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 <[email protected]>:

> 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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