[Cython] Fwd: Re: [Numpy-discussion] Proposed Roadmap Overview

Robert Bradshaw robertwb at math.washington.edu
Tue Feb 21 21:02:41 CET 2012

On Tue, Feb 21, 2012 at 9:19 AM, mark florisson
<markflorisson88 at gmail.com> wrote:
> On 21 February 2012 04:42, Robert Bradshaw <robertwb at math.washington.edu> wrote:
>> Python bytecode -> LLVM is a great idea for creating ufuncs, the
>> overhead of Cython + GCC is atrocious for stuff like this. (I think
>> Cython could make a good frontent as well, especially if we generated
>> just the .c code for the function rather than a full extension module
>> and used a good compiler to generate machine code in-memory
>> on-the-fly.)
> I want elementwise functions and reductions for Cython as well, but
> they would be specialized according to the types you use them for, so
> it wouldn't be atrocious bloat (at least, no more then you'd already
> be having).
> I've been giving profile guided optimizations some thoughts as well, I
> think they are most neatly handled through a python tracer that is
> installed e.g. through sitecustomize which collects data about types,
> time spent in which code regions etc during testing and development.
> You can then tell Cython to compile the module, so e.g. if it
> registered "a numpy array" it could generate some specializations for
> dtypes either statically or dynamically through OpenCL. The
> alternative approach using code instrumentation would be more of a
> chore but would work just as well for Cython code with object-typed
> arguments. That's all rather far off, and this looks like an
> interesting development.

One of the reasons JIT compilers can do so well is that they can use
runtime profile information to write specialized code. It'd be great
to be able to incorporate this kind of profiling into a Cython compile
too. One of the big missing features here is a "fallback mode" where
we can insert guards and fall back to generic code if they're not met,
which I think would open us up to a huge number of optimizations
without violating Python semantics.

>> Going too deeply though and you'll be starting to duplicate the work
>> of Unladen Swallow...
>> On Mon, Feb 20, 2012 at 8:09 PM, Dag Sverre Seljebotn
>> <d.s.seljebotn at astro.uio.no> wrote:
>>> This has got to be the most incredible and interesting turn of events I've
>>> seen in a while! :-)
>>> Dag
>>> -------- Original Message --------
>>> Subject:        Re: [Numpy-discussion] Proposed Roadmap Overview
>>> Date:   Mon, 20 Feb 2012 22:04:00 -0600
>>> From:   Travis Oliphant <travis at continuum.io>
>>> Reply-To:       Discussion of Numerical Python <numpy-discussion at scipy.org>
>>> To:     Discussion of Numerical Python <numpy-discussion at scipy.org>
>>> Interesting you bring this up. I actually have a working prototype of
>>> using Python to emit LLVM. I will be showing it at the HPC tutorial that
>>> I am giving at PyCon. I will be making this available after PyCon to a
>>> wider audience as open source.
>>> It uses llvm-py (modified to work with LLVM 3.0) and code I wrote to do
>>> the translation from Python byte-code to LLVM. This LLVM can then be
>>> "JIT"ed. I have several applications that I would like to use this for.
>>> It would be possible to write "more of NumPy" using this approach.
>>> Initially, it makes it *very* easy to create a machine-code ufunc from
>>> Python code. There are other use-cases of having loops written in Python
>>> and plugged in to a calculation, filtering, or indexing framework that
>>> this system will be useful for.
>>> There is still a need for a core data-type object, a core array object,
>>> and a core calculation object. Maybe some-day these cores can be shrunk
>>> to a smaller subset and more of something along the lines of LLVM
>>> generation from Python can be used. But, there is a lot of work to do
>>> before that is possible. But, a lot of the currently pre-compiled loops
>>> can be done on the fly instead using this approach. There are several
>>> things I'm working on in that direction.
>>> This is not PyPy. It certainly uses the same ideas that they are using,
>>> but instead it fits into the CPython run-time and doesn't require
>>> changing the whole ecosystem. If you are interested in this work let me
>>> know. I think I'm going to call the project numpy-llvm, or fast-py, or
>>> something like that. It is available on github and will be open source
>>> (but it's still under active development).
>>> Here is an example of the code to create a ufunc using the system (this
>>> is like vectorize, but it creates machine code and by-passes the
>>> interpreter and so is 100x faster).
>>> from math import sin, pi
>>> def sinc(x):
>>> if x==0:
>>> return 1.0
>>> else:
>>> return sin(x*pi)/(pi*x)
>>> from translate import Translate
>>> t = Translate(sinc)
>>> t.translate()
>>> print t.mod
>>> res = t.make_ufunc('sinc')
>>> -Travis
>>> On Feb 20, 2012, at 10:55 AM, Sturla Molden wrote:
>>>> Den 20.02.2012 17:42, skrev Sturla Molden:
>>>>> There are still other options than C or C++ that are worth considering.
>>>>> One would be to write NumPy in Python. E.g. we could use LLVM as a
>>>>> JIT-compiler and produce the performance critical code we need on the
>>>>> fly.
>>>> LLVM and its C/C++ frontend Clang are BSD licenced. It compiles faster
>>>> than GCC and often produces better machine code. They can therefore be
>>>> used inside an array library. It would give a faster NumPy, and we could
>>>> keep most of it in Python.
>>>> Sturla
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