Hi Karli, 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.
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. 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. 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. Do these sound like good things to start with? Regards, Aanchan
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