Question

This is program for matrix multiplication on CUDA architecture.
This code is working fine when size of array is 30 x 30 but giving output as a series of 0’s when size is greater.
I am using standard ec2 instance for CUDA hosted on linux machine. Can anybody figure out the reason ?

#include <stdio.h>
#define SIZE 30

__global__ void matrix_multiply(float *input1,float  *input2,float *output,int dimension){


    int input1_index = threadIdx.x / dimension * dimension;
    int input2_index =  threadIdx.x % dimension;
    int i=0;
    for( i =0; i <dimension; i++){
        output[threadIdx.x] += input1[input1_index + i] * input2[input2_index + i * dimension];
    }
}
int main(){
    int i,j,natural_number=1;
    float input1[SIZE][SIZE],input2[SIZE][SIZE],result[SIZE][SIZE]={0};
    float  *c_input1,*c_input2,*c_result;
    for(i=0;i<SIZE;i++){
        for(j=0;j<SIZE;j++){
            input1[i][j]=input2[i][j]=natural_number++;
        }
    }
    cudaMalloc((void**)&c_input1,sizeof(input1));
    cudaMalloc((void**)&c_input2,sizeof(input2));
    cudaMalloc((void**)&c_result,sizeof(result));
    cudaMemcpy(c_input1,input1,sizeof(input1),cudaMemcpyHostToDevice);
    cudaMemcpy(c_input2,input2,sizeof(input2),cudaMemcpyHostToDevice);
    cudaMemcpy(c_result,result,sizeof(result),cudaMemcpyHostToDevice);

    matrix_multiply<<<1,SIZE * SIZE>>>(c_input1,c_input2,c_result,SIZE);
    if(cudaGetLastError()!=cudaSuccess){
        printf("%s\n",cudaGetErrorString(cudaGetLastError()));
    }
    cudaMemcpy(result,c_result,sizeof(result),cudaMemcpyDeviceToHost);
    for(i=0;i<SIZE;i++){
        for(j=0;j<SIZE;j++){
            printf("%.2f ",result[i][j]);
        }
        printf("\n");
    }
    cudaFree(c_input1);
    cudaFree(c_input2);
    cudaFree(c_result); 
    return 0;
}

Answer 1

You probably have a max of 1024 threads per block on your GPU. 30 x 30 = 900, so that should be OK, but e.g. 40 x 40 would results in a kernel launch failure (take-home message: always check for errors !).

You probably want to consider organizing your data differently, e.g. SIZE blocks of SIZE threads and then call the kernel as:

matrix_multiply<<<SIZE, SIZE>>>(c_input1,c_input2,c_result,SIZE);

(Obviously you’ll need to modify your array indexing within the kernel code, e.g. use the block index as the row and the thread index as the column.)