cuda-api-wrappers

1. This implements the clunky C-ish API; there's also the Modern-C++ API wrappers, with automatic error checking, RAII resource control etc.; see: https://github.com/eyalroz/cuda-api-wrappers (due disclosure: I'm the author) 2. Implementing the _runtime_ API is not the right choice; it's important to implement the _driver_ API, otherwise you can't isolate contexts, dynamically add newly-compiled JIT kernels via modules etc. 3. This is less than 3000 lines of code. Wrapping all of the core CUDA APIs (driver, runtime, NVTX, JIT compilation of CUDA-C++ and of PTX) took me > 14,000 LoC.

> since the underlying API's are still C/C++, If the use of GPUs is via CUDA, there are my wrappers [1] which are RAII/CADRe, and therefore less unsafe. And on the Rust side - don't you need a bunch of unsafe code in the library enabling GPU support? [1] : https://github.com/eyalroz/cuda-api-wrappers/

Given your criteria, you might want to consider (modern) C++. * Fast - in many cases faster than Rust, although the difference is inconsequential relative to Python-to-Rust improvement I guess. * _Really_ utilize CUDA, OpenCL, Vulcan etc. Specifically, Rust GPU is limited in its supported features, see: https://github.com/Rust-GPU/Rust-CUDA/blob/master/guide/src/... ... * Host-side use of CUDA is at least as nice, and probably nicer, than what you'll get with Rust. That is, provided you use my own Modern C++ wrappers for the CUDA APIs: https://github.com/eyalroz/cuda-api-wrappers/ :-) ... sorry for the shameless self-plug. * ... which brings me to another point: Richer offering of libraries for various needs than Rust, for you to possibly utilize. * Easier to share than Rust. A target system is less likely to have an appropriate version