chundoong-lab-ta/APWS23/matmul-skeleton/main.cpp

#include <getopt.h>
#include <stdbool.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include "matmul.h"
#include "util.h"

static bool print_matrix = false;
static bool validation = false;
static size_t T = 0;
static size_t M = 8;
static size_t N = 8;
static size_t K = 8;
static size_t num_iterations = 1;

static char matmul_type_string[6][64] = {
    "CPU (sequential)",   "naive GPU", "GPU optimization 1",
    "GPU optimization 2", "multi GPU", "cuBLAS",
};

static void print_help(const char *prog_name) {
  printf("Usage: %s [-pvh] [-n num_iterations] T M N K\n", prog_name);
  printf("Options:\n");
  printf("     -p : print matrix. (default: off)\n");
  printf("     -v : validate matmul. (default: off)\n");
  printf("     -h : print this page.\n");
  printf("     -n : number of iterations (default: 1)\n");
  printf("      T : type of matrix multiplication (default: 0)\n");
  printf("            0 : CPU (sequential)\n");
  printf("            1 : naive GPU\n");
  printf("            2 : GPU optimization 1\n");
  printf("            3 : GPU optimization 2\n");
  printf("            4 : multi GPU\n");
  printf("            5 : cuBLAS\n");
  printf("      M : number of rows of matrix A and C. (default: 8)\n");
  printf("      N : number of columns of matrix B and C. (default: 8)\n");
  printf(
      "      K : number of columns of matrix A and rows of B. (default: 8)\n");
}

static void parse_opt(int argc, char **argv) {
  int c;
  while ((c = getopt(argc, argv, "pvht:n:m:")) != -1) {
    switch (c) {
    case 'p':
      print_matrix = true;
      break;
    case 'v':
      validation = true;
      break;
    case 'n':
      num_iterations = atoi(optarg);
      break;
    case 'h':
    default:
      print_help(argv[0]);
      exit(0);
    }
  }
  for (int i = optind, j = 0; i < argc; ++i, ++j) {
    switch (j) {
    case 0:
      T = (size_t)atoi(argv[i]);
      break;
    case 1:
      M = (size_t)atoi(argv[i]);
      break;
    case 2:
      N = (size_t)atoi(argv[i]);
      break;
    case 3:
      K = (size_t)atoi(argv[i]);
      break;
    default:
      break;
    }
  }

  printf("============= Matrix Multiplication Benchmark =============\n");
  printf("- Matmul Type: %s\n", matmul_type_string[T]);
  printf("- Problem size: M = %lu, N = %lu, K = %lu\n", M, N, K);
  printf("- Number of iterations: %lu\n", num_iterations);
  printf("- Print matrix: %s\n", print_matrix ? "on" : "off");
  printf("- Validation: %s\n", validation ? "on" : "off");
}

int main(int argc, char **argv) {
  parse_opt(argc, argv);
  fflush(stdout);

  /* Allocate and initialize matrices on CPU */
  float *A, *B, *C;
  alloc_mat(&A, M, K);
  alloc_mat(&B, K, N);
  alloc_mat(&C, M, N);
  rand_mat(A, M, K);
  rand_mat(B, K, N);

  /* Initialize Matrix Multiplication */
  switch (T) {
  case 0:
    matmul_cpu_initialize(M, N, K);
    break;
  case 1:
    matmul_naive_initialize(M, N, K);
    break;
  case 2:
    matmul_opt1_initialize(M, N, K);
    break;
  case 3:
    matmul_opt2_initialize(M, N, K);
    break;
  case 4:
    matmul_multigpu_initialize(M, N, K);
    break;
  case 5:
    matmul_cublas_initialize(M, N, K);
    break;
  }

  /* Run few warmup iterations... */
  for (size_t i = 0; i < 3; i++) {
    zero_mat(C, M, N);
    switch (T) {
    case 0:
      matmul_cpu(A, B, C, M, N, K);
      break;
    case 1:
      matmul_naive(A, B, C, M, N, K);
      break;
    case 2:
      matmul_opt1(A, B, C, M, N, K);
      break;
    case 3:
      matmul_opt2(A, B, C, M, N, K);
      break;
    case 4:
      matmul_multigpu(A, B, C, M, N, K);
      break;
    case 5:
      matmul_cublas(A, B, C, M, N, K);
      break;
    }
  }

  /* Run matrix multiplication for num_iterations */
  printf("\n--------------------- Run Benchmark -----------------------\n");

  double elapsed_time_sum = 0;
  for (size_t i = 0; i < num_iterations; ++i) {
    printf("[iter %lu] ", i);
    fflush(stdout);

    zero_mat(C, M, N);
    double elapsed_time_iter = -get_current_time();
    switch (T) {
    case 0:
      matmul_cpu(A, B, C, M, N, K);
      break;
    case 1:
      matmul_naive(A, B, C, M, N, K);
      break;
    case 2:
      matmul_opt1(A, B, C, M, N, K);
      break;
    case 3:
      matmul_opt2(A, B, C, M, N, K);
      break;
    case 4:
      matmul_multigpu(A, B, C, M, N, K);
      break;
    case 5:
      matmul_cublas(A, B, C, M, N, K);
      break;
    }
    elapsed_time_iter += get_current_time();

    printf("%.4f s\n", elapsed_time_iter);
    elapsed_time_sum += elapsed_time_iter;
  }

  if (print_matrix) {
    printf("\n---------------------- Print Matrix -----------------------\n");
    printf("MATRIX A:\n");
    print_mat(A, M, K);
    printf("MATRIX B:\n");
    print_mat(B, K, N);
    printf("MATRIX C:\n");
    print_mat(C, M, N);
  }

  if (validation) {
    printf("\n----------------------- Validation ------------------------\n");
    check_mat_mul(A, B, C, M, N, K);
  }

  /* Print performance results */
  double elapsed_time_avg = elapsed_time_sum / num_iterations;
  printf("\n-------------------- Benchmark Summary --------------------\n");
  printf("Avg. time        : %.4f s\n", elapsed_time_avg);
  printf("Avg. performance : %.1f GFLOPS\n",
         2.0 * M * N * K / elapsed_time_avg / 1e9);

  /* Finalize matrix multiplication */
  switch (T) {
  case 0:
    matmul_cpu_initialize(M, N, K);
    break;
  case 1:
    matmul_naive_initialize(M, N, K);
    break;
  case 2:
    matmul_opt1_initialize(M, N, K);
    break;
  case 3:
    matmul_opt2_initialize(M, N, K);
    break;
  case 4:
    matmul_multigpu_initialize(M, N, K);
    break;
  case 5:
    matmul_cublas_initialize(M, N, K);
    break;
  }

  printf("\n===========================================================\n");
  return 0;
}