robin-hood-hashing VS abseil-cpp

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.

Yeah, it's nice! And Abseil does it, IFF you use LLVM libc++.

https://github.com/abseil/abseil-cpp/blob/master/absl/string...

The standard adopted it as resize_and_overwrite. Which I think is a little clunky.

This may be confusing to those familiar with Google's libraries. The baseline is the Go BTree, which I personally never heard of until just now, not the C++ absl::btree_set. The benchmarks aren't directly comparable, but the C++ version also comes with good microbenchmark coverage.

https://github.com/google/btree

https://github.com/abseil/abseil-cpp/blob/master/absl/contai...

An implementation of call_once that accommodates callbacks that throw: https://github.com/abseil/abseil-cpp/blob/master/absl/base/c...

I wouldn't say it's that cryptic. It's just a few bitwise rotations/shifts/xor operations.

You can see hashing optimizations as well https://www.deepmind.com/blog/alphadev-discovers-faster-sort..., https://github.com/abseil/abseil-cpp/commit/74eee2aff683cc7d...

I was one of the members who reviewed expertly what has been done both in sorting and hashing. Overall it's more about assembly, finding missed compiler optimizations and balancing between correctness and distribution (in hashing in particular).

It was not revolutionary in a sense it hasn't found completely new approaches but converged to something incomprehensible for humans but relatively good for performance which proves the point that optimal programs are very inhuman.

Note that for instructions in sorting, removing them does not always lead to better performance, for example, instructions can run in parallel and the effect can be less profound. Benchmarks can lie and compiler could do something differently when recompiling the sort3 function which was changed. There was some evidence that the effect can come from the other side.

For hashing it was even funnier, very small strings up to 64 bit already used 3 instructions like add some constant -> multiply 64x64 -> xor upper/lower. For bigger ones the question becomes more complicated, that's why 9-16 was a better spot and it simplified from 2 multiplications to just one and a rotation. Distribution on real workloads was good, it almost passed smhasher and we decided it was good enough to try out in prod. We did not rollback as you can see from abseil :)

But even given all that, it was fascinating to watch how this system was searching and was able to find particular programs can be further simplified. Kudos to everyone involved, it's a great incremental change that can bring more results in the future.

Check out https://abseil.io. It offers absl::optional, which is a backport of std::optional.