53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
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import numpy as np
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import time
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import multiprocessing
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def matrix_multiply(A, B):
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n = A.shape[0]
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C = np.zeros((n, n))
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for i in range(n):
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for j in range(n):
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for k in range(n):
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C[i, j] += A[i, k] * B[k, j]
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return C
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def worker(A, B, C, row_indices):
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for i in row_indices:
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for j in range(B.shape[1]):
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for k in range(A.shape[1]):
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C[i, j] += A[i, k] * B[k, j]
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def parallel_matrix_multiply(A, B, num_processes):
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n = A.shape[0]
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C = np.zeros((n, n))
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rows_per_process = n // num_processes
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processes = []
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for i in range(num_processes):
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start_row = i * rows_per_process
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end_row = start_row + rows_per_process if i != num_processes - 1 else n
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p = multiprocessing.Process(target=worker, args=(A, B, C, range(start_row, end_row)))
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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 C
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def benchmark(matrix_size, num_processes):
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A = np.random.rand(matrix_size, matrix_size)
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B = np.random.rand(matrix_size, matrix_size)
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start_time = time.time()
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C_par = parallel_matrix_multiply(A, B, num_processes)
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par_time = time.time() - start_time
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print(f"Размер матрицы: {matrix_size}x{matrix_size}, Потоки: {num_processes}, Время: {par_time:.4f} сек.")
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if __name__ == "__main__":
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mat_sizes = [100, 300, 500]
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for size in mat_sizes:
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for num_processes in [1, 2, 4]:
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benchmark(size, num_processes)
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