No, I don't have any particular application in mind right now, but in general I always found that mixed effects models take a long time to run on large data sets.
On Saturday, August 27, 2016 at 11:13:20 AM UTC-4, Douglas Bates wrote: > > On Friday, August 26, 2016 at 6:08:13 PM UTC-5, Min-Woong Sohn wrote: >> >> Does anybody know of any plan to support ArrayFire in GLM or MixedModels >> any time soon? >> > > Do you have a particular application in mind or is this a general > question? For MixedModels I would say that, depending upon the > configuration of the random-effects terms in a model there could be a great > advantage or almost no advantage in using a GPU, so details are important. > > We're always looking for challenging GLM or mixed-effects problems that > can be used to tune up these packages. If you have cases that seem to be > taking a long time and would be suitable for parallel or GPU computing we > would love to hear about them. > > >> On Friday, June 10, 2016 at 1:08:42 AM UTC-4, [email protected] >> wrote: >>> >>> Hello, >>> >>> We are pleased to announce ArrayFire.jl, a library for GPU and >>> heterogeneous computing in Julia: ( >>> https://github.com/JuliaComputing/ArrayFire.jl). We look forward to >>> your feedback and your contributions as well! >>> >>> For more information, check out Julia Computing's latest blog post: >>> http://juliacomputing.com/blog/2016/06/09/julia-gpu.html >>> >>> Thanks, >>> Ranjan >>> Julia Computing, Inc. >>> >>
