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Maybe a little OT, but why aren't the OR-tools in general more focused (or more "marketed") towards GPU/TPU clouds since they seem like the exact problems which
by jonbaer 7y ago
Maybe a little OT, but why aren't the OR-tools in general more focused (or more "marketed") towards GPU/TPU clouds since they seem like the exact problems which they are made for? More focus seems to be towards TF/PT/NN and not the real problems like what OR-tools and solvers in general push for.
- mochomocha 7y agoTypical algorithms solving combinatorial problems are usually heavily branching, so not so much vectorizable. For example for a branch-and-cut MIP solver, you could run the LP-relaxed problems on GPU, but most of the logic lives in branching and cutting heuristics that are much harder to parallelize.
- 7thaccount 7y agoA lot of OR stuff can be parallelized, but it isn't a panacea. Those techniques don't necessarily get you the best answer sometimes. My company does some massive optimization problems and although we have lots of cores for all the simulations, any one is pretty much single core + a lot of RAM. It doesn't work well on GPU like some heuristic optimization problems can. Edit: Someone smarter than me says it better below :)
- cerved 7y agoFor starters, CP and SAT are based on integers and boolean variables. Secondly, local search and systematic search are just no way near parallelizable to the degree required to see gains running GPU/TPU.