dependencies/cub/examples/block/example_block_reduce_dyn_smem.cu

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/******************************************************************************
 * Simple demonstration of cub::BlockReduce with dynamic shared memory
 *
 * To compile using the command line:
 *   nvcc -arch=sm_XX example_block_reduce_dyn_smem.cu -I../.. -lcudart -O3 -std=c++14
 *
 ******************************************************************************/

// Ensure printing of CUDA runtime errors to console (define before including cub.h)
#define CUB_STDERR

#include <stdio.h>
#include <algorithm>
#include <iostream>

#include <cub/block/block_load.cuh>
#include <cub/block/block_store.cuh>
#include <cub/block/block_reduce.cuh>

#include "../../test/test_util.h"

// Some implementation details rely on c++14
#if CUB_CPP_DIALECT >= 2014

using namespace cub;

//---------------------------------------------------------------------
// Globals, constants and typedefs
//---------------------------------------------------------------------

/// Verbose output
bool g_verbose = false;

/// Default grid size
int g_grid_size = 1;

//---------------------------------------------------------------------
// Kernels
//---------------------------------------------------------------------

/**
 * Simple kernel for performing a block-wide reduction.
 */
template <int BLOCK_THREADS>
__global__ void BlockReduceKernel(
    int         *d_in,          // Tile of input
    int         *d_out          // Tile aggregate
    )
{
    // Specialize BlockReduce type for our thread block
    using BlockReduceT = cub::BlockReduce<int, BLOCK_THREADS>;
    using TempStorageT = typename BlockReduceT::TempStorage;

    union ShmemLayout
    {
      TempStorageT reduce;
      int aggregate;
    };

    // shared memory byte-array
    extern __shared__ __align__(alignof(ShmemLayout)) char smem[];
    
    // cast to lvalue reference of expected type
    auto& temp_storage = reinterpret_cast<TempStorageT&>(smem);

    int data = d_in[threadIdx.x];

    // Compute sum
    int aggregate = BlockReduceT(temp_storage).Sum(data);

    // block-wide sync barrier necessary to re-use shared mem safely
    __syncthreads();
    int* smem_integers = reinterpret_cast<int*>(smem);
    if (threadIdx.x == 0) smem_integers[0] = aggregate;

    // sync to make new shared value available to all threads
    __syncthreads();
    aggregate = smem_integers[0];

    // all threads write the aggregate to output
    d_out[threadIdx.x] = aggregate;
}



//---------------------------------------------------------------------
// Host utilities
//---------------------------------------------------------------------

/**
 * Initialize reduction problem (and solution).
 * Returns the aggregate
 */
int Initialize(int *h_in, int num_items)
{
    int inclusive = 0;

    for (int i = 0; i < num_items; ++i)
    {
        h_in[i] = i % 17;
        inclusive += h_in[i];
    }

    return inclusive;
}

/**
 * Test thread block reduction
 */
template <int BLOCK_THREADS>
void Test()
{
    // Allocate host arrays
    int *h_in = new int[BLOCK_THREADS];

    // Initialize problem and reference output on host
    int h_aggregate = Initialize(h_in, BLOCK_THREADS);

    // Initialize device arrays
    int *d_in           = NULL;
    int *d_out          = NULL;
    cudaMalloc((void**)&d_in,          sizeof(int) * BLOCK_THREADS);
    cudaMalloc((void**)&d_out,         sizeof(int) * BLOCK_THREADS);

    // Display input problem data
    if (g_verbose)
    {
        printf("Input data: ");
        for (int i = 0; i < BLOCK_THREADS; i++)
            printf("%d, ", h_in[i]);
        printf("\n\n");
    }

    // Copy problem to device
    cudaMemcpy(d_in, h_in, sizeof(int) * BLOCK_THREADS, cudaMemcpyHostToDevice);

    // determine necessary storage size:
    auto block_reduce_temp_bytes =
      sizeof(typename cub::BlockReduce<int, BLOCK_THREADS>::TempStorage);
    // finally, we need to make sure that we can hold at least one integer
    // needed in the kernel to exchange data after reduction
    auto smem_size = (std::max)(1 * sizeof(int), block_reduce_temp_bytes);

    // use default stream
    cudaStream_t stream = NULL;

    // Run reduction kernel
    BlockReduceKernel<BLOCK_THREADS>
        <<<g_grid_size, BLOCK_THREADS, smem_size, stream>>>(
            d_in,
            d_out);

    // Check total aggregate
    printf("\tAggregate: ");
    int compare = 0;
    for (int i = 0; i < BLOCK_THREADS; i++) {
        compare = compare || CompareDeviceResults(
            &h_aggregate, d_out + i, 1, g_verbose, g_verbose);
    }
    printf("%s\n", compare ? "FAIL" : "PASS");
    AssertEquals(0, compare);

    // Check for kernel errors and STDIO from the kernel, if any
    CubDebugExit(cudaPeekAtLastError());
    CubDebugExit(cudaDeviceSynchronize());

    // Cleanup
    if (h_in) delete[] h_in;
    if (d_in) cudaFree(d_in);
    if (d_out) cudaFree(d_out);
}


/**
 * Main
 */
int main(int argc, char** argv)
{
    // Initialize command line
    CommandLineArgs args(argc, argv);
    g_verbose = args.CheckCmdLineFlag("v");
    args.GetCmdLineArgument("grid-size", g_grid_size);

    // Print usage
    if (args.CheckCmdLineFlag("help"))
    {
        printf("%s "
            "[--device=<device-id>] "
            "[--grid-size=<grid size>] "
            "[--v] "
            "\n", argv[0]);
        exit(0);
    }

    // Initialize device
    CubDebugExit(args.DeviceInit());

    // Run tests
    Test<1024>();
    Test<512>();
    Test<256>();
    Test<128>();
    Test<64>();
    Test<32>();
    Test<16>();

    return 0;
}

#else // < C++14

int main() {}

#endif