Hi Max, There are two similar projects I know of: https://github.com/mdipierro/ocl (Python to C/OpenCL/JS translator) https://github.com/copperhead/copperhead (Python to CUDA translator/compiler) Both of them seem to be somewhat abandoned.
As a matter of fact, I've been tentatively working on a similar project myself recently. Although I decided to start from implementing a partial evaluation library (a big rework of an existing abandoned project, to be accurate) to serve as a replacement for templates. The translator would be the next step, when I had enough control on the partial evaluation stage (e.g. could tell the evaluator to inline python functions, unroll loops and so on). My main aim was to be able to rewrite Reikna templates completely in Python (including non-trivial template flow, template functions and macros they have in abundance). The partial evaluator is located at https://github.com/Manticore/peval . It is currently in working condition, but there's a big change incoming that will allow me to separate different optimization strategies it employs and make the control flow more predictable. After that I expect I will be able to make a prototype of a translator. The partial evaluation and translation stages are mostly independent, so you may find it useful for your project too. Although it would be nice to join our efforts on this task if you plan to develop your translator further. Best regards, Bogdan On Thu, Apr 24, 2014 at 6:40 AM, Max Argus <argus....@gmail.com> wrote: > Hi, > > I am looking for a python based metaprogramming library that will > allow me to tune programs using python and then export the optimal > cuda (for now) code to a file so that it can be used from a c++ > application. > > 1) What is the best choice for this? > > The metaprogramming systems associated with pycuda are templating > based systems or codepy. Codepy seemed to be recommended by the > documentation. > > However the problem that I encountered was that the codepy > representation of code was only marginally understandable. Because I > wasn't too keen on converting a bigger kernel into this format I had a > look around. I noticed that the codepy classes were very similar to > the python AST, for obvious reasons. > > Based on this I wrote an (incomplete) AST transformer that converted a > python implementation of the codepy demo program into the codepy > representation. > > This is more or less easy enough for the simple program provided. > However the question is if this is still a viable way to do things > when kernels get more complicated. The python AST transformer will > allow one to do pretty much every possible code transformation, in the > end it probably comes down not if it will it be possible to add all of > python to c translation hints/options e.g. loop unrolling ect. in a > way that keeps the code legal python and readable. > At the moment I think this is feasible since pretty much everything > can be wrapped in (or preceded) by a (empty) function who's name can > be used to influence AST transformer behavior. > > Next I will try to implement a 2D convolution. > > 2) Is there a frontend script for codepy that compiles and evaluates > the kernel performance? > > At the moment this seems like the most pythonic way to do > metaprogramming to me in terms of readability and flexibility, though > not in ease of implementation. > > 3) In general does this approach seem sane to you guys, and if does > what should it look like in order to be useful to others too? > > > BR, Max > > _______________________________________________ > PyCUDA mailing list > PyCUDA@tiker.net > http://lists.tiker.net/listinfo/pycuda > _______________________________________________ PyCUDA mailing list PyCUDA@tiker.net http://lists.tiker.net/listinfo/pycuda
