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. Andreas [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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