[SciPy-User] Need a fast cuda/opencl sundials nvector implementation

[Sebastian Walter]

1) Why exactly are you asking this question on the scipy mailing list
and not on the sundials mailing list?
2) I would have thought that the solution of the nonlinear system (I -
gamma*J)Z = R takes most of the computation time.
But apparently I am missing something and I'm curious what it is.


Sebastian



On Mon, Jan 10, 2011 at 10:13 AM, Peter John Garrone
<pgarrone at optusnet.com.au> wrote:
> Hi,
>  Thanks for replying.
>  The sundials distribution, for cvode, provides a "psolve" preconditioning callback. Parameters to this callback include gamma and R, and the user is expected to set a vector called Z to the solution of (I - gamma*J)Z = R, where J is the Jacobian. I am doing this with standard scipy gmres, and it is very fast, and I have no problems there. I have not measured it, merely observed that it solves a ~100000 state system in a fraction of a second.
>
>  The problem seems to me to be that if I add up all the cpu time occupied with the four sundial-cvode callbacks, that is psetup, psolve, jtimes, and the function itself, it is only a small fraction, a few percent, of the total cpu time. So optimising these callbacks is pointless. This leaves the vector operations, which the user can replace. I assume that they are implemented fairly optimally, but not exploiting a GPU.
>
>  So what I am looking for is an implementation of the sundials NVector_serial construct using CUDA or OpenCL.
>
>  If no such implementation is available, I might have a go at attempting to implement it, after verifying my theory that it is the vector ops taking all the cpu. However if something else is freely available, I would like to try it.
>
>  Peter
>
>
> On Sun, Jan 09, 2011 at 12:21:17PM +0100, Sebastian Walter wrote:
>> Hello Peter,
>> so you are saying that you want to replace the built-in functionality
>> to solve large sparse linear systems by your own code which exploits
>> the structure of your Jacobian?
>>
>> Sebastian
>>
>>
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