sgcl
are-we-fast-yet
sgcl | are-we-fast-yet | |
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14 | 18 | |
141 | 315 | |
- | - | |
8.2 | 8.8 | |
25 days ago | 3 months ago | |
C++ | Java | |
zlib License | GNU General Public License v3.0 or later |
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sgcl
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Garbage Collection for Systems Programmers
The SGCL repository contains the source code for this benchmark that uses the tracked pointers: https://github.com/pebal/sgcl/blob/main/examples/treap/treap...
- SGCL: A real-time Garbage Collector for C++
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Boehm Garbage Collector
You can look at the SGCL garbage collector for C++: https://github.com/pebal/sgcl. It works in a separate thread, is locks-free and never stops the world.
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The missing C++ smart pointer
It will never be called gc_ptr because C++ programmers have an allergy to the term GC. However, an attempt was made to implement a similar solution. Take a look at tracked_ptr: https://github.com/pebal/sgcl
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Boehm-Demers-Weiser Garbage Collector
SGCL is a real-time garbage collector for C++ without any pauses.
https://github.com/pebal/sgcl
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Now the C++ removed garbage collector support, is it still possible the have a global garbage collector in a C++ application?
Removed GC support was useless. You can have GC pointers in C++.
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The Year of C++ Successor Languages
Mutators are threads that allocate memory and manipulate pointers, they can work completely independently of the GC. A mutator needs only to tag an object when copies or moves a pointer to this object. The GC detects this tag and marks the object as alive. Here is a working implementation for C++: https://github.com/pebal/sgcl
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Optimizing Concurrent Mark&Sweep latency? What are the ways?
I don't know Rust but you can have a pauseless GC in C++. You just need to provide asynchronous access to root pointers.
- SGCL: Real-time garbage collector for C++
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.
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The Bitter Truth: Python 3.11 vs. Cython vs. C++ Performance for Simulations
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.
- Comparing Language Implementations with Objects, Closures, and Arrays
- 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?
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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Why most programming language performance comparisons are most likely wrong
Then apparently the SOM nbody program is taken as the basis of a new Java nbody program.
What are some alternatives?
rune - Rune is a programming language developed to test ideas for improving security and efficiency.
gleam - ⭐️ A friendly language for building type-safe, scalable systems!
valuable - A C++ smart-pointer with value-semantics 💎
crystal - The Crystal Programming Language
gcpp - Experimental deferred and unordered destruction library for C++
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
nottinygc - Higher-performance allocator for TinyGo WASI apps
PyCall.jl - Package to call Python functions from the Julia language
kit - not-in-progress compiler for Windows/Linux/macOS
Oberon - Oberon parser, code model & browser, compiler and IDE with debugger
bdwgc - The Boehm-Demers-Weiser conservative C/C++ Garbage Collector (bdwgc, also known as bdw-gc, boehm-gc, libgc)
Smalltalk - Parser, code model, interpreter and navigable browser for the original Xerox Smalltalk-80 v2 sources and virtual image file