Karl,
At the moment, the only 'request' has been in the issue I linked to
previously where a user has a matrix too large to fit on one GPU. This can
be addressed by explicit matrix chunking that I could handle perhaps more
simply from the R end. It just starts to get more complex with the solvers.
I'm not as familiar with those algorithms so not sure, at least right now,
how I would approach that. If you know I would certainly appreciate the
insight.
Regards,
Charles
On Thu, Apr 28, 2016 at 11:59 AM, Karl Rupp <r...@iue.tuwien.ac.at
<javascript:_e(%7B%7D,'cvml','r...@iue.tuwien.ac.at');>> wrote:
> Hi Charles,
>
> > Thanks for getting back, there isn't anything else at the moment. The
>
>> issue was needing to use setup_context to explicitly assign the devices.
>>
>
> ok, thanks for reporting.
>
> However, since I have you attention, is there any way for using multiple
>> GPUs on a single job (e.g. GEMM)?
>>
>> Let's say I have two matrices that are too large for one GPU but will
>> fit on both my GPUs. If I create a ONE context with BOTH GPUs and I
>> assign each matrix to one GPU would the normal GEMM workflow function
>> normally?
>>
>
> No, currently this is not implemented. You would have to take care of all
> the data decomposition yourself. The OpenCL semantics of how memory buffers
> are managed don't help either; in the end, one is left with all the data
> juggling.
>
> I don't have a long-term view of whether it is worth to implement such
> multi-GPU support. The (imho) best way to deal with multiple GPUs is via
> MPI, where one has to deal with distributed memory anyway. As such, using
> ViennaCL via PETSc gives you just that for sparse matrices, solvers, etc.
> At the same time, I understand that MPI is not always an option, for
> example in your context. It would be very helpful to know how important
> (vs. just "nice to have") such 'native' GPU support is for you and users of
> gpuR.
>
> Best regards,
> Karli
>
>
>
>
> On Thu, Apr 28, 2016 at 11:12 AM, Karl Rupp <r...@iue.tuwien.ac.at
>> <javascript:_e(%7B%7D,'cvml','r...@iue.tuwien.ac.at');>
>> <mailto:r...@iue.tuwien.ac.at
>> <javascript:_e(%7B%7D,'cvml','r...@iue.tuwien.ac.at');>>> wrote:
>>
>> Hi Charles,
>>
>> > I am trying to set up a list of contexts whereby each context
>> represents
>>
>> one platform and one device. I was thinking the function found
>> here
>> (https://github.com/cdeterman/gpuR/blob/develop/src/context.cpp)
>> starting at line 108 (listContexts) would work.
>>
>> However, as seen in this issue
>> (https://github.com/cdeterman/gpuR/issues/9) this is not the
>> case. For
>> some reason a user with two AMD GPUs, iterating through the
>> platforms
>> and devices with each iterative context id (using
>> 'set_context_platform_index' and
>> 'get_context(id).switch_device(gpu_idx)') results in only the
>> first gpu
>> as recognizable.
>>
>> Perhaps I have made some mistake in how these context functions
>> work? I
>> can confirm access to the second gpu if referring directly to the
>> 'platforms' object but not with respect to a context. Any
>> insight would
>> be appreciated.
>>
>>
>> I see that the referenced issue on GitHub seems to be resolved
>> (sorry for not being able to answer sooner). Is there anything left
>> that I should look into?
>>
>> Best regards,
>> Karli
>>
>>
>>
>
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