Re. micronumpy - all I'm doing in the Mandelbrot demo is multiplying and addition on doubles - that'll work now, right? If so I'll make the modification in a week or so when I next have some time.
I'm guessing that there is no 'complex' support yet, even for basic operations? Adding micronumpy support would make for a nice addition to the v0.2 doc :-) Ian. re. the 6 month 'proper numpy' estimate - I'm just quoting some of the figures that were thrown around (with wide error margins depending on money raised, availability etc). I would *love* to see support come through sooner! On 3 July 2011 19:54, Maciej Fijalkowski <fij...@gmail.com> wrote: > On Sun, Jul 3, 2011 at 8:35 PM, Ian Ozsvald <i...@ianozsvald.com> wrote: >> Hi Maciej. I said that based on what was discussed in Armin+Antonio's >> talk at EuroPython. If it takes 6+months for a numpy re-implementation >> then I'm suggesting that we're into 2012. I'd be *very* happy to be >> proved wrong! > > I would estimate numpy for less than 6 months (numpy, not scipy and > not matplotlib), but well that's me. There is also a lot of outside > contribution, so it might take less wall time than manmonth time. From > my perspective, if we can secure *some* funding for numpy, it should > be ready sooner than 6 months from now. > >> >> Do Python 'array' objects run faster than lists in PyPy? I believe >> that in CPython they run at the same speed (i.e. they're just a >> convenient storage system, they don't offer any of numpy's efficient >> math benefits). > > The situation between PyPy and CPython is quite different here. As of > now, both PyPy and CPython store wrapped objects in lists (let's call > them PyIntObject) and unwrapped (C level int) in numpy arrays. In case > you have only interpreter, when reading a list you do: > > py_x = list[index] > do_something_with_py_x > > when reading from numpy array (or array.array), you do: > > py_x = new PyIntObject(array[index]) > do_something_with_py_x > > so you use less memory (cache is better), but you allocate a new > object each iteration (bad) > > But in case of the JIT, what happens is (list case) > > py_x = list[index] > x = py_x.value > do_something_with_x # x as in integer value not py_x > > array: > > x = list[index] > do_something_with_x > > Hence, no allocation and memory saving - huge win! > >> >> Does the micronumpy library support doubles yet? If so I'd be happy to >> give it a go. If 'micronumpy' is the wrong name then tell me where I >> should look, I'm just going on what I've remembered from the PyPy-blog >> discussions about numpy support. > > yes, doubles is pretty much all it supports as of now :) micronumpy is > a good name, but it comes under a name "numpy", so you can do "import > numpy". Not much will work though :-) > > Note that we're also in the process of implementing faster vectorized > operations, like numpyexpr, except better. The list of operations is > very limited as of now though. > > Cheers, > fijal > > PS. If it's unclear, I can try to explain more or pop up on IRC. > -- Ian Ozsvald (A.I. researcher, screencaster) i...@ianozsvald.com http://IanOzsvald.com http://SocialTiesApp.com/ http://MorConsulting.com/ http://blog.AICookbook.com/ http://TheScreencastingHandbook.com http://FivePoundApp.com/ http://twitter.com/IanOzsvald _______________________________________________ pypy-dev mailing list pypy-dev@python.org http://mail.python.org/mailman/listinfo/pypy-dev
