63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
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import numpy as np
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import time
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import threading
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def determinant_sequential(matrix):
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return np.linalg.det(matrix)
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def determinant_parallel(matrix, num_threads):
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n = len(matrix)
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if n == 1:
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return matrix[0][0]
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if n == 2:
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return matrix[0][0] * matrix[1][1] - matrix[0][1] * matrix[1][0]
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threads = []
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results = [0] * num_threads
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chunk_size = n // num_threads
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def worker(thread_id):
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start_index = thread_id * chunk_size
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end_index = min((thread_id + 1) * chunk_size, n)
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det_sum = 0
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for i in range(start_index, end_index):
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submatrix = np.delete(matrix, i, 0)
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submatrix = np.delete(submatrix, 0, 1)
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det_sum += (-1)**i * matrix[i][0] * determinant_sequential(submatrix)
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results[thread_id] = det_sum
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for i in range(num_threads):
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thread = threading.Thread(target=worker, args=(i,))
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threads.append(thread)
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thread.start()
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for thread in threads:
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thread.join()
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return sum(results)
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sizes = [100, 300, 500]
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num_threads = [1, 2, 4]
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results = {}
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for size in sizes:
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matrix = np.random.rand(size, size)
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results[size] = {}
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for n_threads in num_threads:
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start_time = time.time()
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if n_threads == 1:
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det = determinant_sequential(matrix)
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else:
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det = determinant_parallel(matrix, n_threads)
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end_time = time.time()
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results[size][n_threads] = end_time - start_time
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print(f"Размер матрицы: {size}x{size}, Потоков: {n_threads}, Время: {end_time - start_time:.4f} сек.")
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print("\n## Результаты бенчмарка:")
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print("| Размер матрицы | 1 поток (последовательно) | 2 потока | 4 потока |")
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for size, timings in results.items():
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print(f"| {size}x{size} | {timings [1] :.4f} сек. | {timings.get(2, 'N/A'):.4f} сек. | {timings.get(4, 'N/A'):.4f} сек. |")
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