Hello World Matrix Adder program Add two matrix ( used shared memory for optimum performance)

Accelerated Computing CUDA CUDA Programming and Performance
1

Hi all,

This is my first hello World program! Its running greatly on Tesla GPU platform. I found some problem with the N=1000. Can any one clarify whats wrong with the code.

[codebox]/*

Program to compute the sum of two arrays of size N using GPU

@author Sajan Kumar.S

@email: nospam+ammasajan[A.T]gmail

  • com

    */

    #include <stdio.h>

    #include <stdlib.h>

    #define N 20 // 20 elements

    global void vecAdd(int *A, int *B, int *C){

         int i=threadIdx.x;

    shared int s_A[N],s_B[N],s_C[N];

    // copy the values to shared mem and attack! :D

    s_A[i]=A[i];

        s_B[i]=B[i];

    __syncthreads();

    // C[i]=A[i]+B[i];

    // s_C[i]=s_A[i]+s_B[i]; // to calucate the sume of elements

        s_C[i]=s_A[i]*s_B[i]; // to caluclate the sume of elements

    __syncthreads();

    C[i]=s_C[i];

    }

    int main(){

    int *h_a=0,*h_b=0,*h_c=0;

        int *d_a=0,*d_b=0,*d_c=0;

    int memSize=N*sizeof(int);

    // allocate host memory size of N

        h_a=(int *)malloc(memSize);

    h_b=(int *)malloc(memSize);

    h_c=(int *)malloc(memSize);

    // allocate GPU memory size of N

        cudaMalloc((void **)&d_a,memSize);

    cudaMalloc((void **)&d_b,memSize);

    cudaMalloc((void **)&d_c,memSize);

    // Init values to A and B arrays(clearing C array)

        for(int i=0;i<N;i++){

            h_a[i]=i+2;

            h_b[i]=i+3;

            h_c[i]=0;

    }

    // Copied the values to GPU arrays A and B

        cudaMemcpy(d_a,h_a,memSize,cudaMemcpyHostToDevice);

    cudaMemcpy(d_b,h_b,memSize,cudaMemcpyHostToDevice);

    // printing the A array and B array on CPU

        printf("\n Array A : \n");

    for(int i=0;i<N;i++)

            printf("%d\t",h_a[i]);

    printf("\n Array B : \n");

    for(int i=0;i<N;i++)

            printf("%d\t",h_b[i]);

    printf("\ncalucalting Sum : ");

    vecAdd<<<1, N>>>(d_a,d_b,d_c);

    // copying the output C from GPU to mem

        cudaMemcpy(h_c,d_c,memSize,cudaMemcpyDeviceToHost);

    printf(“\nSum of Arrays: \n”);

        for(int i=0;i<N;i++)

            printf("%d\t",h_c[i]);

    cudaFree(d_a);

        cudaFree(d_b);

    cudaFree(d_c);

    free(h_a);

        free(h_b);

    free(h_c);

    return 1;

    }

                                                                   [/codebox]
    • 2

    The CUDA block size limit is 512. This:

    vecAdd<<<1, N>>>(d_a,d_b,d_c);

    will not work for N>512. You will have to enlarge the grid for larger vectors.

    3

    Calculate array indices like this when you have more than one block:

    int idx = blockDim.x, blockIdx.x + threadIdx.x;

    or you can do:

    int idx = __umul24( blockDim.x, blockIdx.x) + threadIdx.x;

    as this will be faster !

    Some beginners CUDA tricks can be found here

    4

    The use of shared memory in this program is also unnecessary. Shared memory is useful when you need to use a value more than once, or possibly read elements and operate on them in a different order. Here each thread reads an element from A, one from B and writes the sum (actually you have the product uncommented) to C. That can be done directly with no syncthreads and no shared memory storage.