anydsl VS toast

I work on the AnyDSL research project, we have our own IR and optimizing compiler (Thorin), our framework supports partial evaluation and efficient codegen for the host as well as multiple compute offload targets (CUDA, OpenCL C, the NVVM and AMDGPU targets in LLVM), and I've been pursuing targeting SPIR-V as well.

There is also this:

https://anydsl.github.io/

They have some framework that achieves high level compute!

While waiting for std::simd to become a thing in stable and looking for alternatives I stumbled upon this: https://anydsl.github.io/

I had a look at Impala previously. It describes itself as dialect of Rust, but somehow I'm not exactly happy with Rust syntax, but found Impala's much more cuter, up to actually bother to report a doc glitch to make it even more cuter to passers-by ;-). I understand that it's pretty hard to bootstrap a language nowadays, so understand that "any DSL" marketing niche, though I guess it wouldn't look bad if it was promoted as a general-purpose language either. I also understand that it's open-source, but I'm not rushing to look there, by various reasons, like complexity, copyright and possibility to pick up bad ideas ;-). Preferring to do "black box" studying by papers for now, so thanks again for the links.

I have a real life example in this commit: https://github.com/hpc4cmb/toast/pull/380/commits/a38d1d6dbc...

Replacing 2 lines of python code (with tens of glue code in Numba) with hundreds lines of C++ with glue code.

If you just want to do a matrix multiplication with CUDA (and not inside some CUDA code), you should use cuBLAS rather than CUTLASS (here is some wrapper code I wrote and the corresponding helper functions if your difficulty is using the library rather than linking it / building), it is a fairly straightforward BLAS replacement (it can be a pain to install but that is life with C++/nvidia).