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

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.