hana
robin-hood-hashing
Our great sponsors
hana | robin-hood-hashing | |
---|---|---|
4 | 23 | |
1,633 | 1,465 | |
1.5% | - | |
5.4 | 0.0 | |
4 days ago | 12 months ago | |
C++ | C++ | |
Boost Software License 1.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hana
-
What are some C++ projects with high quality code that I can read through?
I like boost::hana. Wish I had more chances to use it at work.
-
Simple `struct` static reflection system I use that tracks names, attributes (in my own code I also keep `constexpr` hashes of names) -- example gets fully inlined into `main()`
And then there's also the longstanding issue with default member initializers -- https://github.com/boostorg/hana/issues/409 -- which was a deal-breaker.The PROP system avoids this issue precisely by having the macro be as local as possible: the default member initializer is outside the macro. It does this while still letting you attach additional PropAttribs metadata (I'm not sure what additional metadata attachment looks like in Hana? haven't really tried). Default values and additional attributes are by and large quite more important to me in my use cases for reflection (component types in game ECS) than range syntax (everything I've needed and can think of needing is covered by for-each loop).
-
cppa2z - Document modern C++ using unit tests
oh yes definitely, but I meant more the particular way I've done it so far - which has been to implement something similar to boost::hana::is_valid(), to make it take the least amount of boilerplate code per-use-case as possible: zero additional lines of code.
-
C Implementation Challenge Replacing Stdmove And
Boost.Hana developers measured a very noticeable 15% decrease in compile time switching from a function call (admittedly -- with an additional layer of wrapper) to a raw static_cast. foonathan himself saw a little over a 5% decrease in his actual code. miki151 saw about a 3% improvement from replacing move with MOV only, ignoring forward. In a completely synthetic benchmark where I just generate a ton of move calls in a row, I get a little over 40% decrease in compile time.
robin-hood-hashing
-
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.
-
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.
-
boost::unordered standalone
Also, FYI there is robin_hood::unordered_{map,set} which has very high performance, and is header-only and standalone.
-
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.
-
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.
-
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
-
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.
-
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
-
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.
What are some alternatives?
mio - Cross-platform C++11 header-only library for memory mapped file IO
parallel-hashmap - A family of header-only, very fast and memory-friendly hashmap and btree containers.
sml - C++14 State Machine library
STL - MSVC's implementation of the C++ Standard Library.
dyno - Runtime polymorphism done right
robin-map - C++ implementation of a fast hash map and hash set using robin hood hashing
filesystem - An implementation of C++17 std::filesystem for C++11 /C++14/C++17/C++20 on Windows, macOS, Linux and FreeBSD.
xxHash - Extremely fast non-cryptographic hash algorithm
doctest - The fastest feature-rich C++11/14/17/20/23 single-header testing framework
C++ Format - A modern formatting library
cppa2z - Document modern C++ using unit tests
tracy - Frame profiler