forked from Alexey/DAS_2024_1
81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
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import numpy as np
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from concurrent.futures import ProcessPoolExecutor
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import time
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#Функция умножения матриц
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def multi(A, B):
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n = len(A)
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k = len(B)
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C = np.zeros((n, n))
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for i in range(n):
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for j in range(n):
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C[i][j] = sum(A[i][p] * B[p][j] for p in range(k))
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return C
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# Функция последовательного умножения матриц
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def multi_sequential(A, B):
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n = len(A)
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C = np.zeros((n, n))
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for i in range(n):
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for k in range(n):
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temp = A[i][k]
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for j in range(n):
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C[i][j] += temp * B[k][j]
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return C
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# Функция умножения матриц с numpy
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def multi_numpy(A, B):
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return np.dot(A, B)
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# Параллельное умножение матриц
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def multi_parallel(A, B, num_threads):
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n = len(A)
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C = np.zeros((n, n))
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step = n // num_threads
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with ProcessPoolExecutor(max_workers=num_threads) as executor:
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futures = []
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for i in range(num_threads):
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start_row = i * step
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end_row = (i + 1) * step if i != num_threads - 1 else n
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a_slice = A[:, i*step: (i+1)*step]
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b_slice = B[start_row:end_row]
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futures.append(executor.submit(multi, a_slice, b_slice))
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for future in futures:
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C += future.result()
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return C
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# Пример использования
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if __name__ == "__main__":
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matrix_sizes = [100, 300, 500]
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num_threads = [2, 4, 5, 10]
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for n in matrix_sizes:
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A = np.random.rand(n, n)
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B = np.random.rand(n, n)
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# Умножение с numpy
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start_np = time.time()
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nump = multi_numpy(A, B)
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end_np = time.time()
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print(f'Умножение матриц {n}x{n} последовательно с numpy: {(end_np - start_np):.6f} с.')
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# Последовательное умножение
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start_seq = time.time()
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sequential = multi_sequential(A, B)
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end_seq = time.time()
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print(f'Умножение матриц {n}x{n} последовательно: {(end_seq - start_seq):.6f} с.')
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# Параллельное умножение
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for thread in num_threads:
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start_par = time.time()
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parallel = multi_parallel(A, B, thread)
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end_par = time.time()
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print(f'Умножение матриц {n}x{n} параллельно для {thread} потоков: {(end_par - start_par):.3f} с.')
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print('')
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