[Numpy-discussion] NumPy to CPU+GPU compiler, looking for tests

Rahul Garg rahulgarg44@gmail....
Sun Oct 21 13:57:04 CDT 2012


Thanks! I need to add support for eig and inv (will do this week, at
least for CPU) but other than that, I should definitely be able to
handle those kinds of benchmarks.

rahul

On Sun, Oct 21, 2012 at 12:01 PM, Aron Ahmadia <aron@ahmadia.net> wrote:
> Hi Rahul,
>
> Very cool!  I'm looking forward to seeing some performance results!  Anders
> Logg posted a computational challenge to G+ about a month ago, and we got
> entries in Octave, Fortran, Python, and Julia (all implementing the same
> solution from Jed Brown).  The challenge is here:
>
> https://plus.google.com/116518787475147930287/posts/jiULACjiGnW
>
> Here is my simple attempt at Cythonizing Jed's Octave code:
>
> https://gist.github.com/3893361
>
> The best solution in Fortran took 38 microseconds.  The best Python solution
> clocked in at around 445.  The Julia solution implemented by Jed took around
> 224 microseconds, a good LLVM solution should come close to or beat that.
>
> Hope this helps.
>
> Aron
>
> On Sun, Oct 21, 2012 at 3:27 PM, Rahul Garg <rahulgarg44@gmail.com> wrote:
>>
>> Hi.
>>
>> I am a PhD student at McGill University and I am developing a compiler
>> for Python for CPUs and GPUs. For CPUs, I build upon LLVM. For GPUs, I
>> generate OpenCL and I have also implemented some library functions on
>> the GPU myself. The restriction that it is only for numerical code and
>> intended for NumPy users. The compiler is aware of simple things in
>> NumPy like matrix multiplication, slicing operators, strided layouts,
>> some library functions (though limited at this time) and the negative
>> indexing semantics etc. However, the compiler is not limited to vector
>> code. Scalar code or manually written loops also work. However, only
>> numerical datatypes are supported with no support for lists, dicts,
>> classes etc. First class functions are not currently supported but are
>> on the roadmap. You will have to add some type annotations to your
>> functions. If you have a compatible GPU, you can also use the GPU by
>> indicating which parts to run on the GPU. Otherwise you can just use
>> it to run your code on the CPU.
>>
>> As an example, simple scalar code like fibonacci function works fine.
>> Simple loops like those used in stencil-type computations are also
>> working. Parallel-for loops are also provided and working. Simple
>> vector oriented code is also working fine on both CPU and GPU.  The
>> system is being tested on Ubuntu 12.04 and tested with Python 2.7
>> (though I think should work with other Python 2.x variants). For GPUs,
>> I am ensuring that the system works with AMD and Nvidia GPUs.
>>
>> The compiler is in early stages and I am looking for test cases. The
>> project will be open-sourced in November under Apache 2 and thereafter
>> will be developed in an open repo. If you have some simple code that I
>> can use as a benchmark that I can use to test and evaluate the
>> compiler, that will be very helpful. Some annotations will be
>> required, which I can help you write. I will be VERY grateful to
>> anyone who can provide test cases. In turn, it will help improve the
>> compiler and everyone will benefit.
>>
>> Some of you may be wondering how it compares to Numba. Well it is
>> essentially very similar in the idea.  So why build a new compiler
>> then? Actually the project I am building is not specific to Python.  I
>> am building a far more general compiler infrastructure for array
>> languages, and Python frontend is just one small part of the project.
>> For example, I am also working on a MATLAB frontend.
>>
>> (Some of you may remember me from an earlier compiler project which
>> unfortunately went nowhere. This is a different project and this time
>> I am determined to convert it into a usable system. I realize the
>> proof is in the pudding, so I hope to convince people by releasing
>> code soon.)
>>
>> thanks,
>> Rahul
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>
>
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