[Numpy-discussion] Proposed Roadmap Overview
Mon Feb 20 11:44:50 CST 2012
Den 20.02.2012 18:18, skrev Dag Sverre Seljebotn:
> I think it is moot to focus on improving NumPy performance as long as in
> practice all NumPy operations are memory bound due to the need to take a
> trip through system memory for almost any operation. C/C++ is simply
> "good enough". JIT is when you're chasing a 2x improvement or so, but
> today NumPy can be 10-20x slower than a Cython loop.
> You need at least a slightly different Python API to get anywhere, so
> numexpr/Theano is the right place to work on an implementation of this
> idea. Of course it would be nice if numexpr/Theano offered something as
> convenient as
> with lazy:
> arr = A + B + C # with all of these NumPy arrays
> # compute upon exiting...
Lazy evaluation is nice. But I was thinking more about how to avoid C++
in the NumPy core, so more than 2 or 3 programmers could contribute.
I.e. my point was not that loops in LLVM would be much faster than C++
(that is besides the point), but the code could be written in Python
instead of C++.
But if the idea is to support other languages as well (which I somehow
forgot), then this approach certainly becomes less useful.
(OTOH, lazy evaluation is certainly easier to achieve with JIT
compilation. But that will have to wait until NumPy 5.0 perhaps...)
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