alt Hacker NewsI think you're conflating GPU 'threads' and 'warps'. GPU 'threads' are SIMD lanes that are all running with the exact same instructions and control flow (only with different filtering/predication), whereas GPU warps are hardware-level threads that run on a single compute unit. There's no issue with adding extra "don't run code" when using warps, unlike GPU threads.
My understanding of warp (https://docs.nvidia.com/cuda/cuda-programming-guide/01-intro...) is that you are essentially paying the cost of taking both the branches.
I understand with newer GPUs, you have clever partitioning / pipelining in such a way block A takes branch A vs block B that takes branch B with sync/barrier essentially relying on some smart 'oracle' to schedule these in a way that still fits in the SIMT model.
It still doesn't feel Turing complete to me. Is there an nvidia doc you can refer me to?