54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
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import time
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import numpy
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import concurrent.futures
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# Функция для умножения матриц
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def multiply_matrices(matrix1, matrix2):
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return numpy.dot(matrix1, matrix2)
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# Параллельное умножение матриц
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def multiply_matrices_parallel(matrix1, matrix2, num_threads):
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result = numpy.zeros_like(matrix1)
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chunk_size = matrix1.shape[0] // num_threads
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def multiply_chunk(start, end):
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nonlocal result
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for i in range(start, end):
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result[i] = numpy.dot(matrix1[i], matrix2)
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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, matrix1.shape[0], chunk_size):
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futures.append(executor.submit(multiply_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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# Бенчмарк для сравнения производительности
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def benchmark(matrix_size, num_threads_list=[1, 2, 4]):
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# Генерация матриц
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matrix1 = numpy.random.rand(matrix_size, matrix_size)
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matrix2 = numpy.random.rand(matrix_size, matrix_size)
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# Бенчмарк для обычного умножения
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start_time = time.time()
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result = multiply_matrices(matrix1, matrix2)
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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"Последовательный: Время выполнения: {end_time - start_time:.6f} секунд")
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# Бенчмарк для параллельного умножения
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for num_threads in num_threads_list:
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start_time = time.time()
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result_parallel = multiply_matrices_parallel(matrix1, matrix2, num_threads)
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end_time = time.time()
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print(f"Параллельный ({num_threads} поток): Время выполнения: {end_time - start_time:.6f} секунд")
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print()
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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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