forked from Alexey/DAS_2023_1
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import numpy as np
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import time
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import concurrent.futures
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def multiply_matrices(matrix_a, matrix_b):
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if len(matrix_a[0]) != len(matrix_b):
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raise ValueError("Incompatible matrix dimensions for multiplication")
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result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
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for i in range(len(matrix_a)):
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for j in range(len(matrix_b[0])):
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for k in range(len(matrix_b)):
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result[i][j] += matrix_a[i][k] * matrix_b[k][j]
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return result
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def multiply_matrices_parallel(matrix_a, matrix_b, num_threads):
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if len(matrix_a[0]) != len(matrix_b):
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raise ValueError("Incompatible matrix dimensions for multiplication")
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result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
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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(len(matrix_a)):
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futures.append(executor.submit(_multiply_row, matrix_a, matrix_b, i))
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for i, future in enumerate(concurrent.futures.as_completed(futures)):
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result[i] = future.result()
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return result
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def _multiply_row(matrix_a, matrix_b, i):
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row_result = [0 for _ in range(len(matrix_b[0]))]
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for j in range(len(matrix_b[0])):
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for k in range(len(matrix_b)):
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row_result[j] += matrix_a[i][k] * matrix_b[k][j]
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return row_result
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def benchmark_sequential(size):
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matrix_a = np.random.rand(size, size)
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matrix_b = np.random.rand(size, size)
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start_time = time.time()
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multiply_matrices(matrix_a, matrix_b)
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end_time = time.time()
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return end_time - start_time
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def benchmark_parallel(size, num_threads):
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matrix_a = np.random.rand(size, size)
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matrix_b = np.random.rand(size, size)
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start_time = time.time()
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multiply_matrices_parallel(matrix_a, matrix_b, num_threads)
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end_time = time.time()
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return end_time - start_time
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if __name__ == "__main__":
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sizes = [300]
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threads = [2, 8]
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for size in sizes:
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sequential_time = benchmark_sequential(size)
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print(f"Время обычное: {sequential_time} с")
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print(f"Размер матрицы: {size}x{size}")
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for thread in threads:
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for size in sizes:
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parallel_time = benchmark_parallel(size, thread)
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print(f"Размер матрицы: {size}x{size}")
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print(f"Время параллельное: {parallel_time} с")
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print(f"Потоков: {thread}")
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