75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
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import multiprocessing
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import numpy as np
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import time
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def sequential_multiply_matrix(A, B):
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rows_A = len(A)
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cols_A = len(A[0])
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rows_B = len(B)
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cols_B = len(B[0])
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if cols_A != rows_B:
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print("Умножение матриц невозможно.")
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return
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result_matrix = [[0 for row in range(cols_B)] for col in range(rows_A)]
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for i in range(rows_A):
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for j in range(cols_B):
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for k in range(cols_A):
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result_matrix[i][j] += A[i][k] * B[k][j]
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return result_matrix
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def parallel_multiply_matrix(A, B, num_processes):
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rows_A = len(A)
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cols_A = len(A[0])
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rows_B = len(B)
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cols_B = len(B[0])
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if cols_A != rows_B:
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print("Умножение матриц невозможно.")
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return
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result_matrix = [[0 for row in range(cols_B)] for col in range(rows_A)]
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chunk_size = int(rows_A / num_processes)
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processes = []
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for i in range(num_processes):
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start = chunk_size * i
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end = chunk_size * (i + 1) if i < num_processes - 1 else rows_A
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p = multiprocessing.Process(target=perform_multiplication, args=(A, B, result_matrix, start, end))
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processes.append(p)
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p.start()
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for p in processes:
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p.join()
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return result_matrix
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def perform_multiplication(A, B, result_matrix, start, end):
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for i in range(start, end):
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for j in range(len(B[0])):
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for k in range(len(A[0])):
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result_matrix[i][j] += A[i][k] * B[k][j]
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if __name__ == "__main__":
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matrix_sizes = [100, 300, 500]
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num_processes = 4
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for n in matrix_sizes:
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matrix_A = np.random.randint(10, size=(n, n))
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matrix_B = np.random.randint(10, size=(n, n))
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start_time = time.time()
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sequential_result = sequential_multiply_matrix(matrix_A, matrix_B)
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end_time = time.time()
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print(f"Последовательное умножение {n}x{n} матриц заняло: {end_time - start_time} секунд")
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start_time = time.time()
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parallel_result = parallel_multiply_matrix(matrix_A, matrix_B, num_processes)
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end_time = time.time()
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print(f"Параллельное умножение {n}x{n} матриц заняло: {end_time - start_time}")
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