60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
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import random
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import time
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import multiprocessing
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import numpy as np
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def create_random_matrix(size):
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return [[random.randint(0, 10) for _ in range(size)] for _ in range(size)]
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# Функция для умножения одной строки матрицы
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def process_row(i, A, B, result):
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size = len(A)
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for j in range(size):
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for k in range(size):
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result[i][j] += A[i][k] * B[k][j]
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# Функция для параллельного умножения матриц с использованием multiprocessing
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def multiply_matrices_in_parallel(A, B, num_processes):
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size = len(A)
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result = [[0] * size for _ in range(size)]
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with multiprocessing.Pool(processes=num_processes) as pool:
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pool.starmap(process_row, [(i, A, B, result) for i in range(size)])
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return result
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# Функция для измерения времени выполнения умножения матриц
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def measure_execution_time(size, num_processes=1):
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A = create_random_matrix(size)
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B = create_random_matrix(size)
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start_time = time.time()
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multiply_matrices_in_parallel(A, B, num_processes)
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elapsed_time = time.time() - start_time
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return elapsed_time
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def main():
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matrix_sizes = [100, 300, 500]
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process_count_options = [1, 2, 4, 6, 8]
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print("-*" * 40)
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print(f"{'Количество потоков':<20}{'|100x100 (сек.)':<20}{'|300x300 (сек.)':<20}{'|500x500 (сек.)'}")
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print("-*" * 40)
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# Запуск тестов для разных значений числа процессов
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for num_processes in process_count_options:
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row = f"{num_processes:<20}"
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for size in matrix_sizes:
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par_time = measure_execution_time(size, num_processes)
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row += f"|{par_time:.4f}".ljust(20)
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print(row)
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print("-*" * 40)
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if __name__ == "__main__":
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main()
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