mps VS are-we-fast-yet

I have a library which has an extremely slow free, around 2m for large files, because of unnaturally scattered allocation patterns, but this old conservative GC didn't help at all. It was about 40% slower with libgc. mimalloc was a bit better. Best would be a properly fast GC, like mps https://github.com/Ravenbrook/mps, but this would be too much work.

The first thing you should look at is MPS (see https://github.com/Ravenbrook/mps and https://www.ravenbrook.com/project/mps/). It's open source, professionally maintained and very powerful, and it was used e.g. in Dylan and LispWorks.

I wonder how the MMTK compares to the venerable Ravenbrook MPS https://www.ravenbrook.com/project/mps/ which originated in Harlequin’s programming language implementations, particularly Dylan.

I am trying to implement garbage collection for my language because I want memory management for arrays/lists and strings. I am looking through LLVM's garbage collection page but the documentation isn't great. Are there any other resources that offer more concrete steps to implement garbage collection? Would it be wise to circumvent LLVM all together for garbage collection and only use something like the Memory Pool System? Thanks!

This post seems related to the authors of MPS (1) that seems to be a general garbage-collector to use with various languages.

Many GC'd languages really didn't bother with stack-allocating variable-size entities, and regardless of if they did then _precicely_ scanning the stack would be complicated without compiler help.

If the compiler doesn't leave any info to the GC, then it can't know if it's scanning a pointer or a float and if your GC strategy relies on compacting memory (ie moving objects) then trying to guess between a float or a pointer can become fatal.

(1) https://github.com/Ravenbrook/mps

> 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.