Hi Karli,
I had a question about the inplace_qr method. The method is an overloaded
function, if the matrix is a dense uBlas matrix Boost matrix type then
inplace_qr_ublas is called. If the matrix type is a ViennaCL type matrix,
the inplace_qr_hybrid is called.
I understand that the QR factorization is implemented using Householder
reflections(HHR) on the columns of an incoming matrix A. If I understand
the behaviour of inpace_qr_hybrid, if the matrix is of a small size then
HHRs are done on just the CPU using uBlas from Boost. Other wise if it is
large then, the HHRs for the first few columns are done on the CPU and the
last few columns are done on the GPU, is that right? If I have compiled
against a CUDA back end, I am assuming the GPU operations are going to be
carried out using CUDA. Typically what size matrices cause the hybrid
(CPU+GPU) behaviour to kick in?
Thanks,
Aanchan
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