Unable to execute device kernel in CUDA - cuda

Question

I am trying to call a device kernel within a global kernel. My global kernel is a Matrix Multiplication and my device kernel is finding the maximum value and the index in each column of the product matrix. Following is the code :
__device__ void MaxFunction(float* Pd, float* max)
{
int x = (threadIdx.x + blockIdx.x * blockDim.x);
int y = (threadIdx.y + blockIdx.y * blockDim.y);
int k = 0;
int temp = 0; int temp_idx = 0;
for (k = 0; k < wB; ++k) {
if(Pd[x*wB + y] > temp){
temp = Pd[x*wB + y];
temp_idx = x*wB + y;
}
max[y*2 + 0] = temp;
max[y*2 + 1] = temp_idx;
}
}
__global__ void MatrixMulKernel(float* Md, float* Nd, float* Pd, float* max)
{
// declare cache in the shared memory
__shared__ float Mds[blockD][blockD];
__shared__ float Nds[blockD][blockD];
float Pvalue = 0;
// Loop over the Md and Nd block dimension required to compute the Pd element
for (int m = (wA * blockD * blockIdx.y), n = (blockD * blockIdx.x);
m < ((wA * blockD * blockIdx.y)+wA-1);
m += blockD, n += (blockD*hB)){
// collaboratively loading of Md and Nd blocks into shared memory
Mds[threadIdx.y][threadIdx.x] = Md[m + wA * threadIdx.y + threadIdx.x];
Nds[threadIdx.y][threadIdx.x] = Nd[n + wA * threadIdx.y + threadIdx.x];
__syncthreads();
// keep track of the running sum
for (int k = 0; k < blockD; k++)
Pvalue += Mds[threadIdx.y][k] * Nds[k][threadIdx.x];
__syncthreads();
}
// write back to the global memory
int p = hB * blockD * blockIdx.y + blockD * blockIdx.x;
Pd[p + hB * threadIdx.y + threadIdx.x] = Pvalue;
__syncthreads();
MaxFunction(Pd, max);
}
The Main code :
#include<stdio.h>
#include "cuda.h"
#include<stdlib.h>
#define blockD 32
const int wA = 128;
const int hA = 1024;
const int wB = 128;
const int hB = wA;
main(void){
void MatrixMultiplication(float *, float *, float *, float *);
int size_A = wA * hA * sizeof(float);
int size_B = wB * hB * sizeof(float);
int size_C = wB * hA * sizeof(float);
int size_max = 2 * wB * sizeof(float);
float *M, *N, *P, *C;
// allocate memory on the CPU
M = (float*)malloc(size_A);
N = (float*)malloc(size_B);
P = (float*)malloc(size_max);
C = (float*)malloc(size_C);
// initialize the matrices
for (int y=0; y < hA; y++) {
for (int x=0; x < wA; x++){
M[y*wA + x] = x;
}
}
for (int y=0; y<hB; y++) {
for (int x=0; x<wB; x++){
N[y*wB + x] = x;
}
}
MatrixMultiplication(M, N, P, C);
//Write
FILE *f1;
int i, j;
f1 = fopen("max_val.txt","w");
for(i=0; i < (wB * 2); i+=2){
fprintf(f1,"%d\t%d\n",int(P[i]),int(P[i+1]));
}
fclose(f1);
f1 = fopen("Prod_mat.txt","w");
for(i=0; i < 2; i++){
for(j=0; j < wB; j++){
fprintf(f1,"%d\t",int(C[i*wB + j]));
}
fprintf(f1,"\n");
}
fclose(f1);
free( M );
free( N );
free( P );
free( C );
cudaDeviceReset();
return 0;
}
void MatrixMultiplication(float *M, float *N, float *P, float *C) {
int size_A = wA * hA * sizeof(float);
int size_B = wB * hB * sizeof(float);
int size_C = wB * hA * sizeof(float);
int size_max = 2 * wB * sizeof(float);
float *Md, *Nd, *Pd, *max;
// allocate memory on the GPU
cudaMalloc((void**)&Md, size_A);
cudaMalloc((void**)&Nd, size_B);
cudaMalloc((void**)&Pd, size_C);
cudaMalloc((void**)&max, size_max);
// transfer M and N to device memory
cudaMemcpy(Md, M, size_A, cudaMemcpyHostToDevice);
cudaMemcpy(Nd, N, size_B, cudaMemcpyHostToDevice);
// kernel invocation code
dim3 dimBlock(blockD, blockD);
dim3 dimGrid(wA/blockD, hB/blockD);
//Execute Kernel
MatrixMulKernel<<<dimGrid, dimBlock>>>( Md, Nd, Pd, max);
// transfer P from device
cudaMemcpy(P, max, size_max, cudaMemcpyDeviceToHost);
cudaMemcpy(C, Pd, size_C, cudaMemcpyDeviceToHost);
cudaFree(Md);
cudaFree(Nd);
cudaFree(Pd);
cudaFree(max);
}
The Matrix Multiplication result is fine (Verified using Matlab), but I am not able to get the max values and their corresponding index. I would appreciate if anyone can kindly point out at what I am doing wrong. The max variable has only garbage when I run the above code.

Answer 1

Apparently you are attempting to find the maximum value in each column, as well as the offset to that value.
But all of your threads in y are hammering on the same location for max value (max[x*2 + 0]). This isn't recommended, as there is no way to sort out a race condition. You should use atomic operations, or other methods (e.g. reduction) to handle multiple threads updating a single max value this way.
Since you have a need to update two values atomically (the max value and it's location), it's not a simple matter of replacing your plain access with a standard atomic function. However, since you are dealing with two 32-bit adjacent quantities, you may be interested in my answer here.
By the way I think matlab's native matrix multiply on gpuArray should be faster than any matrix multiply code you write. But it would require the Parallel Compute Toolbox.