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Has anyone here used double precision scientific computing with new generation (e.g. K20) GPUs through Python?

I know that this technology is rapidly evolving, but what is the best way to do this currently? GPU is out of scope for the popular scientific Python libraries numpy and scipy, and I had wanted to use theano but it seems to use only float32 precision for GPU. I am aware that google can provide search results for python gpu, but I am hoping for more insight than a list of projects which may or may not be on their way to meet their maker.

clipper
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    If I needed this I would probably use PyOpenCL. General purpose GPU coding is still quite low level (try OpenCL C interface, it's tough going). Yet PyOpenCL seems to abstract as much as possible and appears to have considerable momentum behind it. – boyfarrell Jun 12 '13 at 01:59
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    "new generation" should be fairly similar to earlier Tesla series with double precision, with probably the only caveat being CUDA/driver version. So double precision methods that work with Tesla (e.g. M2070) and the current CUDA/Driver version should also work the K20. – internetscooter Jun 12 '13 at 02:51
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    Maybe here(http://stackoverflow.com/questions/5957554/python-gpu-programming) might give some help to you. – imbr Jul 29 '13 at 15:29
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    Theano have a new GPU back-end that support float64. It is not complete yet, but we will anouce it in beta status this week. – nouiz Dec 09 '13 at 21:09
  • Thanks @nouiz - I'd suggest adding your comment as an answer when you make the release. – Aron Ahmadia Dec 09 '13 at 21:57
  • Theano new GPU back-end support this: http://deeplearning.net/software/theano/tutorial/using_gpu.html#gpuarray-backend – nouiz Oct 09 '15 at 19:44

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I don't know why I put this answer in a comment...

If I needed this I would probably use PyOpenCL. General purpose GPU coding is still quite low level (try OpenCL C interface, it's tough going). Yet PyOpenCL seems to abstract as much as possible and appears to have considerable momentum behind it.

boyfarrell
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