Computational Science Stack Exchange is a question and answer site for scientists using computers to solve scientific problems. It only takes a minute to sign up.
Sign up to join this community
Are consumer grade GPUs (like the NVidia Geforce series) used for solving sparse linear equations systems in a professional setting? For example, by engineers performing finite element analysis?
After all, at least for iterative methods, memory bandwidth is the bottleneck, and a Geforce 780 Ti provides a lot more of this than a Tesla C2075, while also costing a lot less.
In a "professional" setting usually you run commercial software only on "certified" hardware, i.e. hardware on which the vendor has tested his software and is willing to provide user support. "Certified and supported" GPU's lists are usually very short...: there is really no technical reason, just commercial ones.
In my experience, high end FEA solution vendors charge such horrible high sums for licenses that clients do not care for the cost of the GPU cards and just buy the supported ones.
This said, if you are going to use "consumer grade" GPUs, you should test if, under sustained load, accuracy of the solution and performance is preserved.
Sparse matrix multiplication (that is, sparse matrix inversion/solution) is a huge area of interest/research in the GPU community. Currently things are looking pretty good, but it's not anywhere near where it is in some other fields. (This is really hard.)
At the nuts-and-bolts level of an analyst using a practical FEA code, it's not there. All the big players are waiting to add/push support until the situation is a bit clearer.
