cl_int ret;

//char string[MEM_SIZE];

FILE *fp;
char fileName[] = "./hello.cl";
char *source_str;
size_t source_size;
int row = widthA;
int col = heightC;
/* Load the source code containing the kernel*/
fp = fopen(fileName, "r");
if (!fp) {
exit(1);
}
source_str = (char*)malloc(MAX_SOURCE_SIZE);
source_size = fread( source_str, 1, MAX_SOURCE_SIZE, fp);
fclose( fp );

/* Get Platform and Device Info */
ret = clGetPlatformIDs(1, &platform_id, &ret_num_platforms);
ret = clGetDeviceIDs( platform_id, CL_DEVICE_TYPE_GPU, 1, &device_id, &ret_num_devices);

/* Create OpenCL context */
context = clCreateContext( NULL, 1, &device_id, NULL, NULL, &ret);

/* Create Command Queue */
command_queue = clCreateCommandQueue(context, device_id, 0, &ret);

/* Create Memory Buffer */
memobjA = clCreateBuffer(context, CL_MEM_READ_WRITE, widthA * heightA * sizeof(float), NULL, &ret);
memobjB = clCreateBuffer(context, CL_MEM_READ_WRITE, widthB * heightB * sizeof(float), NULL, &ret);
memobjC = clCreateBuffer(context, CL_MEM_READ_WRITE, widthC * heightC * sizeof(float), NULL, &ret);
rowA = clCreateBuffer(context, CL_MEM_READ_WRITE,  sizeof(int), NULL, &ret);
colC = clCreateBuffer(context, CL_MEM_READ_WRITE,  sizeof(int), NULL, &ret);

// Copy the lists A and B to their respective memory buffers
ret = clEnqueueWriteBuffer(command_queue,memobjA, CL_TRUE, 0,
widthA * heightA * sizeof(int), A, 0, NULL, NULL);
ret = clEnqueueWriteBuffer(command_queue, memobjB, CL_TRUE, 0,
widthB * heightB * sizeof(int), B, 0, NULL, NULL);
ret = clEnqueueWriteBuffer(command_queue, rowA, CL_TRUE, 0, sizeof(int), &row, 0, NULL, NULL);
ret = clEnqueueWriteBuffer(command_queue, colC, CL_TRUE, 0, sizeof(int), &col, 0, NULL, NULL);

/* Create Kernel Program from the source */
program = clCreateProgramWithSource(context, 1, (const char **)&source_str,
(const size_t *)&source_size, &ret);

/* Build Kernel Program */
ret = clBuildProgram(program, 1, &device_id, NULL, NULL, NULL);

/* Create OpenCL Kernel */
kernel = clCreateKernel(program, "matrixMultiplication", &ret);

/* Set OpenCL Kernel Arguments */
ret = clSetKernelArg(kernel, 0, sizeof(cl_mem), (void *)&memobjA);
ret = clSetKernelArg(kernel, 1, sizeof(cl_mem), (void *)&memobjB);
ret = clSetKernelArg(kernel, 2, sizeof(cl_mem), (void *)&memobjC);
//ret = clSetKernelArg(kernel, 0, sizeof(cl_mem), (void *)&memobjA);
ret = clSetKernelArg(kernel, 3, sizeof(int), (void *)&row);
ret = clSetKernelArg(kernel, 4, sizeof(int), (void *)&col);
/* Execute OpenCL Kernel */
//ret = clEnqueueTask(command_queue, kernel, 0, NULL,NULL);

/* Copy results from the memory buffer */
ret = clEnqueueReadBuffer(command_queue, memobjC, CL_TRUE, 0,
widthA * heightC * sizeof(float),Res, 0, NULL, NULL);

fp1 = fopen("matGPURes.txt", "w");
if (!fp1) {
exit(1);
}

printf("\nResult\n");
for(int i = 0;i < widthA; i++)
{
for(int j=0;j < heightC; j++)
{

fprintf(fp1, "%f ",*(Res+i*heightC+j));

}
fprintf(fp1, "\n");
}
fclose(fp1);

ret = clFlush(command_queue);
ret = clFinish(command_queue);
ret = clReleaseKernel(kernel);
ret = clReleaseProgram(program);
ret = clReleaseMemObject(memobjA);
ret = clReleaseMemObject(memobjB);
ret = clReleaseMemObject(memobjC);
ret = clReleaseCommandQueue(command_queue);
ret = clReleaseContext(context);

free(source_str);
system("pause");

float sum=0.0;

for(int i = 0;i < widthA; i++)
{
for(int j = 0; j < heightC; j++)
{
sum = 0;
for(int k = 0; k < widthB; k++)
{
sum += A[i*col+k] * B[k*row+j];
}
D[i*heightC+j] = sum;
}

}

fp1 = fopen("matNormalMultiplicationRes.txt", "w");
if (!fp1) {
exit(1);
}

printf("\nResult\n");
for(int i = 0;i < widthA; i++)
{
for(int j=0;j < heightC; j++)
{
fprintf(fp1, "%f ",*(D+i*heightC+j));

}
fprintf(fp1, "\n");
}
system("pause");
return 0;
}
```

You can check configuration and set up in Visual studio here.

The actual Kernel executed in the GPU is as follows.

```__kernel
void matrixMultiplication(__global float* A, __global float* B, __global float* C,  int widthA, int widthB )
{
int i = get_global_id(0);
int j = get_global_id(1);
float value=0;
for ( int k = 0; k < widthA; k++)
{
value = value + A[k + j * widthA] * B[k*widthB + i];
}
C[i + widthA * j] = value;
}

```

1. January 17, 2012 at 10:01 pm

Thank you very much!!! I learned a lot from it!!!

Could you please upload code for matrix inversion!!!! or give at least a hint!!!

2. February 23, 2013 at 1:58 pm

thanks for this code. i have a query. when i used few days back this code on my w8, visual studio, it was working well. but since today the program runs too slowly for executing this lines
float p=(rand()%100)/7.0;
*(A+i*heightA+j)=rand()%100 + p;
fprintf(fp1, “%f “,*(A+i*heightA+j));

and also program is not finishing. command console just remains open. can u suggest any solution?

3. January 17, 2014 at 10:49 pm

Useful solution. In line 45 and 62 you have a bug – missing elements of for loop.

For me it’s works correctly, but I getting following output:

Result

Result

4. January 19, 2014 at 5:15 pm

Cool code thx. Hovewer I tried to implement it in vs 2012 and got two errors.

1.First:
for(int i = 0;i < widthA; i++)
{
for(int j=0;j { //I think you forgot here the hightA
float p=(rand()%100)/7.0;
*(A+i*heightA+j)=rand()%100 + p;
fprintf(fp1, "%f ",*(A+i*heightA+j));
}
fprintf(fp1, "\n");
}

2.Second:
for(int i = 0;i < widthB; i++)
{
for(int j=0; j { //Here the same thing
float p=(rand()%100)/7.0;
*((B+i*heightB+j))=rand()%100 + p;
fprintf(fp1, "%f ",*(B+i*heightA+j));
}
fprintf(fp1, "\n");
}

Best regards Alex

1. November 20, 2011 at 12:28 pm