======================== Announcing Numexpr 2.0 ======================== Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It wears multi-threaded capabilities, as well as support for Intel's VML library, which allows for squeezing the last drop of performance out of your multi-core processors. What's new ========== This version comes with support for the new iterator in NumPy (introduced in NumPy 1.6), allowing for improved performance in practically all the scenarios (the exception being very small arrays), and most specially for operations implying broadcasting, fortran-ordered or non-native byte orderings. The carefully crafted mix of the new NumPy iterator and direct access to data buffers turned out to be so powerful and flexible, that the internal virtual machine has been completely revamped around this combination. The drawback is that you will need NumPy >= 1.6 to run numexpr 2.0. However, NumPy 1.6 has been released more than 6 months ago now, so we think this is a good time for taking advantage of it. Many thanks to Mark Wiebe for such an important contribution! For some benchmarks on the new virtual machine, see: http://code.google.com/p/numexpr/wiki/NewVM Also, Gaëtan de Menten contributed important bug fixes, code cleanup as well as speed enhancements. Francesc Alted contributed some fixes, and added compatibility code with existing applications (PyTables) too. In case you want to know more in detail what has changed in this version, see: http://code.google.com/p/numexpr/wiki/ReleaseNotes or have a look at RELEASE_NOTES.txt in the tarball. Where I can find Numexpr? ========================= The project is hosted at Google code in: http://code.google.com/p/numexpr/ You can get the packages from PyPI as well: http://pypi.python.org/pypi/numexpr Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy! -- Francesc Alted
This is great news, I hope this gets included in the epd distribution soon. I had mailed a few questions about numexpr sometime ago. I am still curious about those. I have included the relevant parts below. In addition, I have another question. There was a numexpr branch that allows a "out=blah" parameer to build the output in place, has that been merged or its functionality incorporated ? This goes without saying, but, thanks for numexpr. -- from old mail -- What I find somewhat encumbering is that there is no single piece of document that lists all the operators and functions that numexpr can parse. For a new user this will be very useful There is a list in the wiki page entitled "overview" but it seems incomplete (for instance it does not describe the reduction operations available). I do not know enough to know how incomplete it is. Is there any plan to implement the reduction like enhancements that ufuncs provide: namely reduce_at, accumulate, reduce ? It is entirely possible that they are already in there but I could not figure out how to use them. If they aren't it would be great to have them. On Sun, Nov 27, 2011 at 7:00 AM, Francesc Alted <faltet@pytables.org> wrote:
======================== Announcing Numexpr 2.0 ========================
Hi srean, Sorry for being late answering, the latest weeks have been really crazy for me. See my comments below. 2011/12/13 srean <srean.list@gmail.com>:
This is great news, I hope this gets included in the epd distribution soon.
I had mailed a few questions about numexpr sometime ago. I am still curious about those. I have included the relevant parts below. In addition, I have another question. There was a numexpr branch that allows a "out=blah" parameer to build the output in place, has that been merged or its functionality incorporated ?
Yes, the `out` parameter is fully supported in 2.0 series, as well as new `order` and `casting` ones. These are fully documented in docstrings in forthcoming 2.0.1, as well as in the new User's Guide wiki page at: http://code.google.com/p/numexpr/wiki/UsersGuide Thanks for pointing this out!
This goes without saying, but, thanks for numexpr.
-- from old mail --
What I find somewhat encumbering is that there is no single piece of document that lists all the operators and functions that numexpr can parse. For a new user this will be very useful There is a list in the wiki page entitled "overview" but it seems incomplete (for instance it does not describe the reduction operations available). I do not know enough to know how incomplete it is.
The reduction functions are just `sum()` and `prod()` and are fully documented in the new User's Guide.
Is there any plan to implement the reduction like enhancements that ufuncs provide: namely reduce_at, accumulate, reduce ? It is entirely possible that they are already in there but I could not figure out how to use them. If they aren't it would be great to have them.
No, these are not implemented, but we will gladly accept contributions ;) -- Francesc Alted
participants (3)
-
Francesc Alted -
Francesc Alted -
srean