51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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import numpy as np
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from multiprocessing import Pool
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import time
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import argparse
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def determinant_block(matrix_block):
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return np.linalg.det(matrix_block)
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def determinant_parallel(matrix, num_processes):
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size = matrix.shape[0]
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step = size // num_processes
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blocks = []
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for i in range(num_processes):
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start_row = i * step
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end_row = start_row + step if i < num_processes - 1 else size
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blocks.append(matrix[start_row:end_row, start_row:end_row])
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pool = Pool(processes=num_processes)
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dets = pool.map(determinant_block, blocks)
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pool.close()
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pool.join()
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return np.prod(dets)
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def benchmark(size, num_processes):
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matrix = np.random.rand(size, size)
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start = time.time()
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det_parallel = determinant_parallel(matrix, num_processes)
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end = time.time()
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print(f"Матрица {size}x{size} с {num_processes} процессами заняла {end - start:.5f} сек (Параллельно)")
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start = time.time()
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det_seq = determinant_block(matrix)
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end = time.time()
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print(f"Матрица {size}x{size} последовательный вычисление заняло {end - start:.5f} сек (Последовательно)")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Вычисление детерминанта с параллельной обработкой")
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parser.add_argument("--processes", type=int, default=4)
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args = parser.parse_args()
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sizes = [100, 300, 500]
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for size in sizes:
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benchmark(size, args.processes)
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