Hi Karli, Thanks for that great e-mail.
I'd be glad if you could send me the randomized SVD paper. I'll take a look at this weekend. With the 'standard' SVD yes, I'll take a look at the OpenCL part. For the generalized SVD is there an implementation for N=2 in ViennaCL? I did already fork both viennacl-dev and PyViennaCl-dev. I will re-fork it on Monday and start from there. Also, I'm comfortable with git, so pull-requests through that should not be a problem. Regards, Aanchan On Fri, Oct 31, 2014 at 5:07 PM, Karl Rupp <r...@iue.tuwien.ac.at> wrote: > Hi Aanchan, > > > Just to give you an intro: > >> I work in speech recognition, with C/C++ toolkits like HTK and Kaldi. We >> have added some of our own libraries to these codebases maintained >> internally at McGill. I am also familiar with Python, Perl, Bash and >> CUDA. I use a Python wrapper called Gnumpy (wrapped around a CUDA matrix >> library called CUDAMat) to train neural networks on GPU boards >> routinely. I use Linear Algebra in my work routinely. >> > > Ah, I see, thanks for the informations, all good stuff :-) > > > I had a very nice conversation with Philippe on IRC who mentioned that >> there are some issues with GEMV in PyViennaCL. Since I am familiar with >> Gnumpy I could look into that. >> > > Hmm, that might be pretty hard to debug for a start, since it connects the > most high-level-layer with the most low-level functionality. Also it seems > like Philippe found a fix for this just shortly after your chat. > > > > Philippe also mentioned that the current > >> implementation of the SVD is a bit slow. I could profile that, and start >> from there. If necessary start with a newer implementation and see if I >> get any better. >> > > This is actually a very good starting point. We get a few user requests on > a faster SVD, and I think there is quite a bit of optimization potential > for the existing OpenCL implementation. A bachelor student will also port > this to CUDA and OpenMP in the next months, so if you want to look at the > OpenCL part now, it would certainly help. > > > I was also recently working with the Generalized SVD. As >> of the 70's the GSVD was developed for N=2 matrices. Recently a group at >> Utah has developed it for three or more matrices >> :http://www.alterlab.org/HO_GSVD/. N=2 matrices case is based off the QR >> decomposition, for N>2 a QR decomposition based method is not that >> straightforward. But given their current (non-QR) solution I could try >> and implement that. >> > > This also sounds like a great project to start with. Although I only > looked at the paper briefly, the algorithm looks sufficiently compact. More > importantly, you also seem to have direct use for the implementation, which > is a good thing maintenance-wise :-) > > > Do these sound like good things to start with? >> > > Generalized SVD and 'standard' SVD are certainly good things to look at if > you're familiar with the linear algebra behind it. I can also send you > slides about a randomized SVD algorithm, which I know is fairly easy to > implement for all three compute backends with the existing functionality in > ViennaCL. > > Are you fine with issuing pull requests through GitHub for a start? If you > want to start off with a stable state, I suggest you start (i.e. fork) > early next week when the 1.6.0 release is out. :-) > > Best regards, > Karli > >
------------------------------------------------------------------------------
_______________________________________________ ViennaCL-devel mailing list ViennaCL-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/viennacl-devel
