robin-hood-hashing VS STL

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.

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.

Also, FYI there is robin_hood::unordered_{map,set} which has very high performance, and is header-only and standalone.

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

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.

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.

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.

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

The implementation is mostly inspired by this comment and lessons learned from my older robin-hood-hashing hashmap.

Again, they are barely functional.

MSVC chokes on many standard-defined constructs: https://github.com/microsoft/STL/issues/1694

clang does not claim to be "mostly usable" at all - most papers are not implemented: https://clang.llvm.org/cxx_status.html#cxx20

And gcc will only start ot be usable with CMake when version 14 is released - that has not happened yet.

And, as I mentioned before, IDE support is either buggy (Visual Studio) or non-existing (any other IDE/OS). So you're off to writing in a text editor and hoping your compiler works to a somewhat usable degree. Yes, at some point people should start using modules, I agree, but to advise library maintainers to ship modularized code... the tooling just isn't there yet.

I mean, the GitHub issue is Microsoft trying to ship their standard library modularized, they employ some of the most capable folks on the planet and pay them big money to get that done, while metaphorically sitting next to the Microsoft compiler devs, and they barely, barely get it done (with bugs, as they themselves mention). This is too much for most other library maintainers.

Notice that there are in practice three distinct implementations of the C++ standard library. They're all awful to read though, here's Microsoft's std::vector https://github.com/microsoft/STL/blob/main/stl/inc/vector

However you're being slightly unfair because Rust's Vec is just defined (opaquely) as a RawVec plus a length value, so let's link RawVec, https://doc.rust-lang.org/src/alloc/raw_vec.rs.html -- RawVec is the part responsible for the messy problem of how to actually implement the growable array type.

Still, the existence of three C++ libraries with slightly different (or sometimes hugely different) quality of implementation means good C++ code can't depend on much beyond what the ISO document promises, and yet it must guard against the nonsense inflicted by all three and by lacks of the larger language. In particular everything must use the reserved prefix so that it's not smashed inadvertently by a macro, and lots of weird C++ idioms that preserve performance by sacrificing clarity of implementation are needed, even where you'd ordinarily sacrifice to get the development throughput win of everybody know what's going on. For example you'll see a lot of "pair" types bought into existence which are there to squirrel away a ZST that in C++ can't exist, using the Empty Base Optimisation. In Rust the language has ZSTs so they can just write what they meant.

Here is Microsoft's implementation of map in the standard library. I think of myself as a competent programmer / computer scientist. I couldn't write this: https://github.com/microsoft/STL/blob/f392449fb72d1a387ac502...

Be careful on Windows, the MSVC STL implementation uses the system time, so it can be badly impacted by clock adjustments: https://github.com/microsoft/STL/issues/718

Can you provide a link? If it affects import std;, I'd like to add it to my tracking issue.

Do you have a list of the bugs you've filed and their current status, like the one I have for the STL? I saw you mentioned 3 bugs 7 months ago, 2 of which were fixed in 17.6 and the third of which was a duplicate of an active bug ("deducing this" is known to not yet work with modules, which is why we don't define the feature-test macro to claim full support).

Yep. And look at our implementation's name: https://github.com/microsoft/STL