As we know, work items running on GPUs could diverge when there are conditional branches. One of those mentions exist in Apple's OpenCL Programming Guide for Mac.
As such, some portions of an algorithm may run "single-threaded", having only 1 work item running. And when it's especially serial and long-running, some applications take those work back to CPU.
However, this question concerns only GPU and assume those portions are short-lived. Do these "single-threaded" portions also diverge (as in execute both true and false code paths) when they have conditional branches? Or will the compute units (or processing elements, whichever your terminology prefers) skip those false branches?
Update
In reply to comment, I'd remove the OpenCL tag and leave the Vulkan tag there.
I included OpenCL as I wanted to know if there's any difference at all between clEnqueueTask and clEnqueueNDRangeKernel with dim=1:x=1. The document says they're equivalent but I was skeptical.
I believe Vulkan removed the special function to enqueue a single-threaded task for good reasons, and if I'm wrong, please correct me.
Q : Or will the compute units skip those false branches?
The ecosystem of CPU / GPU code-execution is rather complex.
The layer of hardware is where the code-paths (translated into "machine"-code) operate. On this laye, the SIMD-Computing-Units cannot and will not skip anything they are ordered to SIMD-process by the hardware-scheduler (next layer).
The layer of hardware-specific scheduler (GPUs have typically right two-modes: a WARP-mode scheduling for coherent, non-diverging code-paths efficiently scheduled in SIMD-blocks and greedy-mode scheduling). From this layer, the SIMD-Computing-Units are loaded to work on SIMD-operated blocks-of-work, so any first divergence detected on the lower layer (above) breaks the execution, flags the SIMD-hardware scheduler about blocks, deferred to be executed later and all known SIMD-specific block-device-optimised scheduling is well-known to start to grow less-efficient and less-efficient, due to each such run-time divergence.
The layer of { OpenCL | Vulkan API }-mediated device-specific programming decides a lot about the ease or comfort of human-side programming of the wide range of the target-devices, all without knowing about its respective internal constraints, about (compiler decided) preferred "machine"-code computing problem re-formulation and device-specific tricks and scheduling. A bit oversimplified battlefield picture has made for years human-users just stay "in front" of the mediated asynchronous work-units ( kernel's ) HOST-to-DEVICE scheduling queues and wait until we receive back the DEVICE-to-HOST delivered results back, doing some prior-H2D/posterior-D2H memory transfers, if allowed and needed.
The HOST-side DEVICE-kernel-code "scheduling" directives are rather imperative and help the mediated-device-specific programming reflect user-side preferences, yet leave user blind from seeing all internal decisions ( assembly-level reviews are indeed only for hard-core, DEVICE-specific, GPU-engineering Aces and hard to modify, if willing to )
All that said, "adaptive" run-time values' based decisions to move a particular "work-unit" back-to-the-HOST-CPU, rather than finalising it all in DEVICE-GPU, are not, to the best of my knowledge, taking place on the bottom of this complex computing ecosystem hierarchy ( afaik, it would be exhaustively expensive to try to do so ).