Explanation for change in output of CUDA code by changing number of threads

Question

I wanted to determine how many numbers of the form x^2+1 are prime, for 1 <= x <= 10^7. I just wanted to parallelize it with CUDA and check the difference, so I used trivial primality check and I'm not concerned with improving its algorithm.

I arranged a grid, and sled it over my interval, record the results in shared memory of each block, performed a reduction on gpu over each block and finally performed a reduction in cpu to get the final result.

My problem is that the output result changes when I change the number of blocks and number of threads in each block. Another thing I can not explain is that, for a config of 8 blocks and 2048 threads per block, code runs in under 100ms, but when I reduce the number of threads to 1024 and double the number of blocks, the code will cause a timeout in memcpy from device to host!! How can I explain this behaviour and where the correctness falls into problem?

I'm using a GTX 480 nvidia gpu.

My Code is:

#include <stdio.h>
static void HandleError( cudaError_t err, const char *file, int line )
{
    if (err != cudaSuccess) {
        printf( "%s in %s at line %d\n", cudaGetErrorString( err ), file, line );
        exit( EXIT_FAILURE );
    }
}

#define HANDLE_ERROR( err ) (HandleError( err, __FILE__, __LINE__ ))
#define N 10000000
#define BLOCKS 8
#define THREADS 2048

__device__ int isprime(int x)
{
    long long n = (long long)x*x + 1;
    for( int p=3; p<=x+1; p+=2 )
        if ( n % p == 0 ) return 0;
    return 1;
}

__global__ void solve(int n, int* result)
{
    __shared__ int ipc[THREADS];

    int tid = threadIdx.x;
    int x = blockIdx.x*blockDim.x + threadIdx.x + 2;

    // sliding grid window over interval of to-be-computed data
    int acc = 0;
    while( x <= n )
    {
        if ( isprime(x) ) acc++;
        x += blockDim.x*gridDim.x;
    }
    ipc[tid] = acc;
    __syncthreads();


    // reduction over each block in parallel
    for( int s=blockDim.x/2; s>0; s>>=1 )
    {
        if ( tid < s )
        {
            ipc[tid] += ipc[tid+s];
        }
        __syncthreads();
    }

    if ( tid == 0 ) result[blockIdx.x] = ipc[0];
}

int main()
{
    int *dev;
    int res[BLOCKS];

    int ans = 0;

    HANDLE_ERROR( cudaMalloc((void**)&dev, BLOCKS * sizeof(int)) );

    solve<<<BLOCKS, THREADS>>>(N, dev);

    HANDLE_ERROR( cudaMemcpy(res, dev, BLOCKS*sizeof(int), cudaMemcpyDeviceToHost) );

    // final reduction over results for each block
    for( int j=0; j<BLOCKS; j++ )
        ans += res[j];

    printf("ans = %d\n", ans);

    HANDLE_ERROR( cudaFree( dev ) );
    return 0;
}

score 5 · Accepted Answer · edited May 23 '17 at 10:25

You can't run 2048 threads per block on any current GPU:

#define THREADS 2048
...
solve<<<BLOCKS, THREADS>>>(N, dev);
                  ^
                  |
                2048 is illegal here

You're not doing proper cuda error checking on the kernel call, so your code won't tell you that this error is occurring.

So in the case of 2048 threads per block, your kernel is not even executing (and your results should be bogus.)

In the case when you cut the threads in half, the timeout is probably due to your kernel taking too long to execute, and the windows TDR mechanism kicks in.

I tried running your code with BLOCKS = 16 and THREADS = 1024

With N = 100000, the total execution time was about 1.5 seconds on my M2050 GPU. With N = 1000000, the execution time was about 75 seconds. With N = 10000000 which is what you have, the execution time is very long.

Explanation for change in output of CUDA code by changing number of threads

1 Answers1