roast VS ocaml-multicore

Add more IO::Path::parent tests #801: merged 2022-02-19

23. Raku - $79,448

Pakku is a package manager for the Raku Programming Language. Latest releases of Pakku are part of Pakku Celastrina version family. Celastrina name means elegant and beutiful, So I will take the opportunity to introduce how elegant IMO Pakku handles Raku distributions.

At the last European Perl Conference I proposed to change the name of "Perl 6". After a lot of discussion, it was decided that it was going to be called the Raku Programming Language.

Raku note: This language has no length method on strings, because in Unicode world it is super confusing. Instead there are separate methods to ask precisely about amount of characters, amount of code points and amount of bytes.

Elizabeth Mattijsen reports on all recent developments around Rakudo, an implementation of the Raku Programming Language.

The raku.org website.

This became one of the first things that needed to be done, to be able to say the new implementation would be matching the old. During the development of these tests, it became clear there were some inconsistencies in the existing implementation, and worse: outright bugs.

1. Domains are the unit of parallelism. A domain is essentially an OS thread with a bunch of extra runtime book-keeping data. You can use Domain.spawn (https://github.com/ocaml-multicore/ocaml-multicore/blob/5.00...) to spawn off a new domain which will run the supplied function and terminate when it finishes. This is heavyweight though, domains are expected to be long-running.

2. Domainslib is the library developed alongside multicore to aid users in exploiting parallelism. It supports nested parallelism and is pretty highly optimised (https://github.com/ocaml-multicore/domainslib/pull/29 for some graphs/numbers). The domainslib repo has some good examples: https://github.com/ocaml-multicore/domainslib/tree/master/te...

3. We've not tested against other forms of parallelism. There isn't anything stopping you exploiting SIMD in addition to parallelism from domains.

4. No, we've not compared performance by OS.

5. No plans for the multicore team to look at accelerator integration at the moment.

For Algebraic Effects and Multicore OCaml specifically, I have this intro saved and they've been publishing regular updates here's October's. They have a paper linked from their repo's README but I don't remember the contents offhand.

Until recently Multicore OCaml was focused on deep handlers. The people working on the formalization of effects (either for program proofs or typed effects) were quite keen to have shallow handler integrated however. Thus, the effect module of the OCaml 5 preview contains both (see https://github.com/ocaml-multicore/ocaml-multicore/blob/5.00...) since September. I fear that non-academic literature has not followed this change (on the academic side, see https://dl.acm.org/doi/10.1145/3434314 for a program proofs point of view).

Yes, it's announcing that the next but one version, 5.0, will support multicore and effect handlers.

For what it's worth you can actually start using Multicore OCaml today, there are installation instructions on the wiki: https://github.com/ocaml-multicore/ocaml-multicore

ocaml-multicore/ocaml-multicore

Could you explain (in simple terms if possible) how the Multicore OCaml achieves a memory model which is much simpler on more efficient than in Java or C (mentioned at https://github.com/ocaml-multicore/ocaml-multicore/wiki)?

Didn't see any mentions of critical sections (mutexes) with C++ examples in the documentation ("Bounding Data Races in Space and Time"). I'm not sure I understand the comparisons the writers are presenting.

There are getting started instructions up on https://github.com/ocaml-multicore/ocaml-multicore