import numpy as np
import time
from concurrent.futures import ThreadPoolExecutor


def sequential_matrix_multiply(matrix_a, matrix_b):
    return np.dot(matrix_a, matrix_b)


def parallel_matrix_multiply(matrix_a, matrix_b, num_threads):
    result = np.zeros_like(matrix_a)
    rows, cols = matrix_a.shape

    def multiply_row(row):
        nonlocal matrix_a, matrix_b, result
        result[row, :] = np.dot(matrix_a[row, :], matrix_b)

    with ThreadPoolExecutor(max_workers=num_threads) as executor:
        executor.map(multiply_row, range(rows))

    return result


def benchmark(matrix_size, num_threads=1):
    matrix_a = np.random.rand(matrix_size, matrix_size)
    matrix_b = np.random.rand(matrix_size, matrix_size)

    start_time = time.time()
    sequential_result = sequential_matrix_multiply(matrix_a, matrix_b)
    sequential_time = time.time() - start_time

    start_time = time.time()
    parallel_result = parallel_matrix_multiply(matrix_a, matrix_b, num_threads)
    parallel_time = time.time() - start_time

    return sequential_time, parallel_time


# Пример использования для матриц размером 100x100, 300x300, 500x500 элементов
matrix_sizes = [100, 300, 500]
threads_count = 4  # Указать желаемое количество потоков

for size in matrix_sizes:
    sequential_time, parallel_time = benchmark(size, threads_count)
    print(f"Размер матрицы: {size}x{size}")
    print(f"Время с последовательным выполнением: {sequential_time:.6f} секунд")
    print(f"Время с параллельной обработкой ({threads_count} потоков): {parallel_time:.6f} секунд")
    print("=" * 30)