cublas_GemmStridedBatchedEx_example.cu

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#include <cstdio>
#include <cstdlib>
#include <vector>

#include <cublas_v2.h>
#include <cuda_runtime.h>

#include "cublas_utils.h"

using data_type = double;

int main(int argc, char *argv[]) {
    cublasHandle_t cublasH = NULL;
    cudaStream_t stream = NULL;

    const int m = 2;
    const int n = 2;
    const int k = 2;
    const int lda = 2;
    const int ldb = 2;
    const int ldc = 2;
    const int batch_count = 2;

    const long long int strideA = m * k;
    const long long int strideB = k * n;
    const long long int strideC = m * n;

    /*
     *   A = | 1.0 | 2.0 | 5.0 | 6.0 |
     *       | 3.0 | 4.0 | 7.0 | 8.0 |
     *
     *   B = | 5.0 | 6.0 |  9.0 | 10.0 |
     *       | 7.0 | 8.0 | 11.0 | 12.0 |
     */

    const std::vector<data_type> A = {1.0, 3.0, 2.0, 4.0, 5.0, 7.0, 6.0, 8.0};
    const std::vector<data_type> B = {5.0, 7.0, 6.0, 8.0, 9.0, 11.0, 10.0, 12.0};
    std::vector<data_type> C(m * n * batch_count);
    const data_type alpha = 1.0;
    const data_type beta = 0.0;

    data_type *d_A = nullptr;
    data_type *d_B = nullptr;
    data_type *d_C = nullptr;

    cublasOperation_t transa = CUBLAS_OP_N;
    cublasOperation_t transb = CUBLAS_OP_N;

    printf("A[0]\n");
    print_matrix(m, k, A.data(), lda);
    printf("=====\n");

    printf("A[1]\n");
    print_matrix(m, k, A.data() + (m * k), lda);
    printf("=====\n");

    printf("B[0]\n");
    print_matrix(k, n, B.data(), ldb);
    printf("=====\n");

    printf("B[1]\n");
    print_matrix(k, n, B.data() + (k * n), ldb);
    printf("=====\n");

    /* step 1: create cublas handle, bind a stream */
    CUBLAS_CHECK(cublasCreate(&cublasH));

    CUDA_CHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
    CUBLAS_CHECK(cublasSetStream(cublasH, stream));

    /* step 2: copy data to device */
    CUDA_CHECK(cudaMalloc(reinterpret_cast<void **>(&d_A), sizeof(data_type) * A.size()));
    CUDA_CHECK(cudaMalloc(reinterpret_cast<void **>(&d_B), sizeof(data_type) * B.size()));
    CUDA_CHECK(cudaMalloc(reinterpret_cast<void **>(&d_C), sizeof(data_type) * C.size()));

    CUDA_CHECK(cudaMemcpyAsync(d_A, A.data(), sizeof(data_type) * A.size(), cudaMemcpyHostToDevice,
                               stream));
    CUDA_CHECK(cudaMemcpyAsync(d_B, B.data(), sizeof(data_type) * B.size(), cudaMemcpyHostToDevice,
                               stream));

    /* step 3: compute */
    CUBLAS_CHECK(cublasGemmStridedBatchedEx(
        cublasH, transa, transb, m, n, k, &alpha, d_A, traits<data_type>::cuda_data_type, lda,
        strideA, d_B, traits<data_type>::cuda_data_type, ldb, strideB, &beta, d_C,
        traits<data_type>::cuda_data_type, ldc, strideC, batch_count, CUBLAS_COMPUTE_64F,
        CUBLAS_GEMM_DEFAULT_TENSOR_OP));

    /* step 4: copy data to host */
    CUDA_CHECK(cudaMemcpyAsync(C.data(), d_C, sizeof(data_type) * C.size(), cudaMemcpyDeviceToHost,
                               stream));

    CUDA_CHECK(cudaStreamSynchronize(stream));

    /*
     *   C = | 19.0 | 22.0 | 111.0 | 122.0 |
     *       | 43.0 | 50.0 | 151.0 | 166.0 |
     */

    printf("C[0]\n");
    print_matrix(m, n, C.data(), ldc);
    printf("=====\n");

    printf("C[1]\n");
    print_matrix(m, n, C.data() + (m * n), ldc);
    printf("=====\n");

    /* free resources */
    CUDA_CHECK(cudaFree(d_A));
    CUDA_CHECK(cudaFree(d_B));
    CUDA_CHECK(cudaFree(d_C));

    CUBLAS_CHECK(cublasDestroy(cublasH));

    CUDA_CHECK(cudaStreamDestroy(stream));

    CUDA_CHECK(cudaDeviceReset());

    return EXIT_SUCCESS;
}