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Nobody worked on that.
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From memory we call numpy or scipy on the CPU. Can you be more precise? From memory NumPy or scipy have parameter or different implementation with speed vs precision trade off.
Fred
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Hi,The GPU cholesky decomposition relies on cuSOLVER or Magma. I believe nvidia knows their hardware well and cuSOLVER should provide the best efficient result.Although cholesky decomposition is very numerical stable, when I write the test case, I find that I will get trouble for relatively small matrix if I use single-precision.Are you using single-precision on a big matrix?If not, try to compute the condition number of the matrix to see if it is too big.If it is not too big, then it may be a bug. I also need to use the cholesky operator, Please send me the matrix and I am willing to fix it.Best,
2020年2月6日(木) 0:34 Paul Baggenstoss <p.m.ba...@ieee.org>:
HI Simon, I was wondering if you got anywhere with the faster Cholesky for Theano. I also use it a lot and have found it to be unstable on the GPU.--
Paul
On Saturday, March 7, 2015 at 11:45:36 AM UTC+1, Simon Ebner wrote:Hi all,I want to do computations where I rely heavily on the Cholesky decomposition. Writing a small benchmark for tensor.slinalg.Cholesky, I noticed that the implementation is not as fast as I hoped. As far as I can tell it is not optimized for GPUs yet but relies on the scipy implementation?Doing a bit of a google seach I found several cuda implementations for fast Cholesky decompositions on the GPU. Before I try to include that code into my theano environment, could you let me know whether you decided not to implement fast Cholesky decomposition on the GPU on purpose? Furthermore, since I'm fairly new to theano I'm not completely confident how to incorporate cuda code best into my existing theano code. Is the sensible to create a custom OP with optimized C-Code?Best,Simon
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Paul Baggenstoss <p.m.ba...@ieee.org> 於 2020年2月7日 週五 下午9:49寫道:
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Hi Paul,I am not quite sure what you are going to do.If you want a batch version of Cholesky and TriangularSolve, then you will end up with a "nb" copy of gradients.What are you going to do with that "nb" copy of gradients?AFAIK, theano.grad only accept scalar input. If you need a Jacobian, you can only implement it by theano.scan and I know theano.scan is inefficient.You may be interested in this thread: https://groups.google.com/d/msg/theano-users/Rg8ZIru-pgo/DgvwY57RBwAJBest,wonghang
Paul Baggenstoss <p.m.ba...@ieee.org> 於 2020年2月10日 週一 下午8:46寫道:
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