On Fri, Jun 17, 2016 at 9:22 AM, Robert Kern <robert.k...@gmail.com> wrote:
> On Fri, Jun 17, 2016 at 4:08 PM, Pavlyk, Oleksandr < > oleksandr.pav...@intel.com> wrote: > > > > 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. > > Cool! You can find pertinent discussion here: > > https://github.com/numpy/numpy/issues/6967 > > And the current effort for adding new core PRNGs here: > > https://github.com/bashtage/ng-numpy-randomstate > I wonder if the easiest thing to do at this point might be to implement a new redesigned random module and keep the old one around for backward compatibility? Not that that would make everything easy, but at least folks could choose to use the new functions for speed and versatility if they needed them. The current random module is pretty stable so maintenance should not be too onerous. Chuck
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