DAS_2024_1/turner_ilya_lab_5/main.py

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import random
import time
import multiprocessing
import numpy as np
# Генерация матрицы
def generate_matrix(size):
return [[random.randint(0, 10) for _ in range(size)] for _ in range(size)]
# Умножение одной строки
def multiply_row(i, A, B, result):
size = len(A)
for j in range(size):
for k in range(size):
result[i][j] += A[i][k] * B[k][j]
# параллельное умножение матриц с помощью multiprocessing
def parallel_matrix_multiply(A, B, num_processes):
size = len(A)
result = [[0] * size for _ in range(size)]
with multiprocessing.Pool(processes=num_processes) as pool:
pool.starmap(multiply_row, [(i, A, B, result) for i in range(size)])
return result
# Замер времени на умножение
def benchmark(size, num_processes=1):
A = generate_matrix(size)
B = generate_matrix(size)
start_time = time.time()
parallel_matrix_multiply(A, B, num_processes)
par_time = time.time() - start_time
return par_time
def main():
# Размеры матриц
matrix_sizes = [100, 300, 500]
# Количество потоков
num_processes_list = [1, 2, 4, 6, 8]
# Таблица с бенчмарками
print("-*" * 40)
print(f"{'Количество потоков':<20}{'|100x100 (сек.)':<20}{'|300x300 (сек.)':<20}{'|500x500 (сек.)'}")
print("-*" * 40)
for num_processes in num_processes_list:
row = f"{num_processes:<20}"
for size in matrix_sizes:
par_time = benchmark(size, num_processes)
row += f"|{par_time:.4f}".ljust(20)
print(row)
print("-*" * 40)
if __name__ == "__main__":
main()