[Numpy-discussion] ANN: PyCuda

Andreas Klöckner lists@informa.tiker....
Sun Jun 22 10:02:24 CDT 2008

Hi all,

I am happy to announce the availability of PyCuda [1,8], which is a 
value-added Python wrapper around Nvidia's CUDA [2] GPU Computation 
framework. In the presence of other wrapping modules [3,4], why would you 
want to use PyCuda?

* It's designed to work and interact with numpy.

* RAII, [5] i.e. object cleanup is tied to lifetime of objects. This idiom 
makes it much easier to write correct, leak- and crash-free code. PyCuda 
knows about liftime dependencies, too, so (for example) it won’t detach from 
a context before all memory allocated in it is also freed.

* Convenience. Abstractions like pycuda.driver.SourceModule [6] and 
pycuda.gpuarray.GPUArray [7] make CUDA programming even more convenient than 
with Nvidia’s C-based runtime.

* Completeness. PyCuda puts the full power of CUDA’s driver API at your 
disposal, if you wish.

* Automatic Error Checking. All CUDA errors are automatically translated into 
Python exceptions.

* Speed. PyCuda’s base layer is written in C++, so all the niceties above are 
virtually free.

* Helpful documentation [8] with plenty of examples.

If you run into any issues using the code, don't hesitate to post here or get 
in touch.


[1] http://mathema.tician.de/software/pycuda
[2] http://nvidia.com/cuda
[3] http://code.google.com/p/pystream/
[4] ftp://ftp.graviscom.com/pub/code/python-cuda
[5] http://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization
[6] http://tiker.net/doc/pycuda/driver.html#pycuda.driver.SourceModule
[7] http://tiker.net/doc/pycuda/array.html#pycuda.gpuarray.GPUArray
[8] http://tiker.net/doc/pycuda <-- click here!
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