Yes, Julian is doing an amazing work on getting rid of temporaries inside
NumPy. However, NumExpr still has the advantage of using multi-threading
right out of the box, as well as integration with Intel VML. Hopefully
these features will eventually arrive to NumPy, but meanwhile there is
Just a side note that at a smaller scale some of the benefits of
numexpr are coming to numpy: Julian Taylor has been working on
identifying temporary arrays in
https://github.com/numpy/numpy/pull/7997. Julian also commented
A guy on reddit suggested looking at SymPy for just such a thing. I know
that Dask also represents its process as a graph.
I'll think about it some more but it seems a little abstract still. To a
certain extent the NE3 compiler
Thanks for this. It looks absolutely fantastic. I've been putting off using
numexpr but it looks like I don't have a choice anymore. ;)
Regarding feature requests, I've always found it off putting that I have to
wrap my expressions in a string to speed them up. Has anyone explored
Thanks for your comments, reply below the fold.
On Fri, Feb 17, 2017 at 4:34 PM, Daπid wrote:
> This is very nice indeed!
> On 17 February 2017 at 12:15, Robert McLeod wrote:
> > * bytes and unicode support
> > * reductions (mean, sum,
This is very nice indeed!
On 17 February 2017 at 12:15, Robert McLeod wrote:
> * bytes and unicode support
> * reductions (mean, sum, prod, std)
I use both a lot, maybe I can help you get them working.
Also, regarding "Vectorization hasn't been done yet with cmath
Yay! This looks really exciting. Thanks for all the hard work!
2017-02-17 12:15 GMT+01:00 Robert McLeod :
> Hi everyone,
> I'm pleased to announce that a new branch of NumExpr has been developed
> that will hopefully lead to a new major version release in the