Numpy performance differences depending on numerical values

Question

I found a strange performance difference while evaluating an expression in Numpy.

I executed the following code:

import numpy as np
myarr = np.random         


        

Answer 1

Use Intel SVML

I have no working numexpr with Intel SVML, but numexpr with working SVML should perform as good as Numba. The Numba Benchmarks show quite the same behaviour without SVML, but perform much better with SVML.

Code

import numpy as np
import numba as nb

myarr = np.random.uniform(-1,1,[1100,1100])

@nb.njit(error_model="numpy",parallel=True)
def func(arr,div):
  return np.exp( - 0.5 * (myarr / div)**2 )

Timings

#Core i7 4771
#Windows 7 x64
#Anaconda Python 3.5.5
#Numba 0.41 (compilation overhead excluded)
func(myarr,0.1)                      -> 3.6ms
func(myarr,0.001)                    -> 3.8ms

#Numba (set NUMBA_DISABLE_INTEL_SVML=1), parallel=True
func(myarr,0.1)                      -> 5.19ms
func(myarr,0.001)                    -> 12.0ms

#Numba (set NUMBA_DISABLE_INTEL_SVML=1), parallel=False
func(myarr,0.1)                      -> 16.7ms
func(myarr,0.001)                    -> 63.2ms

#Numpy (1.13.3), set OMP_NUM_THREADS=4
np.exp( - 0.5 * (myarr / 0.001)**2 ) -> 70.82ms
np.exp( - 0.5 * (myarr / 0.1)**2 )   -> 12.58ms

#Numpy (1.13.3), set OMP_NUM_THREADS=1
np.exp( - 0.5 * (myarr / 0.001)**2 ) -> 189.4ms
np.exp( - 0.5 * (myarr / 0.1)**2 )   -> 17.4ms

#Numexpr (2.6.8), no SVML, parallel
ne.evaluate("exp( - 0.5 * (myarr / 0.001)**2 )") ->17.2ms
ne.evaluate("exp( - 0.5 * (myarr / 0.1)**2 )")   ->4.38ms

#Numexpr (2.6.8), no SVML, single threaded
ne.evaluate("exp( - 0.5 * (myarr / 0.001)**2 )") ->50.85ms
ne.evaluate("exp( - 0.5 * (myarr / 0.1)**2 )")   ->13.9ms

Answer 2

This may produce denormalised numbers which slow down computations.

You may like to disable denormalized numbers using daz library:

import daz
daz.set_daz()

More info: x87 and SSE Floating Point Assists in IA-32: Flush-To-Zero (FTZ) and Denormals-Are-Zero (DAZ):

To avoid serialization and performance issues due to denormals and underflow numbers, use the SSE and SSE2 instructions to set Flush-to-Zero and Denormals-Are-Zero modes within the hardware to enable highest performance for floating-point applications.

Note that in 64-bit mode floating point computations use SSE instructions, not x87.