> Yes. However, it is worth making the distinction between > embarrassingly parallel problems and SIMD problems. Not all > embarrassingly parallel problems are SIMD-capable. GPUs do SIMD, not > generally embarrassing problems.
GPUs exploit both dimensions of parallelism, both simd (aka vectorization) and parallelization (aka multicore). And yeah, 99.9% of the time branching on GPU should be the least/last of your worries if your problem is data-parallel. There are much worse things than branchings. As for SIMD special functions, branching can certainly be eliminated. I have written/come across some special functions myself, and I do not know any case which is difficult to do efficiently on a gpu. Certainly, I know less than some folks around here. May be you can contribute a counter example to this discussion. Regards, -- Rohit Garg http://rpg-314.blogspot.com/ Senior Undergraduate Department of Physics Indian Institute of Technology Bombay _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
