Oberon
are-we-fast-yet
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Oberon | are-we-fast-yet | |
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76 | 18 | |
415 | 315 | |
- | - | |
7.4 | 8.8 | |
15 days ago | about 1 month ago | |
C++ | Java | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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Oberon
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Boehm Garbage Collector
> 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.
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Niklaus Wirth, or the Importance of Being Simple
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.
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Tex-Oberon: Make Project Oberon Pretty Again
> 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.
- FreeOberon cross-platform Oberon language IDD
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Project Oberon (New Edition 2013)
> 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
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KolibriOS on Single Floppy Disk
> 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...).
- Why Use Pascal?
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C or LLVM for a fast backend?
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.
- Do transpilers just use a lot of string manipulation and concatenation to output the target language?
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Native AOT Overview
> 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.
are-we-fast-yet
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Boehm Garbage Collector
> 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.
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A C++ version of the Are-we-fast-yet benchmark suite
See https://github.com/smarr/are-we-fast-yet/blob/master/docs/guidelines.md.
- Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
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.NET 6 vs. .NET 5: up to 40% speedup
> 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).
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Is CoreCLR that much faster than Mono?
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?
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Is CoreCLR really that much faster than Mono?
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.
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.
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Ranking programming languages by energy efficiency (scientific paper, 2021)
If you want to compare different language implementations, you have to somehow control what you compare; the implementations must do the same thing with the same quantity, and especially for VMs and interpreters you want to make sure that you're not comparing a native library call with an interpreted version of the same function. The Are-we-fast-yet has a decent set of rules from by point of view to enable fair comparisons, and even though it's still possible to use ideomatic paradigms supported by the language. Have you seen this document: https://github.com/smarr/are-we-fast-yet/blob/master/docs/guidelines.md?
Personally, I like this benchmark suite better, but unfortunately the number of implementations is still quite small: https://github.com/smarr/are-we-fast-yet
See the publication (https://stefan-marr.de/papers/dls-marr-et-al-cross-language-compiler-benchmarking-are-we-fast-yet/) about what rules they apply. The code is ideomatic, but they require that all implementations use the same data structure implementations to make it comparable. Here is a discussion in the pull request: https://github.com/smarr/are-we-fast-yet/pull/30.
What are some alternatives?
gleam - ⭐️ A friendly language for building type-safe, scalable systems!
oberon-riscv - Oberon RISC-V port, based on Samuel Falvo's RISC-V compiler and Peter de Wachter's Project Norebo. Part of an academic project to evaluate Project Oberon on RISC-V.
crystal - The Crystal Programming Language
MoarVM - A VM with adaptive optimization and JIT compilation, built for Rakudo
Smalltalk - Parser, code model, interpreter and navigable browser for the original Xerox Smalltalk-80 v2 sources and virtual image file
tectonic - A modernized, complete, self-contained TeX/LaTeX engine, powered by XeTeX and TeXLive.
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
aws-lambda-rust-runtime - A Rust runtime for AWS Lambda
nappgui - Cross-Platform C SDK (precompiled)
atldotnet - Fully managed, portable and easy-to-use C# library to read and edit audio data and metadata (tags) from various audio formats, playlists and CUE sheets
core - .NET news, announcements, release notes, and more!
PyCall.jl - Package to call Python functions from the Julia language