Hi everyone,
Operations such as np.sqrt or np.cos on numpy scalars are much slower than
operations on an array of size 1 (benchmark below). The reason seems to be
that a numpy scalar (e.g. np.float64(1.1)) is converted internally to an
array, an array is created for the result of the operation and the result
is converted back to a scalar (so three object constructions in total).
Operating directly on an array creates an array for the result, which is
returned (so one object construction in total).
Has anyone looked into improving this performance before or has ideas how
to tackle this?
With kind regards,
Pieter Eendebak
Example benchmark:
import numpy as np
from numpy import sqrt
import time
v=np.array([1.1])
t0=time.perf_counter()
x=np.float64(v)
for kk in range(1_000_000):
w1=sqrt(x)
dt1=time.perf_counter()-t0
print(dt1)
t0=time.perf_counter()
x=v
for kk in range(1_000_000):
w2=sqrt(x)
dt2=time.perf_counter()-t0
print(dt2)
print(f'factor {dt1/dt2:.2f} faster')
assert(float(w1)==float(w2))
Results in
1.0878291579974757
0.5115369699997245
factor 2.13 faster
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