You also get very easy parallelism in Goroutines, excellent ecosystem, and one of the best performance profilers in any programming language, pprof.
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?
https://github.com/gonum/gonum
For sparse, I think the best is still Intel's sparse libraries for MKL on CPU, it's just not a workload that runs well on GPU compared to dense SGEMM. You can call it via CGo.
For GPU offloading, there should be a decent amount of CUDA bindings available for Go right now, but I'm not as familiar in that front since I'm building my own GPU compute runtime in Vulkan, but it's still very much an experimental work in progress right now.
Yeah, my understanding is also that working with sparse matrices inherently requires heavy branching, which translates badly to GPU. I usually do everything I can via dense matrices via CUDA, but switch to sparse matrices on CPU if I run out of VRAM…