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
- Object cleanup tied to lifetime of objects. This idiom, often
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.
- Helpful Documentation and a
Relatedly, like-minded computing goodness for OpenCL
is provided by PyCUDA’s sister project PyOpenCL.
TODO: Brief introduction on what you do with files - including link to relevant help section.