Did you saw the gpu nd array project? We try to do something similar but only for the GPU.
https://github.com/inducer/compyte/wiki Fred On Sun, Oct 21, 2012 at 2:57 PM, Rahul Garg <rahulgar...@gmail.com> wrote: > 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 <a...@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 <rahulgar...@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 >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
