Async Ruby VS are-we-fast-yet

The Async gem is the natural successor, It's actively maintained, and allows you write synchronous code is if it wasn't non-blocking, and most libraries don't need any special support for Async (exceptions are gems with C extensions that do I/O and DB libraries with connection pooling that would otherwise be thread-based).

https://github.com/socketry/async uses coroutines and I think in general it’s been a great model with very few downsides in practice.

There's async I/O. Here's a library that leans on Ruby 3's fiber scheduler.

Hey folks, wanted to show this off and get feedback. Still early/experimental but there are quite a few concepts I'm excited about here. This project came about while writing a program in Go and loving its approach to concurrency. Being a long-time Rubyist I immediately started to think about what similar concepts might look like in Ruby.

I set out with two main design constraints:

1. Lightweight: I didn't want routines to be backed by fibers or threads. Having been involved some in the async project (https://github.com/socketry/async), I had some experience using fibers for concurrency but was curious if they could be avoided.

2. Explicitness: Routine behavior must be written to describe exactly how it is to behave. I always felt like concurrent code was hard to fully understand because of the indirection involved. On the spectrum between tedium and magical I wanted to err more on the side of tedium with Goru.

Goru routines are just blocks that are called once for every tick of the reactor. It is up to the developer to implement behavior in terms of a state machine, where on each tick the routine takes some action and then updates the state of the routine for the next tick. This fulfills both design constraints:

1. Because routines are just blocks, they weigh in at about ~345 bytes of memory overhead.

2. Routine behavior is explicit because it is written as a state machine inside the block.

Couple more features worth noting:

* Goru includes channels for buffered reading/writing (similar to channels in Go).

* Goru ships with primitives for non-blocking IO to easily build things like http servers.

Curious your thoughts!

For those who are interested here is the question.

Welcome! Ruby isn't exactly "dying", but the hype/popularity is definitely fading. This is primarily because Ruby is no longer "new", most of Ruby's popularity came from Rails, and now Rails is no longer the "new hotness". However, Ruby still has lots of awesome features and lots of awesome other libraries and frameworks, such as the new fancy irb gem that uses reline, nokogiri, chunky_png, the async gems, Dragon Ruby, SciRuby, Ronin, and the new Hanami web framework.

In Github, there is a Async Gem(https://github.com/socketry/async).

> Sure there's a small overhead to smart pointers

Not so small, and it has the potential to significantly speed down an application when not used wisely. Here are e.g. some measurements where the programmer used C++11 and did everything with smart pointers: https://github.com/smarr/are-we-fast-yet/issues/80#issuecomm.... There was a speed down between factor 2 and 10 compared with the C++98 implementation. Also remember that smart pointers create memory leaks when used with circular references, and there is an additional memory allocation involved with each smart pointer.

> Garbage collection has an overhead too of course

The Boehm GC is surprisingly efficient. See e.g. these measurements: https://github.com/rochus-keller/Oberon/blob/master/testcase.... The same benchmark suite as above is compared with different versions of Mono (using the generational GC) and the C code (using Boehm GC) generated with my Oberon compiler. The latter only is 20% slower than the native C++98 version, and still twice as fast as Mono 5.

See https://github.com/smarr/are-we-fast-yet/blob/master/docs/guidelines.md.

That's a very interesting article, thanks. Interesting to note that Cython is only about twice as fast as Python 3.10 and only about 40% faster than Python 3.11.

The official Python site advertises a speedup of 25% from 3.10 to 3.11; in the article a speedup of 60% was measured. It therefore usually makes sense to measure different algorithms. Unfortunately there is no Python or C++ implementation yet for https://github.com/smarr/are-we-fast-yet.

> Software benchmarks are super subjective.

No, they are not, but they are just a measurement tool, not a source of absolute thruth. When I studied engineering at ETH we learned "Who measures measures rubbish!" ("Wer misst misst Mist!" in German). Every measurement has errors and being aware of these errors and coping with it is part of the engineering profession. The problem with programming language benchmarks is often that the goal is to win by all means; to compare as fairly and objectively as possible instead, there must be a set of suitable rules adhered to by all benchmark implementations. Such a set of rules is e.g. given for the Are-we-fast-yet suite (https://github.com/smarr/are-we-fast-yet).

I am aware of the various published test results where CoreCLR shows fantastic speed-ups compared to Mono, e.g. when calculating MD5 or SHA hash sums.

But my measurements based on the Are-we-fast-yet benchmark suite (see https://github.com/smarr/are-we-fast-yet and https://github.com/rochus-keller/Oberon/tree/master/testcases/Are-we-fast-yet) show a completely different picture. Here the difference between Mono and CoreCLR (both versions 3 and 5) is within +/- 10%, so nothing earth shattering.

Here are my measurement results:

https://github.com/rochus-keller/Oberon/blob/master/testcases/Are-we-fast-yet/Are-we-fast-yet_results_linux.pdf comparing the same benchmark on the same machine run under LuaJIT, Mono, Node.js and Crystal.

https://github.com/rochus-keller/Oberon/blob/master/testcases/Are-we-fast-yet/Are-we-fast-yet_results_windows.pdf comparing Mono, .Net 4 and CoreCLR 3 and 5 on the same machine.

Here are the assemblies of the Are-we-fast-yet benchmark suite used for the measurements, in case you want to reproduce my results: http://software.rochus-keller.ch/Are-we-fast-yet_CLI_2021-08-28.zip.

I was very surprised by the results. Perhaps it has to do with the fact that I measured on x86, or that the benchmark suite used includes somewhat larger (i.e. more representative) applications than just micro benchmarks.

What are your opinions? Do others have similar results?

There is a good reason for this; have a look at e.g. https://github.com/smarr/are-we-fast-yet/blob/master/docs/guidelines.md.

Then apparently the SOM nbody program is taken as the basis of a new Java nbody program.