# Cuda : Reduce (max/min) function on matrix implementation

So I am learning cuda c++ and now i’m implementing a reduce function on a matrix : find the max value in a matrix. I have implemented a function to get the max on an array and transforming it to a matrix version should be straightforward but I can’t get it to work (I don’t get the correct result). I am wondering if this is the right approach. You can find the code for the two versions below :

For an array :

``````__global__
void reduce_kernal_shared_mem(float *d_in, float *d_out){
int indx = blockDim.x * blockIdx.x + threadIdx.x;

extern __shared__ float sh_in[];

sh_in[tindx] = -99999.0f;

sh_in[tindx] = d_in[indx];

for(int i = blockDim.x / 2; i > 0; i >>= 1){
if(tindx < i){
sh_in[tindx] = fmax(sh_in[tindx], sh_in[tindx + i]);
}
}

if(tindx == 0){
d_out[blockIdx.x] = sh_in[0];
}
}

void reduce(float *d_in, float *d_int, float *d_out, const int ARRAY_SIZE){
reduce_kernal_shared_mem<<<1024, 1024, 1024 * sizeof(float)>>>(d_in, d_int);
reduce_kernal_shared_mem<<<1, 1024, 1024 * sizeof(float)>>>(d_int, d_out);
}
``````

For a matrix :

``````__global__
void get_max(const float* d_logLuminance, float *d_out, int numRows, int numCols){
int col = blockIdx.x * blockDim.x + threadIdx.x;
int row = blockIdx.y * blockDim.y + threadIdx.y;
int pos_1D = row * numCols + col;
int pos_1D_t = r_t * blockDim.x + c_t;

extern __shared__ float sh_mem[];

sh_mem[pos_1D_t] = -999999.0f;

if(pos_1D > numCols * numRows)
return;

sh_mem[pos_1D_t] = d_logLuminance[pos_1D];

for(int s = (blockDim.x * blockDim.y) / 2; s > 0; s >>= 1){
if(pos_1D_t < s)
sh_mem[pos_1D_t] = fmax(sh_mem[pos_1D_t], sh_mem[pos_1D_t + s]);
}

if(r_t == 0 && c_t == 0)
d_out[blockIdx.y * gridDim.x + blockIdx.x] = sh_mem[0];
}

void max(const float *d_logLuminance, int numRows, int numCols, float &max_logLum){

float *d_out, *d_int;
cudaMalloc(&d_out, sizeof(float) * numRows * numCols);
cudaMalloc(&d_int, sizeof(float) * numRows * numCols);

cudaMemcpy(&max_logLum, d_out, sizeof(float), cudaMemcpyDeviceToHost);

printf("max : %f\n", max_logLum);

cudaFree(d_out);
cudaFree(d_int);

}
``````

I could just launch a kernel with 1-dimensional blocksize and gridsize with number of threads = numRows * numCols and work on the matrix as an array (without the indexing tricks) and it works but I would like to know why my approach won’t.

Thank you.

https://stackoverflow.com/questions/57573872/cuda-reduce-max-min-function-on-matrix-implementation

If you want people to respond to posts like this, in my experience you’re more likely to get attention if you provide a complete code. That is something that I or someone could copy, paste, compile, and run, without having to add anything or change anything. Don’t make someone else write a main routine, or guess at the sizes of the data set you are actually using.