distributed-computing/tasks/plaksina-av/lab_5/benchmarking.py

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2023-12-16 23:31:32 +04:00
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 = [4, 16, 32]
for size in sizes:
sequential_time = benchmark_sequential(size)
print(f"Время для последовательного способа: {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 для параллельного спобсоба: {parallel_time} с")
print(f"Потоков: {thread}")