Hi! Engineer who writes many high-performance GPU things. We don't get nearly as much attention since part of the job is to write language bindings so that not

Hi! Engineer who writes many high-performance GPU things. We don't get nearly as much attention since part of the job is to write language bindings so that not everyone on the team has to look at CUDA kernels directly. There's a pretty big market for it, but it's still hard/expensive to both write performant code and train people to be able to do so. A lot of it reads roughly similar to CPU math library implementations (programming in "assembler" via compiler intrinsics). I'd say any way this can be made easier without losing a ton of performance will just make more problems more affordable to solve, since the time of senior performance/optimization engineers is so limited. I'd argue that this project will be successful if you can write at least a few primitives of Thrust-like library in it: it'll give reasonably-sized problems to tackle, a production implementation to compare against for both testing and performance benchmarking, and demonstrate that the project has the basic functionality (optimized primitives like parallel sums/reductions are building blocks for a lot of useful things).