Chung Hwan Kim writes: > I and two other students have formed up a team for a project called > "Accelerating Dynamic Binary Translation with the GPUs". As the name of > the project suggests our main idea is to parallelize Dynamic Binary > Translation (DBT) process and speed it up with GPUs using the NVIDIA > CUDA library.
AFAIK, DBT is a fairly control flow intensive code, so you'll probably run into lots of branch divergence problems, so that performance will suffer a lot, even if you use instruction template tables (like in qemu's PPC target). Nonetheless, I think new fermi models have less problems with that, but it's still an architecture thought for control-flow-homogeneous parallel code. In any case, I'm not sure what is the real cost of translation related to execution, it all depends on the kind of applications you're running; but the computation on the GPU better have a huge speedup compared to the current approach, or otherwise the data transfers to/from the GPU will dominate the cost, specially if they're small transfers. Lluis -- "And it's much the same thing with knowledge, for whenever you learn something new, the whole world becomes that much richer." -- The Princess of Pure Reason, as told by Norton Juster in The Phantom Tollbooth
