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Numpy can be “linked/compiled” against different BLAS implementations (MKL, ACML, ATLAS, GotoBlas, etc). That’s not always straightforward to configure but it is possible.
Is it also possible to “link/compile” numpy against NVIDIA’s CUBLAS implementation?
I couldn’t find any resources in the web and before I spend too much time trying it I wanted to make sure that it possible at all.
In a word: no, you can’t do that.
There is a rather good scikit which provides access to CUBLAS from scipy called
scikits.cudawhich is built on top of PyCUDA. PyCUDA provides anumpy.ndarraylike class which seamlessly allows manipulation of numpy arrays in GPU memory with CUDA. So you can use CUBLAS and CUDA with numpy, but you can’t just link against CUBLAS and expect it to work.There is also a commercial library that provides numpy and cublas like functionality and which has a Python interface or bindings, but I will leave it to one of their shills to fill you in on that.