DAS_2023_1/antonov_dmitry_lab_5/benchmark.py

import multiprocessing

import numpy as np
import time


def multiply_matrices(matrix_a, matrix_b):
    if len(matrix_a[0]) != len(matrix_b):
        raise ValueError("матрицы имеют разную длину")

    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]

    for i in range(len(matrix_a)):
        for j in range(len(matrix_b[0])):
            for k in range(len(matrix_b)):
                result[i][j] += matrix_a[i][k] * matrix_b[k][j]

    return result


def multiply_row(args):
    matrix_a, matrix_b, i = args
    row_result = [0 for _ in range(len(matrix_b[0]))]
    for j in range(len(matrix_b[0])):
        for k in range(len(matrix_b)):
            row_result[j] += matrix_a[i][k] * matrix_b[k][j]
    return row_result, i


def multiply_matrices_parallel(matrix_a, matrix_b, threads):
    if len(matrix_a[0]) != len(matrix_b):
        raise ValueError("матрицы имеют разную длину")

    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]

    with multiprocessing.Pool(processes=threads) as pool:
        args_list = [(matrix_a, matrix_b, i) for i in range(len(matrix_a))]
        rows_results = pool.map(multiply_row, args_list)

    for row_result, row_index in rows_results:
        result[row_index] = row_result

    return result


def benchmark_sequential(size):
    matrix_a = np.random.rand(size, size)
    matrix_b = np.random.rand(size, size)

    start_time = time.time()
    multiply_matrices(matrix_a, matrix_b)
    end_time = time.time()

    return end_time - start_time


def benchmark_parallel(size, num_threads):
    matrix_a = np.random.rand(size, size)
    matrix_b = np.random.rand(size, size)

    start_time = time.time()
    multiply_matrices_parallel(matrix_a, matrix_b, num_threads)
    end_time = time.time()

    return end_time - start_time


if __name__ == "__main__":
    sizes = [100, 300, 500]
    threads = [2, 16, 32]

    for size in sizes:
        sequential_time = benchmark_sequential(size)
        print(f"Время обычное: {sequential_time} с")
        print(f"Размер матрицы: {size}x{size}")

    for thread in threads:
        for size in sizes:
            parallel_time = benchmark_parallel(size, thread)
            print(f"Размер матрицы: {size}x{size}")
            print(f"Время параллельное: {parallel_time} с")
            print(f"Потоков:  {thread}")