text: /* Copyright (c) 2021, 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
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   * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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   * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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   * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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   * 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 <stdio.h>
  #include <assert.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);
  }
  