Generally speaking, what GPUs excel at is applying exactly the same operation to many parallel data streams. Neural networks are like that. Random forests are

Generally speaking, what GPUs excel at is applying exactly the same operation to many parallel data streams. Neural networks are like that. Random forests are the opposite: branching on conditional tests is very expensive. It's been several years since I last wrote raw CUDA and OpenCL, but if memory serves, back then the docs essentially said that every if...else amounted to running both branches on all the data and then deciding what to keep, effectively halving performance. So a decision tree just a few levels deep would slow you down by an order of magnitude.