Hi,
I am new to this list, so I will start with an introduction. My name is
Oleksandr Pavlyk. I now work at Intel Corp. on the Intel Distribution for
Python, and previously worked at Wolfram Research for 12 years. My latest
project was to write a mirror to numpy.random, named numpy.random_intel. The
module uses MKL to sample from different distributions for efficiency. It
provides support for different underlying algorithms for basic pseudo-random
number generation, i.e. in addition to MT19937, it also provides SFMT19937,
MT2203, etc.
I recently published a blog about it:
https://software.intel.com/en-us/blogs/2016/06/15/faster-random-number-generation-in-intel-distribution-for-python
I originally attempted to simply replace numpy.random in the Intel Distribution
for Python with the new module, but due to fixed seed backwards incompatibility
this results in numerous test failures in numpy, scipy, pandas and other
modules.
Unlike numpy.random, the new module generates a vector of random numbers at a
time, which can be done faster than repeatedly generating the same number of
variates one at a time.
The source code for the new module is not upstreamed yet, and this email is
meant to solicit early community feedback to allow for faster acceptance of the
proposed changes.
Thank you,
Oleksandr
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