import random
import time
import multiprocessing
import numpy as np

# Генерация случайной матрицы
def create_random_matrix(dim):
    return [[random.randint(0, 10) for _ in range(dim)] for _ in range(dim)]

# Умножение отдельной строки
def compute_row_product(row_idx, mat_a, mat_b, output_mat):
    dim = len(mat_a)
    for col_idx in range(dim):
        for k in range(dim):
            output_mat[row_idx][col_idx] += mat_a[row_idx][k] * mat_b[k][col_idx]

# Параллельное умножение матриц
def parallel_matrix_multiplication(mat_a, mat_b, num_workers):
    dim = len(mat_a)
    result_matrix = [[0] * dim for _ in range(dim)]
    
    with multiprocessing.Pool(processes=num_workers) as pool:
        pool.starmap(compute_row_product, [(i, mat_a, mat_b, result_matrix) for i in range(dim)])
    
    return result_matrix

# Измерение времени выполнения
def run_benchmark(dim, num_workers=1):
    mat_a = create_random_matrix(dim)
    mat_b = create_random_matrix(dim)

    start_time = time.time()
    parallel_matrix_multiplication(mat_a, mat_b, num_workers)
    elapsed_time = time.time() - start_time

    return elapsed_time

def main():
    # Размеры матриц
    matrix_dimensions = [100, 300, 500]  
    # Количество рабочих процессов
    worker_counts = [1, 2, 4, 6, 8]
    
    # Печать таблицы с результатами
    print("-*" * 40)
    print(f"{'Количество процессов':<20}{'|100x100 (сек.)':<20}{'|300x300 (сек.)':<20}{'|500x500 (сек.)'}")
    print("-*" * 40)
    
    for workers in worker_counts:
        row = f"{workers:<20}"
        
        for dim in matrix_dimensions:
            benchmark_time = run_benchmark(dim, workers)
            row += f"|{benchmark_time:.4f}".ljust(20)
        print(row)
        print("-*" * 40)

if __name__ == "__main__":
    main()