49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
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import numpy as np
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import time
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from concurrent.futures import ThreadPoolExecutor
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def sequential_matrix_multiply(matrix_a, matrix_b):
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return np.dot(matrix_a, matrix_b)
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def parallel_matrix_multiply(matrix_a, matrix_b, num_threads):
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result = np.zeros_like(matrix_a)
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rows, cols = matrix_a.shape
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def multiply_row(row):
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nonlocal matrix_a, matrix_b, result
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result[row, :] = np.dot(matrix_a[row, :], matrix_b)
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with ThreadPoolExecutor(max_workers=num_threads) as executor:
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executor.map(multiply_row, range(rows))
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return result
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def benchmark(matrix_size, num_threads=1):
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matrix_a = np.random.rand(matrix_size, matrix_size)
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matrix_b = np.random.rand(matrix_size, matrix_size)
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start_time = time.time()
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sequential_result = sequential_matrix_multiply(matrix_a, matrix_b)
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sequential_time = time.time() - start_time
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start_time = time.time()
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parallel_result = parallel_matrix_multiply(matrix_a, matrix_b, num_threads)
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parallel_time = time.time() - start_time
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return sequential_time, parallel_time
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# Пример использования для матриц размером 100x100, 300x300, 500x500 элементов
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matrix_sizes = [100, 300, 500]
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threads_count = 4 # Указать желаемое количество потоков
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for size in matrix_sizes:
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sequential_time, parallel_time = benchmark(size, threads_count)
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print(f"Размер матрицы: {size}x{size}")
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print(f"Время с последовательным выполнением: {sequential_time:.6f} секунд")
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print(f"Время с параллельной обработкой ({threads_count} потоков): {parallel_time:.6f} секунд")
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print("=" * 30)
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