On Sat, Feb 22, 2014 at 7:45 PM, Ronan Lamy <ronan.l...@gmail.com> wrote: > Hello Ian, > > Le 20/02/14 20:40, Ian Ozsvald a écrit : > >> Hi Armin. The point of the question was not to remove numpy but to >> understand the behaviour :-) I've already done a set of benchmarks >> with lists and with numpy, I've copied the results below. I'm using >> the same Julia code throughout (there's a note about the code below). >> PyPy on lists is indeed very compelling. >> >> One observation I've made of beginners (and I did the same) is that >> iterating over numpy arrays seems natural until you learn it is >> horribly slow. The you learn to vectorise. Some of the current tools >> handle the non-vectorised case really well and that's something I want >> to mention. >> >> For Julia I've used lists and numpy. Using a numpy list rather than an >> `array` makes sense as arrays won't hold a complex type (and messing >> with decomposing the complex elements into two arrays gets even >> sillier) and the example is still trivial for a reader to understand. >> numpy arrays (and Python arrays) are good because they use much less >> RAM than big lists. The reason why my example code above made lists >> and then turned them into numpy arrays...that's because I was lazy and >> hadn't finished tidying this demo (my bad!). > > > I agree that your code looks rather sensible (at least, to people who > haven't internalised yet all the "stupid" implementation details concerning > arrays, lists, iteration and vectorisation). So it's a bit of a shame that > PyPy doesn't do better. > > >> I don't mind that my use of numpy is silly, I'm just curious to >> understand why pypynumpy diverges from the results of the other >> compiler technologies. The simple answer might be 'because pypynumpy >> is young' and that'd be fine - at least I'd have an answer if someone >> asks the question in my talk. If someone has more details, that'd be >> really interesting too. Is there a fundamental reason why pypynumpy >> couldn't do the example as fast as cython/numba/pythran? > > > To answer such questions, the best way is to use the jitviewer > (https://bitbucket.org/pypy/jitviewer ). Looking at the trace for the inner > loop, I can see every operation on a scalar triggers a dict lookup to obtain > its dtype. This looks like self-inflicted pain coming the broken objspace > abstraction rather than anything fundamental. Fixing that should improve > speed by about an order of magnitude. > > Cheers, > Ronan >
Hi Ronan. You can't blame objspace for everything ;-) It looks like it's easily fixable. I'm in transit right now but I can fix it once I'm home. Ian - please come with more broken examples, they usually come from stupid reasons! Cheers, fijal _______________________________________________ pypy-dev mailing list pypy-dev@python.org https://mail.python.org/mailman/listinfo/pypy-dev
