[Numpy-discussion] size of arrays
Todd
toddrjen at gmail.com
Sat Mar 13 16:05:11 EST 2021
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--kent at 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?
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