Ideally float64 uses 64 bits for each number while float16 uses 16 bits. 64/16=4. However, there is some additional overhead. This overhead makes up a large portion of small arrays, but becomes negligible as the array gets bigger.
On Sat, Mar 13, 2021, 16:01 <klark--k...@yandex.ru> wrote: > Dear colleagues! > > Size of np.float16(1) is 26 > Size of np.float64(1) is 32 > 32 / 26 = 1.23 > > Since memory is limited I have a question after this code: > > import numpy as np > import sys > > a1 = np.ones(1, dtype='float16') > b1 = np.ones(1, dtype='float64') > div_1 = sys.getsizeof(b1) / sys.getsizeof(a1) > # div_1 = 1.06 > > a2 = np.ones(10, dtype='float16') > b2 = np.ones(10, dtype='float64') > div_2 = sys.getsizeof(b2) / sys.getsizeof(a2) > # div_2 = 1.51 > > a3 = np.ones(100, dtype='float16') > b3 = np.ones(100, dtype='float64') > div_3 = sys.getsizeof(b3) / sys.getsizeof(a3) > # div_3 = 3.0 > Size of np.float64 numpy arrays is four times more than for np.float16. > Is it possible to minimize the difference close to 1.23? > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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