Well, one of the authors is from NVIDIA, and the paper already mentions CUDA, and it's only to argue that CUDA is a good point of reference but doesn't do what the authors need it do (shader programming). SYCL doesn't add anything to that conversation other than being yet another example.
Hi, lead author here. As oddity mentions, this paper references CUDA and C++ AMP (which both support C++ lambdas as well) as examples of unified programming models for GPU compute as contrasts to the non-unified programming models available for real-time graphics. SYCL is another interesting example in the GPU compute space, but as far as I am aware, it (like the others) does not include any new features that would provide adequate support for the crucial shader specialization optimization.
Adding C++ lambda support to shader programming would certainly be beneficial, but I don't think it, alone, would provide the modularity, composition, and GPU code specialization features necessary for an effective shader programming model.
That said, I am not an expert in SYCL, so if you have some ideas about how SYCL could meet our design goals, I would appreciate further feedback!
I think you should have cited SYCL anyway. You way cited things less relevant, like rust-gpu. While SYCL may not have all the features, it's a precursor by enabling programming GPU and CPU from a single source.
This does unfortunately feel like the case. We've gone all-in on SYCL, mainly because of a pre-existing Intel relationship, but it never really seems to have gotten mindshare and e.g. never seems to have been dicussed here https://hn.algolia.com/?query=sycl
