CODEWITHSUNDEEP
cudanonlinear-optimization
user73898
user73898

Reputation: 143

are there existing libraries for many optimization jobs in parallel on GPU

I'm looking to perform many (thousands) of small optimization jobs on my nVidia Geforce.

With small jobs I mean 3-6 dimensions and around 1000 data points input each. Basically it's for curve fitting purposes, so the objective function to minimize is a sum of squares of a continuous (non-trivial) analytical function, of which I can compute the first derivative analytically. Each dimension is constrained between lower and upper boundary.

The only thing these jobs have in common is the original data series out of which they take different 1000 data points.

I suspect this will be much faster on my GPU than now, running them one by one my CPU, so I could use it for realtime monitoring.

However, the GPU libraries I've seen only focus on calculating a single function evaluation (faster) on the GPU.

There was a thread on my specific question on the nvidia CUDA forum, with more users looking for this, but the forums have been down for a while. It mentioned porting an existing C library (eg. levmar) to the CUDA language, but this got lost...

Do you know of an existing library to run many optimizations in parallel on a gpu?

Thanks!

Upvotes: 1

Views: 503

Answers (1)

arrayfire
arrayfire

Reputation: 1754

The GFOR loop is meant to tile together many small problems like this. Each body of the loop is tiled together with the other loop bodies. Many people have used it for optimization problems like you describe. It is available for C/C++, Fortran, or Python as well as MATLAB code.

My disclaimer is that I work on GFOR. But I'm not aware of any other production-level GPU library that does similar optimization problems. You might be able to find some academic projects if you search around.

Upvotes: 2

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