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PyCUDA: Pythonic Access to CUDA, with Arrays and Algorithms
PyCUDA lets you access Nvidia's CUDA parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about PyCUDA?
Object cleanup tied to lifetime of objects. This idiom, often
called
RAII
in C++, makes it much easier to write correct, leak- and
crash-free code. PyCUDA knows about 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 and
pycuda.gpuarray.GPUArray 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. It also includes code for
interoperability with OpenGL.
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.