Interesting. Parallelism and profiling are both strong selling points.

I’ve done pretty low-level C++ and Fortran in the past, but these days I’m honestly mostly used Python with either NumPy or CuPy for calculations, so not very low-level at all. My code is pretty performance-sensitive though, so unless the main operations can be framed neatly in terms of NumPy or CuPy primitives, one had to drop down to low level.

Do you know how the Go library support is for typical scientific computing stuff, e.g. matrix diagonalization, sparse matrices, or handing off such calculations to CUDA (or other GPU frameworks)? Is there a strong numerics community in Go these days, or would you have to implement most of what you need yourself?