CUDA:cudaOpenMP

Sample: cudaOpenMP
Minimum spec: SM 3.0

This sample demonstrates how to use OpenMP API to write an application for multiple GPUs.

Key concepts:
CUDA Systems Integration
OpenMP
Multithreading
 

/*
 * Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
 *
 * Please refer to the NVIDIA end user license agreement (EULA) associated
 * with this source code for terms and conditions that govern your use of
 * this software. Any use, reproduction, disclosure, or distribution of
 * this software and related documentation outside the terms of the EULA
 * is strictly prohibited.
 *
 */

/*
 * Multi-GPU sample using OpenMP for threading on the CPU side
 * needs a compiler that supports OpenMP 2.0
 */

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

using namespace std;

// 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 element 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

    printf("%s Starting...\n\n", argv[0]);

    /////////////////////////////////////////////////////////////////
    // 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(num_gpus);  // 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
    #pragma omp parallel
    {
        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;
        checkCudaErrors(cudaSetDevice(cpu_thread_id % num_gpus));   // "% num_gpus" allows more CPU threads than GPU devices
        checkCudaErrors(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));

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

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

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

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


    ////////////////////////////////////////////////////////////////
    // check the result
    //
    bool bResult = correctResult(a, n, b);

    if (a)
        free(a); // free CPU memory

    exit(bResult ? EXIT_SUCCESS : EXIT_FAILURE);
}