robin-hood-hashing
zig
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robin-hood-hashing | zig | |
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23 | 816 | |
1,465 | 30,631 | |
- | 5.2% | |
0.0 | 10.0 | |
12 months ago | 3 days ago | |
C++ | Zig | |
MIT License | MIT License |
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robin-hood-hashing
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Factor is faster than Zig
In my example the table stores the hash codes themselves instead of the keys (because the hash function is invertible)
Oh, I see, right. If determining the home bucket is trivial, then the back-shifting method is great. The issue is just that it’s not as much of a general-purpose solution as it may initially seem.
“With a different algorithm (Robin Hood or bidirectional linear probing), the load factor can be kept well over 90% with good performance, as the benchmarks in the same repo demonstrate.”
I’ve seen the 90% claim made several times in literature on Robin Hood hash tables. In my experience, the claim is a bit exaggerated, although I suppose it depends on what our idea of “good performance” is. See these benchmarks, which again go up to a maximum load factor of 0.95 (Although boost and Absl forcibly grow/rehash at 0.85-0.9):
https://strong-starlight-4ea0ed.netlify.app/
Tsl, Martinus, and CC are all Robin Hood tables (https://github.com/Tessil/robin-map, https://github.com/martinus/robin-hood-hashing, and https://github.com/JacksonAllan/CC, respectively). Absl and Boost are the well-known SIMD-based hash tables. Khash (https://github.com/attractivechaos/klib/blob/master/khash.h) is, I think, an ordinary open-addressing table using quadratic probing. Fastmap is a new, yet-to-be-published design that is fundamentally similar to bytell (https://www.youtube.com/watch?v=M2fKMP47slQ) but also incorporates some aspects of the aforementioned SIMD maps (it caches a 4-bit fragment of the hash code to avoid most key comparisons).
As you can see, all the Robin Hood maps spike upwards dramatically as the load factor gets high, becoming as much as 5-6 times slower at 0.95 vs 0.5 in one of the benchmarks (uint64_t key, 256-bit struct value: Total time to erase 1000 existing elements with N elements in map). Only the SIMD maps (with Boost being the better performer) and Fastmap appear mostly immune to load factor in all benchmarks, although the SIMD maps do - I believe - use tombstones for deletion.
I’ve only read briefly about bi-directional linear probing – never experimented with it.
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If this isn't the perfect data structure, why?
From your other comments, it seems like your knowledge of hash tables might be limited to closed-addressing/separate-chaining hash tables. The current frontrunners in high-performance, memory-efficient hash table design all use some form of open addressing, largely to avoid pointer chasing and limit cache misses. In this regard, you want to check our SSE-powered hash tables (such as Abseil, Boost, and Folly/F14), Robin Hood hash tables (such as Martinus and Tessil), or Skarupke (I've recently had a lot of success with a similar design that I will publish here soon and is destined to replace my own Robin Hood hash tables). Also check out existing research/benchmarks here and here. But we a little bit wary of any benchmarks you look at or perform because there are a lot of factors that influence the result (e.g. benchmarking hash tables at a maximum load factor of 0.5 will produce wildly different result to benchmarking them at a load factor of 0.95, just as benchmarking them with integer keys-value pairs will produce different results to benchmarking them with 256-byte key-value pairs). And you need to familiarize yourself with open addressing and different probing strategies (e.g. linear, quadratic) first.
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boost::unordered standalone
Also, FYI there is robin_hood::unordered_{map,set} which has very high performance, and is header-only and standalone.
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Solving “Two Sum” in C with a tiny hash table
std::unordered_map is notoriously slow, several times slower than a "proper" hashmap implementation like Google's absl or Martin's robin-hood-hashing [1]. That said, std::sort is not the fastest sort implementation, either. It is hard to say which will win.
[1]: https://github.com/martinus/robin-hood-hashing
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Convenient Containers v1.0.3: Better compile speed, faster maps and sets
The main advantage of the latest version is that it reduces build time by about 53% (GCC 12.1), based on the comprehensive test suit found in unit_tests.c. This improvement is significant because compile time was previously a drawback of this library, with maps and sets—in particular—compiling slower than their C++ template-based counterparts. I achieved it by refactoring the library to do less work inside API macros and, in particular, use fewer _Generic statements, which seem to be a compile-speed bottleneck. A nice side effect of the refactor is that the library can now more easily be extended with the planned dynamic strings and ordered maps and sets. The other major improvement concerns the performance of maps and sets. Here are some interactive benchmarks[1] comparing CC’s maps to two popular implementations of Robin Hood hash maps in C++ (as well as std::unordered_map as a baseline). They show that CC maps perform roughly on par with those implementations.
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Effortless Performance Improvements in C++: std:unordered_map
For anyone in a situation where a set/map (or unordered versions) is in a hot part of the code, I'd also highly recommend Robin Hood: https://github.com/martinus/robin-hood-hashing
It made a huge difference in one of the programs I was running.
- Inside boost::unordered_flat_map
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What are some cool modern libraries you enjoy using?
Oh my bad. Still thought -- your name.. it looks very familiar to me. Are you the robin_hood hashing guy perhaps? Yes you are! My bad -- https://github.com/martinus/robin-hood-hashing.
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Performance comparison: counting words in Python, C/C++, Awk, Rust, and more
Got a bit better C++ version here which uses a couple libraries instead of std:: stuff - https://gist.github.com/jcelerier/74dfd473bccec8f1bd5d78be5a... ; boost, fmt and https://github.com/martinus/robin-hood-hashing
$ g++ -I robin-hood-hashing/src/include -O2 -flto -std=c++20 -fno-exceptions -fno-unwind-tables -fno-asynchronous-unwind-tables -lfmt
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A fast & densely stored hashmap and hashset based on robin-hood backward shift deletion
The implementation is mostly inspired by this comment and lessons learned from my older robin-hood-hashing hashmap.
zig
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Memory-mapped IO registers in Zig. (2021)
There is an issue proposing this approach: https://github.com/ziglang/zig/issues/4284
- Zig Programming Language
- Zig Language 0.12 Release
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Zig 0.12.0 Release Notes
https://github.com/ziglang/zig/issues/224
e.g.:
> > When debugging/prototyping, it's useful to comment out a line without having to refactor, e.g.
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How to Write a PHP Extension with Zig?
When writing code in a scripting language, sometimes you need that extra bit of performance (or maybe an async feature from Zig).
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Bun - The One Tool for All Your JavaScript/Typescript Project's Needs?
NodeJS is by no means a slow runtime, it wouldn’t be so popular if it was. But compared to Bun, it’s slow. Bun was built from the ground up with speed in mind, using both JavascriptCore and Zig. The Bun team spent an enormous amount of time and energy trying to make Bun fast, including lots of profiling, benchmarking, and optimizations.
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Bun 1.1
ntdll.dll!RtlUserThreadStart()
There are valid reasons to use APIs from NTDLL. Where I disagree with zig#1840 is the idea that it is always better to use NTDLL versions of API. Every other software ecosystem uses the standard Win32 APIs and diverging from that without a good reason seems like a good way to have unexpected behavior. One concrete example is most users and programmers expect Windows to redirect some file system paths when running on WOW64. But this is implemented in Kernel32, not ntdll.
https://github.com/ziglang/zig/issues/11894
- Zig, Rust, and Other Languages
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Nanos – A Unikernel
Zig also has an IRC channel on libera (#zig) that is moderated by Andrew Kelley.[1]
[1] https://github.com/ziglang/zig/wiki/Community
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What are some alternatives?
parallel-hashmap - A family of header-only, very fast and memory-friendly hashmap and btree containers.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
STL - MSVC's implementation of the C++ Standard Library.
Odin - Odin Programming Language
robin-map - C++ implementation of a fast hash map and hash set using robin hood hashing
v - Simple, fast, safe, compiled language for developing maintainable software. Compiles itself in <1s with zero library dependencies. Supports automatic C => V translation. https://vlang.io
xxHash - Extremely fast non-cryptographic hash algorithm
rust - Empowering everyone to build reliable and efficient software.
C++ Format - A modern formatting library
go - The Go programming language
tracy - Frame profiler
ssr-proxy-js - A Server-Side Rendering Proxy focused on customization and flexibility!