On Mon, Oct 17, 2011 at 12:10 AM, Armin Rigo <ar...@tunes.org> wrote: > Hi, > > On Sun, Oct 16, 2011 at 23:41, David Cournapeau <courn...@gmail.com> wrote: >> Interesting to know. But then, wouldn't this limit the speed gains to >> be expected from the JIT ? > > Yes, to some extent. It cannot give you the last bit of performance > improvements you could expect from arithmetic optimizations, but (as > usual) you get already the several-times improvements of e.g. removing > the boxing and unboxing of float objects. Personally I'm wary of > going down that path, because it means that the results we get could > suddenly change their least significant digit(s) when the JIT kicks > in. At least there are multiple tests in the standard Python test > suite that would fail because of that.
The thing is that as with python there are scenarios where we can optimize a lot (like you said by doing type specialization or folding array operations or using multithreading based on runtime decisions) where we don't have to squeeze the last 2% of performance. This is the approach that worked great for optimizing Python so far (concentrate on the larger picture). > >> And I am not sure I understand how you can "not go there" if you want >> to vectorize code to use SIMD instruction sets ? > > I'll leave fijal to answer this question in detail :-) I suppose that > the goal is first to use SIMD when explicitly requested in the RPython > source, in the numpy code that operate on matrices; and not do the > harder job of automatically unrolling and SIMD-ing loops containing > Python float operations. But even the later could be done without > giving up on the idea that all Python operations should be present in > a bit-exact way (e.g. by using SIMD on 64-bit floats, not on 32-bit > floats). For now we restrict SIMD operations to explicit array arithmetics and we don't do automatic vectorization. We'll see later what we do with it :) Cheers, fijal _______________________________________________ pypy-dev mailing list pypy-dev@python.org http://mail.python.org/mailman/listinfo/pypy-dev
