i_have_problem_with_openmp_cuda

why one thread is executed beyond that, I set the number 4, already!

#include <omp.h>
#include <stdio.h> // stdio functions are used since C++ streams aren’t necessarily thread safe
#include <cuda.h>

// a simple kernel that simply increments each array element by b
global void kernelAddConstant(int *g_a, const int b)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
g_a[idx] += b;
}

// a predicate that checks whether each array elemen is set to its index plus b
int correctResult(int *data, const int n, const int b)
{
for(int i = 0; i < n; i++)
if(data[i] != i + b)
return 0;
return 1;
}

int main(int argc, char *argv)
{
int num_gpus = 0; // number of CUDA GPUs

    /////////////////////////////////////////////////////////////////
    // determine the number of CUDA capable GPUs
    //
cudaGetDeviceCount(&num_gpus);
    if(num_gpus < 1)
    {
            printf("no CUDA capable devices were detected\n");
            return 1;
    }

    /////////////////////////////////////////////////////////////////
    // display CPU and GPU configuration
    //
printf("number of host CPUs:\t%d\n", omp_get_num_procs());
printf("number of CUDA devices:\t%d\n", num_gpus);
for(int i = 0; i < num_gpus; i++)
{
    cudaDeviceProp dprop;
    cudaGetDeviceProperties(&dprop, i);
            printf("   %d: %s\n", i, dprop.name);
}
    printf("---------------------------\n");


/////////////////////////////////////////////////////////////////
// initialize data
    //
unsigned int n = num_gpus * 8192;
unsigned int nbytes = n * sizeof(int);
    int *a = 0;             // pointer to data on the CPU
    int b = 3;              // value by which the array is incremented
    a = (int*)malloc(nbytes);
    if(0 == a)
    {
            printf("couldn't allocate CPU memory\n");
            return 1;
    }
    for(unsigned int i = 0; i < n; i++)
    a[i] = i;


////////////////////////////////////////////////////////////////
    // run as many CPU threads as there are CUDA devices
    //   each CPU thread controls a different device, processing its
    //   portion of the data.  It's possible to use more CPU threads
    //   than there are CUDA devices, in which case several CPU
    //   threads will be allocating resources and launching kernels
    //   on the same device.  For example, try omp_set_num_threads(2*num_gpus);
    //   Recall that all variables declared inside an "omp parallel" scope are
    //   local to each CPU thread
    //

omp_set_num_threads(4) ;
#pragma omp parallel
{

	int nth=omp_get_num_threads();
	 /*omp_set_num_threads(2); */ // create as many CPU threads as there are CUDA devices
//omp_set_num_threads(2*num_gpus);// create twice as many CPU threads as there are CUDA devices
	 printf("nnnnnnn % 3.1i \n ",nth);
    unsigned int cpu_thread_id = omp_get_thread_num();
            unsigned int num_cpu_threads = omp_get_num_threads();

            // set and check the CUDA device for this CPU thread
            int gpu_id = -1;
            cudaSetDevice(cpu_thread_id % num_gpus);        // "% num_gpus" allows more CPU threads than GPU devices
            cudaGetDevice(&gpu_id);

            printf("CPU thread %d (of %d) uses CUDA device %d\n", cpu_thread_id, num_cpu_threads, gpu_id);

            int *d_a = 0;   // pointer to memory on the device associated with this CPU thread
            int *sub_a = a + cpu_thread_id * n / num_cpu_threads;   // pointer to this CPU thread's portion of data
            unsigned int nbytes_per_kernel = nbytes / num_cpu_threads;
            dim3 gpu_threads(128);  // 128 threads per block
            dim3 gpu_blocks(n / (gpu_threads.x * num_cpu_threads));

      cudaMalloc((void**)&d_a, nbytes_per_kernel);
      cudaMemset(d_a, 0, nbytes_per_kernel);
      cudaMemcpy(d_a, sub_a, nbytes_per_kernel, cudaMemcpyHostToDevice);
    kernelAddConstant<<<gpu_blocks, gpu_threads>>>(d_a, b);

      cudaMemcpy(sub_a, d_a, nbytes_per_kernel, cudaMemcpyDeviceToHost);
      cudaFree(d_a);


}
    printf("---------------------------\n");

    if(cudaSuccess != cudaGetLastError())
            printf("%s\n", cudaGetErrorString(cudaGetLastError()));


    ////////////////////////////////////////////////////////////////
    // check the result
    //
if(correctResult(a, n, b))
    printf("Test PASSED\n");
else
    printf("Test FAILED\n");

free(a);    // free CPU memory

cudaThreadExit();
system("pause");
return 0;

}

execution:
number of host CPUs: 4
number of CUDA devices: 1
0: GeForce 820M

nnnnnnn 1
CPU thread 0 (of 1) uses CUDA device 0

Test PASSED
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