/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *  * Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 *  * Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the distribution.
 *  * Neither the name of NVIDIA CORPORATION nor the names of its
 *    contributors may be used to endorse or promote products derived
 *    from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
 * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
 * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
 * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
 * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
 * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */

/**
 * Matrix multiplication: C = A * B.
 * Host code.
 *
 * This sample implements matrix multiplication as described in Chapter 3
 * of the programming guide.
 * It has been written for clarity of exposition to illustrate various CUDA
 * programming principles, not with the goal of providing the most
 * performant generic kernel for matrix multiplication.
 *
 * See also:
 * V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra,"
 * in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08),
 * Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11.
 */

// System includes
#include <assert.h>
#include <stdio.h>

// CUDA runtime
#include <cuda_runtime.h>

#include "nvrtc_helper.h"

// Helper functions and utilities to work with CUDA
#include <helper_functions.h>

void constantInit(float *data, int size, float val)
{
    for (int i = 0; i < size; ++i) {
        data[i] = val;
    }
}

/**
 * Run a simple test of matrix multiplication using CUDA
 */
int matrixMultiply(int argc, char **argv, int block_size, dim3 &dimsA, dim3 &dimsB)
{
    // Allocate host memory for matrices A and B
    unsigned int size_A     = dimsA.x * dimsA.y;
    unsigned int mem_size_A = sizeof(float) * size_A;
    float       *h_A        = (float *)malloc(mem_size_A);
    unsigned int size_B     = dimsB.x * dimsB.y;
    unsigned int mem_size_B = sizeof(float) * size_B;
    float       *h_B        = (float *)malloc(mem_size_B);

    // Initialize host memory
    const float valB = 0.01f;
    constantInit(h_A, size_A, 1.0f);
    constantInit(h_B, size_B, valB);

    // Allocate device memory
    CUdeviceptr d_A, d_B, d_C;

    char  *cubin, *kernel_file;
    size_t cubinSize;

    kernel_file = sdkFindFilePath("matrixMul_kernel.cu", argv[0]);
    compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 1);

    CUmodule module = loadCUBIN(cubin, argc, argv);

    // Allocate host matrix C
    dim3         dimsC(dimsB.x, dimsA.y, 1);
    unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float);
    float       *h_C        = (float *)malloc(mem_size_C);

    if (h_C == NULL) {
        fprintf(stderr, "Failed to allocate host matrix C!\n");
        exit(EXIT_FAILURE);
    }

    checkCudaErrors(cuMemAlloc(&d_A, mem_size_A));
    checkCudaErrors(cuMemAlloc(&d_B, mem_size_B));
    checkCudaErrors(cuMemAlloc(&d_C, mem_size_C));

    // copy host memory to device
    checkCudaErrors(cuMemcpyHtoD(d_A, h_A, mem_size_A));
    checkCudaErrors(cuMemcpyHtoD(d_B, h_B, mem_size_B));

    // Setup execution parameters
    dim3 threads(block_size, block_size);
    dim3 grid(dimsB.x / threads.x, dimsA.y / threads.y);

    // Create and start timer
    printf("Computing result using CUDA Kernel...\n");

    CUfunction kernel_addr;
    if (block_size == 16) {
        checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "matrixMulCUDA_block16"));
    }
    else {
        checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "matrixMulCUDA_block32"));
    }

    void *arr[] = {(void *)&d_C, (void *)&d_A, (void *)&d_B, (void *)&dimsA.x, (void *)&dimsB.x};

    // Execute the kernel
    int nIter = 300;

    for (int j = 0; j < nIter; j++) {
        checkCudaErrors(cuLaunchKernel(kernel_addr,
                                       grid.x,
                                       grid.y,
                                       grid.z, /* grid dim */
                                       threads.x,
                                       threads.y,
                                       threads.z, /* block dim */
                                       0,
                                       0,       /* shared mem, stream */
                                       &arr[0], /* arguments */
                                       0));

        checkCudaErrors(cuCtxSynchronize());
    }

    // Copy result from device to host
    checkCudaErrors(cuMemcpyDtoH(h_C, d_C, mem_size_C));

    printf("Checking computed result for correctness: ");

    bool correct = true;

    // test relative error by the formula
    //     |<x, y>_cpu - <x,y>_gpu|/<|x|, |y|>  < eps

    double eps = 1.e-6; // machine zero

    for (int i = 0; i < (int)(dimsC.x * dimsC.y); i++) {
        double abs_err    = fabs(h_C[i] - (dimsA.x * valB));
        double dot_length = dimsA.x;
        double abs_val    = fabs(h_C[i]);
        double rel_err    = abs_err / abs_val / dot_length;

        if (rel_err > eps) {
            printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n", i, h_C[i], dimsA.x * valB, eps);
            correct = false;
        }
    }

    printf("%s\n", correct ? "Result = PASS" : "Result = FAIL");

    printf("\nNOTE: The CUDA Samples are not meant for performance measurements. "
           "Results may vary when GPU Boost is enabled.\n");

    // Clean up memory
    free(h_A);
    free(h_B);
    free(h_C);

    checkCudaErrors(cuMemFree(d_A));
    checkCudaErrors(cuMemFree(d_B));
    checkCudaErrors(cuMemFree(d_C));

    if (correct) {
        return EXIT_SUCCESS;
    }
    else {
        return EXIT_FAILURE;
    }
}

/**
 * Program main
 */

int main(int argc, char **argv)
{
    printf("[Matrix Multiply Using CUDA] - Starting...\n");

    if (checkCmdLineFlag(argc, (const char **)argv, "help") || checkCmdLineFlag(argc, (const char **)argv, "?")) {
        printf("Usage -device=n (n >= 0 for deviceID)\n");
        printf("      -wA=WidthA -hA=HeightA (Width x Height of Matrix A)\n");
        printf("      -wB=WidthB -hB=HeightB (Width x Height of Matrix B)\n");
        printf("  Note: Outer matrix dimensions of A & B matrices must be equal.\n");

        exit(EXIT_SUCCESS);
    }

    int block_size = 32;

    // original:
    dim3 dimsA(5 * 2 * block_size, 5 * 2 * block_size, 1);
    dim3 dimsB(5 * 4 * block_size, 5 * 2 * block_size, 1);

    // reduce sizes to avoid running out of memory
    // dim3 dimsA(32,32, 1);
    // dim3 dimsB(32,32,1);

    // width of Matrix A
    if (checkCmdLineFlag(argc, (const char **)argv, "wA")) {
        dimsA.x = getCmdLineArgumentInt(argc, (const char **)argv, "wA");
    }

    // height of Matrix A
    if (checkCmdLineFlag(argc, (const char **)argv, "hA")) {
        dimsA.y = getCmdLineArgumentInt(argc, (const char **)argv, "hA");
    }

    // width of Matrix B
    if (checkCmdLineFlag(argc, (const char **)argv, "wB")) {
        dimsB.x = getCmdLineArgumentInt(argc, (const char **)argv, "wB");
    }

    // height of Matrix B
    if (checkCmdLineFlag(argc, (const char **)argv, "hB")) {
        dimsB.y = getCmdLineArgumentInt(argc, (const char **)argv, "hB");
    }

    if (dimsA.x != dimsB.y) {
        printf("Error: outer matrix dimensions must be equal. (%d != %d)\n", dimsA.x, dimsB.y);
        exit(EXIT_FAILURE);
    }

    printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y, dimsB.x, dimsB.y);

    int matrix_result = matrixMultiply(argc, argv, block_size, dimsA, dimsB);

    exit(matrix_result);
}
