You say you're using Linux as the underlying OS, but you must have looked at bare-metal embedded usage as well.

How feasible would you say it is, today, to use OCaml with MirageOS (or something similar) on a bare-metal target, say a small softcore RISCV32 with 256kB of RAM?

1) You have been ordered to directly interface with some external library that has a complicated C ABI, and you can't isolate it in a separate process and do IPC, and the FFI would be too clunky or slow.

2) You really need to manipulate lots of bits or bytes or floats very fast, or there are lots of them, and speed or memory footprint is becoming an issue. You need to multicore and/or SIMD, you need efficient abstractions, and you do that kind of thing all over the place, not just in a few functions that you separately implement in C or whatever.

As a bonus here's a good reason for NOT leaving OCaml: You can quickly bootstrap the compiler and run it on small embedded machines. I remember using OCaml on a Cyrix 686 with 64 megs I think, it was perfectly fine. Today, the lights dim a bit when I start cargo build.

The size variants for floats and integers is definitely appreciated.

For the "read-only BigArrays": At the time I didn't know any Rust, but today that would simply be passing a mutable or immutable reference. Similar to the Fortran in/out designators in some way. I think that's pretty important when you have some complicated numerical code, sometimes with in-place modification.

Since there is a "zero_alloc checker", maybe a similar kind of annotation exists or could be added? Something like

  let foo (x : [@readonly]) = ... 
    x.{0} <- 1.23

  ^ Attempt to write to read-only array

OCaml was very much part of the GHG measurements. On the satellite it was controlling the cameras, acquiring the images, losslessly compressing them, encrypting them and transferring them to the platform controller using a clunky but mandated CSP-based file tranfer protocol. On the ground, OCaml was running almost the entire data processing chain, including spectroscopy, image corrections, retrievals and post-retrieval ad hoc bias corrections, as well as simulations.

I simply used an mmap()'d Bigarrays to do parallel processing (back then OCaml wasn't multi-core.)

At a later stage I replaced a few bits of code (e.g. some sparse matrix routines) with Fortran. The only processing-related part that wasn't OCaml (besides the shells scripts to glue the things together) was the image alignment algorithm which was written by someone else in C++. I even had a job scheduling system written in OCaml.

I gave a talk about the payload software at the Paris OCaml users group.

The reason for selecting that archicture was that I didn't expect to write the whole payload software by myself, and I assumed that when some other developers join in they would, obviously, not want to use a weird language like OCaml, and so they could write their portion in C/C++/whatever and the system could still work. Of course that didn't happen.

I'd be surprised if the company still uses OCaml, as the standad tendency is to revert to "industry-standard" languages to get industry-standard problems. The whole processing and simulation toolchain was also written in OCaml.

Today there is little reason not to use Rust and it can cover both the processing side and the payload software. But people still insist on using C/C++. I'm OK with that as long as I can invoice them.

EDIT: Found my slides https://lambda-diode.com/static/data/GHGSat_OCaml.pdf