forked from Alexey/DAS_2024_1
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
|
import random as rnd
|
||
|
|
import threading
|
||
|
|
import time
|
||
|
|
from multiprocessing import Pool
|
||
|
|
|
||
|
|
def generateSquareMatrix(size):
|
||
|
|
return [[rnd.randint(0, 100) for i in range(size)] for j in range(size)]
|
||
|
|
|
||
|
|
def printMatrix(matrix):
|
||
|
|
for row in matrix:
|
||
|
|
print(*row, sep="\t")
|
||
|
|
|
||
|
|
|
||
|
|
# Перемножение без использования потоков
|
||
|
|
def matrixMultiplyStandard(matrix1, matrix2):
|
||
|
|
l1 = len(matrix1)
|
||
|
|
l2 = len(matrix2)
|
||
|
|
global result_matrix
|
||
|
|
result = result_matrix
|
||
|
|
for i in range(l1):
|
||
|
|
for j in range(l2):
|
||
|
|
for k in range(l2):
|
||
|
|
result[i][j] += matrix1[i][k] * matrix2[k][j]
|
||
|
|
|
||
|
|
return result
|
||
|
|
|
||
|
|
result_matrix = [[0 for i in range(500)] for j in range(500)]
|
||
|
|
|
||
|
|
# Перемножение в отдельном потоке
|
||
|
|
def matrixMultiplySingleThread(args):
|
||
|
|
matrix1, matrix2, start_i, end_i = args
|
||
|
|
global result_matrix
|
||
|
|
|
||
|
|
result = result_matrix
|
||
|
|
|
||
|
|
for i in range(start_i, end_i):
|
||
|
|
for j in range(len(matrix2[0])):
|
||
|
|
for k in range(len(matrix2)):
|
||
|
|
result[i][j] += matrix1[i - start_i][k] * matrix2[k][j]
|
||
|
|
|
||
|
|
# Параллельное перемножение, использует ф-ю выше для каждого потока
|
||
|
|
def matrixMultiplyWithThreads(matrix1, matrix2, thread_count):
|
||
|
|
l1 = len(matrix1)
|
||
|
|
l2 = len(matrix2)
|
||
|
|
|
||
|
|
# Кол-во строк на последний поток, если деление по потокам будет неточным
|
||
|
|
last_rows_count = 0
|
||
|
|
|
||
|
|
if l1 % thread_count == 0:
|
||
|
|
rows_per_thread = l1 // thread_count
|
||
|
|
else:
|
||
|
|
rows_per_thread = l1 // thread_count
|
||
|
|
last_rows_count = l1 % thread_count
|
||
|
|
|
||
|
|
for i in range(thread_count):
|
||
|
|
start_i = i * rows_per_thread
|
||
|
|
|
||
|
|
if (i - 1) == thread_count and last_rows_count > 0:
|
||
|
|
end_i = start_i + last_rows_count
|
||
|
|
else:
|
||
|
|
end_i = start_i + rows_per_thread
|
||
|
|
|
||
|
|
args = []
|
||
|
|
args.append((matrix1[start_i:end_i], matrix2, start_i, end_i))
|
||
|
|
with Pool(processes = thread_count) as pool:
|
||
|
|
pool.map(matrixMultiplySingleThread, args)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
|
||
|
|
sizes = [100, 300, 500]
|
||
|
|
num_threads = [1, 5, 8, 12]
|
||
|
|
|
||
|
|
for size in sizes:
|
||
|
|
matrix1 = generateSquareMatrix(size)
|
||
|
|
matrix2 = generateSquareMatrix(size)
|
||
|
|
start_time = time.time()
|
||
|
|
matrixMultiplyStandard(matrix1, matrix2)
|
||
|
|
end_time = time.time()
|
||
|
|
print(f"Standard size {size}: {end_time - start_time}s")
|
||
|
|
|
||
|
|
for threads in num_threads:
|
||
|
|
start_time = time.time()
|
||
|
|
matrixMultiplyWithThreads(matrix1, matrix2, threads)
|
||
|
|
end_time = time.time()
|
||
|
|
print(f"Parallel size {size}, {threads} thread(s): {end_time - start_time}s")
|
||
|
|
|
||
|
|
print("-" * 100)
|