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I have an application in which an (expensive) linear operator is implemented on the GPU. Methods such as GMRES from IterativeSolvers attempt to apply the operator to a RHS which is a view of another larger array. Since there are no copy! methods for views, a naive implementation fails.
In the case of GMRES (and one would imagine most performance critical applications), the views will be an appropriate sized column of a matrix, thus the memory is linear and the copy operation could presumably be performed without too much pain.
I am not that familiar with the SubArray architecture - would it be to implement a copy! method which accepts a view, so long as the indices are linear and the dimensions correct?
The text was updated successfully, but these errors were encountered:
Having looked a little at the documentation, perhaps it would be possible simply to add a new method with signature
function Base.$copyfun(dst::CuArray{T}, src::SubArray{T,N,A,I,true}) where T
which checks for unit stride, and then continues in the same vein as the standard copy methods. It appears in that in this case passing a pointer and length to the CUDA memcpy function should suffice.
I have an application in which an (expensive) linear operator is implemented on the GPU. Methods such as
GMRES
fromIterativeSolvers
attempt to apply the operator to a RHS which is a view of another larger array. Since there are nocopy!
methods for views, a naive implementation fails.In the case of GMRES (and one would imagine most performance critical applications), the views will be an appropriate sized column of a matrix, thus the memory is linear and the copy operation could presumably be performed without too much pain.
I am not that familiar with the
SubArray
architecture - would it be to implement acopy!
method which accepts a view, so long as the indices are linear and the dimensions correct?The text was updated successfully, but these errors were encountered: