83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
from multiprocessing import Pool, cpu_count
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import time
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import numpy as np
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def determinant(matrix):
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matrix = np.array(matrix, dtype=float)
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n = matrix.shape[0]
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for i in range(n):
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if matrix[i, i] == 0:
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for j in range(i + 1, n):
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if matrix[j, i] != 0:
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matrix[[i, j]] = matrix[[j, i]]
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break
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if matrix[i, i] == 0:
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return 0
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for j in range(i + 1, n):
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factor = matrix[j, i] / matrix[i, i]
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matrix[j] -= factor * matrix[i]
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det = 1.0
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for i in range(n):
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det *= matrix[i, i]
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return det
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def _determinant_parallel_chunk(args):
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matrix, start_row, end_row = args
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n = matrix.shape[0]
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for i in range(start_row, end_row):
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for j in range(i + 1, n):
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factor = matrix[j, i] / matrix[i, i]
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matrix[j] -= factor * matrix[i]
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return matrix
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def determinant_parallel(matrix, num_processes=None):
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if num_processes is None:
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num_processes = cpu_count()
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matrix = np.array(matrix, dtype=float)
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n = matrix.shape[0]
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chunk_size = n // num_processes
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tasks = [
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(
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matrix.copy(),
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i * chunk_size,
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(i + 1) * chunk_size if i != num_processes - 1 else n,
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)
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for i in range(num_processes)
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]
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with Pool(processes=num_processes) as pool:
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results = pool.map(_determinant_parallel_chunk, tasks)
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# В каждой части делаем лишь частичное вычитание
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# Теперь выполняем окончательное вычисление детерминанта
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det = 1.0
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for i in range(n):
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det *= results[0][i, i]
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return det
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sizes = [100, 300, 500]
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NUM_PROCESSES = 12
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for size in sizes:
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A = np.random.rand(size, size)
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start_time = time.time()
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det_seq = determinant(A)
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seq_time = time.time() - start_time
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start_time = time.time()
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det_par = determinant_parallel(A, num_processes=NUM_PROCESSES)
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par_time = time.time() - start_time
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print(
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f"Size {size}x{size}: Sequential Time = {seq_time:.4f} s, Parallel Time = {par_time:.4f} s"
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)
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