Received from Geoffrey Anderson on Fri, Apr 05, 2013 at 04:18:30PM EDT:
(snip)
> That said, how could my application use *both* of the GPU devices that I have
> installed on my workstation (or server)? I am using a collection of GPU
> devices in the larger context of research in parallelizing application
> software for the CPU as well as for the GPU. I already have developed an
> inventory of applications that are parallelizable on CPUs. Therefore
> naturally it would make quite a lot of sense for me to partition the
> independent parts of the computations within my applications, and send one
> work partition to each of these GPU devices. I envision that the results may
> be computed at the same time on my two CUDA devices, and then the results
> returned to the host application for final aggregation of the partial results
> after a parallel barrier, or after the completion of a sequence of blocking
> join calls one per CUDA device.
>
> Here's the thing: The current API of the PyCUDA system seems to be designed or
> intended to use just one CUDA device at a time by any given application. The
> current API looks at the environment variable CUDA_DEVICE or the special disk
> file, to decide which GPU device to the send the work to. I am having a hard
> time to think of a way to use such an API to drive both GPU devices in a
> reliable or predictable manner. I would love to see the existing work of
> others in this regard, if you would like to share it with us on the mailing
> list.
My colleagues and I have been implementing a small Python package to complement
PyCUDA with extra support for multiple GPUs. At the moment, we are focusing on
facilitating several data transfer patterns between GPUs (e.g., using GPUDirect
2.0 when possible) that are needed for some other research projects, but we are
thinking about implementing other features as well.
We are hoping to release some alpha code on Github next month for folks to try
out; I'll let the list know when we do so.
L.G.
Bionet Group,
Columbia University
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