After poking around with different headers it appears I need one of the
object type files such as "viennacl/matrix.hpp" in order to declare
viennacl::context objects. Shouldn't this be possible from just
"viennacl/ocl/backend.hpp"? Just curious on the design at this point.
Regards,
Charles
On Tue, May 10, 2016 at 8:40 AM, Charles Determan <cdeterma...@gmail.com>
wrote:
> Excellent, thank you for explaining that. One followup thing, I am trying
> a trivial function to test this:
>
> int findContext(int x){
> viennacl::context ctx(viennacl::ocl::get_context(static_cast<long>(x)))
> return x
> }
>
> but I keep getting a compiler error:
>
> >error: variable ‘viennacl::context ctx’ has initializer but incomplete
> type
>
> Did I miss something?
>
> Thanks,
> Charles
>
>
> On Mon, May 9, 2016 at 4:09 PM, Karl Rupp <r...@iue.tuwien.ac.at> wrote:
>
>> Hi Charles,
>>
>> setContext() is not thread-safe, so if mclapply() is executing in
>> parallel, there will be a race. MPI works across processes, so globals are
>> not shared (and hence setContext() has no problems).
>>
>> If you want to run multithreaded, you should manage the contexts
>> explicitly. That is, pass a viennacl::context object to the respective
>> constructors of viennacl::vector, viennacl::matrix, etc. You can find
>> examples here:
>>
>> https://github.com/viennacl/viennacl-dev/blob/master/examples/tutorial/multithreaded.cpp
>>
>> https://github.com/viennacl/viennacl-dev/blob/master/examples/tutorial/multithreaded_cg.cpp
>>
>> Best regards,
>> Karli
>>
>>
>>
>>
>> On 05/09/2016 10:32 PM, Charles Determan wrote:
>>
>>> I am trying to use multiple GPU's in parallel using ViennaCL through my
>>> gpuR package. In essence the context is selected at the R level (one
>>> context per device already initialized). Then a matrix is created on
>>> that device. The code looks like this:
>>>
>>> create_matrix <- function(id){
>>> # sets context
>>> setContext(id)
>>> # create matrix
>>> mat <- vclMatrix(rnorm(16), nrow=4, ncol=4)
>>> return(mat)
>>> }
>>>
>>> # a fork over context 1 and 2
>>> out <- mclapply(1:2, function(x) create_matrix(x))
>>>
>>> Yet, strangely this just hangs. It doesn't return anything. Perhaps I
>>> am missing something in how OpenCL contexts are handed in parallel? I
>>> ask as I recall that PETSC had some multi GPU functionality with MPI.
>>> The above makes sense to me without MPI but again I may be missing
>>> something.
>>>
>>> The actual copying happens in this file at line 36
>>> (https://github.com/cdeterman/gpuR/blob/develop/src/dynVCLMat.cpp) just
>>> after "starting to copy". It seems almost like it a problem with the
>>> class objects (defined in
>>>
>>> https://github.com/cdeterman/gpuR/blob/develop/inst/include/gpuR/dynVCLMat.hpp
>>> ).
>>>
>>> Any insight would be appreciated.
>>>
>>> Regards,
>>> Charles
>>>
>>>
>>>
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>>
>
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