50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
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import numpy as np
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import time
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import concurrent.futures
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def calculate_determinant(matrix):
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return np.linalg.det(matrix)
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def calculate_determinant_parallel(matrix, num_threads):
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result = 1.0
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chunk_size = matrix.shape[0] // num_threads
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def calculate_chunk(start, end):
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nonlocal result
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for i in range(start, end):
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result *= matrix[i, i]
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with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
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futures = []
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for i in range(0, matrix.shape[0], chunk_size):
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futures.append(executor.submit(calculate_chunk, i, i + chunk_size))
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for future in concurrent.futures.as_completed(futures):
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future.result()
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return result
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def benchmark(matrix_size, num_threads=1):
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# Генерация квадратной матрицы
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matrix = np.random.rand(matrix_size, matrix_size)
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# Бенчмарк для обычного нахождения детерминанта
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start_time = time.time()
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det_normal = calculate_determinant(matrix)
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end_time = time.time()
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print(f"Размер матрицы {matrix_size}x{matrix_size}")
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print(f"Время при обычном выполнении: {end_time - start_time:.6f} секунд")
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print(f"Детерминант: {det_normal}")
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# Бенчмарк для параллельного нахождения детерминанта
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start_time = time.time()
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det_parallel = calculate_determinant_parallel(matrix, num_threads)
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end_time = time.time()
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print(f"Время при параллельном выполнении ({num_threads} поток): {end_time - start_time:.6f} секунд")
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print(f"Детерминант: {det_parallel}\n")
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# Запуск бенчмарков
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benchmark(100)
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benchmark(300)
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benchmark(500)
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