[SOLVED] Python 3.X: Why numexpr.evaluate() slower than eval()?

Python 3.X: Why numexpr.evaluate() slower than eval()?

The purpose of using the numexpr.evaluate() is to speed up the compute. But im my case it wokrs even slower than numpy und eval(). I would like to know why?

code as example:

import datetime
import numpy as np
import numexpr as ne

expr = '11808000.0*1j*x**2*exp(2.5e-10*1j*x) + 1512000.0*1j*x**2*exp(5.0e-10*1j*x)'


# use eval
start_eval = datetime.datetime.now()
namespace = dict(x=np.array([m+3j for m in range(1, 1001)]), exp=np.exp)
result_eval = eval(expr, namespace)
end_eval = datetime.datetime.now()
# print(result)
print("time by using eval   : %s" % (end_eval- start_eval))


# use numexpr
# ne.set_num_threads(8)
start_ne = datetime.datetime.now()
x = np.array([n+3j for n in range(1, 1001)])
result_ne = ne.evaluate(expr)
end_ne = datetime.datetime.now()
# print(result_ne)
print("time by using numexpr: %s" % (end_ne- start_ne))

return: time by using eval : 0:00:00.002998

time by using numexpr: __ 0:00:00.052969

Solution

Thank you all

I have get the answer from robbmcleod in Github

for NumExpr 2.6 you'll need arrays around 128 - 256 kElements to see a speed-up. NumPy always starts faster as it doesn't have to synchronize at a thread barrier and otherwise spin-up the virtual machine

Also, once you call numexpr.evaluate() the second time, it should be faster as it will have already compiled everything. Compilation takes around 0.5 ms for simple expressions, more for longer expressions. The expression is stored as a hash, so the next time that computational expense is gone.