are-we-fast-yet VS Oberon

> 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.

> 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.

Great, thanks!

There are books online for free, e.g.

https://people.inf.ethz.ch/wirth/ProgInOberonWR.pdf

and https://ssw.jku.at/Research/Books/Oberon2.pdf

Oberon+ is a superset of Oberon 90 and Oberon-2. Here is more information: https://oberon-lang.github.io/, and here is the current language specification: https://github.com/oberon-lang/specification/blob/master/The.... I already had valuable feedback here on HN concerning the channel extensions. Further research brought me to the conclusion, that Oberon+ should support both, channels and also monitors, because even in Go, the sync package primitives are used twice as much as channels. Mutexes and condition variables can be emulated with channels (I tried my luck here: https://www.quora.com/How-can-we-emulate-mutexes-and-conditi...), but for efficiency reasons I think monitors should be directly supported in the language as well, even if it might collide with the goal of simplicity.

Feel free to comment here or e.g. in https://github.com/rochus-keller/Oberon/discussions/45.

> Does anyone know why Wirth never modernized his style?

Readability. It's easier to read the source code with uppercase keywords. (I think Wirth once said that code is written once but read many times). See this source code - https://raw.githubusercontent.com/rochus-keller/OberonSystem... - to get an idea of this (the uppercase keywords allow you to easily scan the blocks of code). Ofcourse, one can claim that the same can be achieved better today with colour-coded keywords.

If I remember right, the Oberon+ IDE - https://github.com/rochus-keller/Oberon - gives you an option to disable this and use lowercase keywords.

> gain a deep understanding of it .. generate smaller subsets of the system

You can use the OberonViewer for this purpose with the original source code, or the Oberon IDE with a version of the Project Oberon System which runs with SDL on all platforms, see https://github.com/rochus-keller/oberon/#binary-versions and https://github.com/rochus-keller/OberonSystem/tree/FFI

> Regardless, which one is more likely to be ported to a different architecture in the future?

Not sure I understand the question. I'm talking about CPU architectures. The current implementation is in x86 assembler. So if you want to run it on AMD64 or ARM, then you have to replace all assembler files, in the present case probable the full source code.

> what are the comparative performance benchmarks of the low-level language versus the high-level language?

I don't have any measurements. But consider that many operating systems are implemented in C (e.g. Linux) with only isolated parts in assembler, so it is easier to port to other architectures. Linux apparently is fast enough and available for nearly every CPU. Oberon in contrast to C is garbage collected, which also affects performance. I have measurements comparing the same benchmark suite implemented in C++ and in Oberon, where the former is about 22% faster (see https://github.com/rochus-keller/Oberon/blob/master/testcase...).

I actually had a similar problem some years ago and finally moved away from LLVM because of complexity, continuous research effort and performance. My current Oberon+ implementation works like this: the CIL code generator together with Mono is used during development, integrated with the IDE, using the debugging features integrated in Mono; to deploy the application and to gain another factor 2 of performance C99 instead of CIL can be generated and compiled with any compatible toolchain. Here are some performance measurements: https://github.com/rochus-keller/Oberon/blob/master/testcases/Are-we-fast-yet/Are-we-fast-yet_results_linux.pdf. Compiling to CIL is very fast and the time Mono needs to compile and run is barely noticable.

> annoying aspects was requiring the .NET runtime ... OpenJDK is a blessed implementation in a way that Mono never was

Which is unjustified, because Mono CLR is just a single executable less than 5 MB which you can download and run without a complicated installation process (see e.g. https://github.com/rochus-keller/Oberon/#binary-versions ). AOT compilation on the other hand is a huge and complex installation depending on a lot of stuff including LLVM, and the resulting executables are not really smaller than the CLR + mscorlib + app.