undefined | Better HN

0 pointstextlapse1mo ago0 comments

warp is expensive - essentially it's running a 'don't run code' to maintain SIMT.

GPUs are still not practically-Turing-complete in the sense that there are strict restrictions on loops/goto/IO/waiting (there are a bunch of band-aids to make it pretend it's not a functional programming model).

So I am not sure retrofitting a Ferrari to cosplay an Amazon delivery van is useful other than for tech showcase?

Good tech showcase though :)

0 comments

zozbot2341mo ago

I 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.

textlapseOP1mo ago

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?

rowanG0771mo ago

That applies inside a single warp, notice the wording:

> In SIMT, all threads in the warp are executing the same kernel code, but each thread may follow different branches through the code. That is, though all threads of the program execute the same code, threads do not need to follow the same execution path.

This doesn't say anything about dependencies of multiple warps.

1 more reply

j / k navigate · click thread line to collapse