These sort of books are only "dated" when it comes to specific languages/frameworks/libraries. The methods/techniques are evergreen and often conceptually better explained in these older books.
For recent up to date works on HPC, the free multi-volume The Art of High Performance Computing by Victor Eijkhout can't be beat - https://news.ycombinator.com/item?id=38815334
Understanding the fundamentals of parallel programming comes first, IMO.
Are there any good resources you’d recommend for that?
Foundations of Multithreaded, Parallel, and Distributed Programming by Gregory Andrews - An old classic but still very good explanations of concurrent algorithmic concepts.
Parallel Programming: Concepts and Practice by Bertil Schmidt et.al. - A relatively recent book with comprehensive coverage.
1. Learning CUDA - the framework, libraries and high-layer wrappers. This is something that changes with times and trends.
2. Learning high-performance computing approaches. While a GPU and the Nvlink interfaces are Nvidia specific, working in a massively-parallel distributed computing environment is a general branch of knowledge that is translatable across HPC architectures.
3. Application specifics. If your thing is Transformers, you may just as well start from Torch, Tensorflow, etc. and rely on the current high-level abstractions, to inspire your learning down to the fundamentals.
I’m no longer active in any of the above, so I can’t be more specific, but if you want to master CUDA, I would say learning how massive-parallel programming works, is the foundation that may translate into transferable skills.