39 lines
1.0 KiB
Python
39 lines
1.0 KiB
Python
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import numpy as np
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from multiprocessing import Pool
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import time
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def determinant_block(matrix_block):
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return np.linalg.det(matrix_block)
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def determinant_parallel(matrix, num_processes):
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size = matrix.shape[0]
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step = size // num_processes
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pool = Pool(processes=num_processes)
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blocks = []
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for i in range(0, size, step):
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blocks.append(matrix[i:i+step, i:i+step])
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dets = pool.map(determinant_block, blocks)
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return np.prod(dets)
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if __name__ == "__main__":
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sizes = [100, 300, 500]
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processes = [2, 4, 8]
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for size in sizes:
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matrix = np.random.rand(size, size)
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for p in processes:
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start = time.time()
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det = determinant_parallel(matrix, p)
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end = time.time()
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print(f"{size}x{size} matrix with {p} processes took {end - start:.5f} secs")
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start = time.time()
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det_seq = determinant_block(matrix)
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end = time.time()
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print(f"{size}x{size} matrix sequential took {end - start:.5f} secs")
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