flat_hash_map VS llvm-project

If you don't need all the guarantees provided by std::unordered_map (pointer stability is usually the big one), you can go a /lot/ faster with a map that uses open addressing.

Some of my favorite alternative hash map implementations are ska::flat_hash_map and ska::bytell_hash_map from https://github.com/skarupke/flat_hash_map. They're fast, and the single header implementation makes them easy to add to a project. For my use cases they generally offer similar performance to abseil and folly F14.

Don't be fooled by the fact that they haven't been updated in ~5 years. I've been using them for nearly that long and have yet to find any bugs.

When int64 is the key, then the winner remains the unorder map from Malte Skarupke if (and only if) associated with a custom allocator.

Unordered hash map shootout CMAP = https://github.com/tylov/STC KMAP = https://github.com/attractivechaos/klib PMAP = https://github.com/greg7mdp/parallel-hashmap FMAP = https://github.com/skarupke/flat_hash_map RMAP = https://github.com/martinus/robin-hood-hashing HMAP = https://github.com/Tessil/hopscotch-map TMAP = https://github.com/Tessil/robin-map UMAP = std::unordered_map Usage: shootout [n-million=40 key-bits=25] Random keys are in range [0, 2^25). Seed = 1656617916: T1: Insert/update random keys: KMAP: time: 1.949, size: 15064129, buckets: 33554432, sum: 165525449561381 CMAP: time: 1.649, size: 15064129, buckets: 22145833, sum: 165525449561381 PMAP: time: 2.434, size: 15064129, buckets: 33554431, sum: 165525449561381 FMAP: time: 2.112, size: 15064129, buckets: 33554432, sum: 165525449561381 RMAP: time: 1.708, size: 15064129, buckets: 33554431, sum: 165525449561381 HMAP: time: 2.054, size: 15064129, buckets: 33554432, sum: 165525449561381 TMAP: time: 1.645, size: 15064129, buckets: 33554432, sum: 165525449561381 UMAP: time: 6.313, size: 15064129, buckets: 31160981, sum: 165525449561381 T2: Insert sequential keys, then remove them in same order: KMAP: time: 1.173, size: 0, buckets: 33554432, erased 20000000 CMAP: time: 1.651, size: 0, buckets: 33218751, erased 20000000 PMAP: time: 3.840, size: 0, buckets: 33554431, erased 20000000 FMAP: time: 1.722, size: 0, buckets: 33554432, erased 20000000 RMAP: time: 2.359, size: 0, buckets: 33554431, erased 20000000 HMAP: time: 0.849, size: 0, buckets: 33554432, erased 20000000 TMAP: time: 0.660, size: 0, buckets: 33554432, erased 20000000 UMAP: time: 2.138, size: 0, buckets: 31160981, erased 20000000 T3: Remove random keys: KMAP: time: 1.973, size: 0, buckets: 33554432, erased 23367671 CMAP: time: 2.020, size: 0, buckets: 33218751, erased 23367671 PMAP: time: 2.940, size: 0, buckets: 33554431, erased 23367671 FMAP: time: 1.147, size: 0, buckets: 33554432, erased 23367671 RMAP: time: 1.941, size: 0, buckets: 33554431, erased 23367671 HMAP: time: 1.135, size: 0, buckets: 33554432, erased 23367671 TMAP: time: 1.064, size: 0, buckets: 33554432, erased 23367671 UMAP: time: 5.632, size: 0, buckets: 31160981, erased 23367671 T4: Iterate random keys: KMAP: time: 0.748, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 CMAP: time: 0.627, size: 23367671, buckets: 33218751, repeats: 8, sum: 4465059465719680 PMAP: time: 0.680, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 FMAP: time: 0.735, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 RMAP: time: 0.464, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 HMAP: time: 0.719, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 TMAP: time: 0.662, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 UMAP: time: 6.168, size: 23367671, buckets: 31160981, repeats: 8, sum: 4465059465719680 T5: Lookup random keys: KMAP: time: 0.943, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 CMAP: time: 0.863, size: 23367671, buckets: 33218751, lookups: 34235332, found: 29040438 PMAP: time: 1.635, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 FMAP: time: 0.969, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 RMAP: time: 1.705, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 HMAP: time: 0.712, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 TMAP: time: 0.584, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 UMAP: time: 1.974, size: 23367671, buckets: 31160981, lookups: 34235332, found: 29040438

I believe that when the number of elements is larger than 4 (a rough estimation), the associative linear table won't be faster than ska::flat_hash_map or fph-table with the identity hash function. If you look at the benchmark results, you will find that the average lookup time may well be less than 2 nanoseconds when item number is smaller than one thousand on morden CPUs. For these two hash tables, there are only about ten instructions in the critical path of lookup. And this should be faster than the linear search in a associative table, where there are a lot of branches and comparing instructions. However, you should benchmark it youself to get the real conclusion. This is just a simple analysis on paper from mine. By the way, the associative table can be faster if it is implemented with hardware circuits or SIMD instructions.

I have done similar benchmarks (with a lot of care) a few months ago, but the results were different than yours when using a very fast hash map. I was surprised that even for small maps, flat_hash_map was faster than searching small arrays (searching int64_t).

You should look for flat-hash-maps. This is a good implementation, skarupke/flat_hash_map. The author also has a talk about the implementation at one of the boost conferences on youtube.

You may want to give a try to Skarupke's HashMaps.

You can either use Abseil's Swiss Table, or Facebook's F14, or Skarupke's flat_hash_map.

The state of the art for hash-based containers would be either Abseil's or Skarupke's.

'Computer Architeture: A Quantitative Apporach" and/or more specific design types (mips, arm, etc) can be found under the Morgan Kaufmann Series in Computer Architeture and Design.

"Getting Started with LLVM Core Libraries: Get to Grips With Llvm Essentials and Use the Core Libraries to Build Advanced Tools "

"The Architecture of Open Source Applications (Volume 1) : LLVM" https://aosabook.org/en/v1/llvm.html

"Tourist Guide to LLVM source code" : https://blog.regehr.org/archives/1453

llvm home page : https://llvm.org/

llvm tutorial : https://llvm.org/docs/tutorial/

llvm reference : https://llvm.org/docs/LangRef.html

learn by examples : C source code to 'llvm' bitcode : https://stackoverflow.com/questions/9148890/how-to-make-clan...

See

https://github.com/llvm/llvm-project/issues/88344

https://fortran-lang.discourse.group/t/flang-new-how-to-forc...

You can never mistake type_declaration with an identifier, otherwise the program will not work. Aside from that constraint, you are free to name them whatever you like, there is no one standard, and each parser has it own naming conventions, unless you are planning to use something like LLVM. If you are interested, you can see examples of naming in different language parsers in the AST Explorer.

> There is one way to make the LLVM JIT compiler more usable, but I fear it’s going to take years to be implemented: being able to cache and reuse compiled queries.

Actually, it's implemented in LLVM for years :) https://github.com/llvm/llvm-project/commit/a98546ebcd2a692e...

> It's true, this was a CVE in Rust and not a CVE in C++, but only because C++ doesn't regard the issue as a problem at all. The problem definitely exists in C++, but it's not acknowledged as a problem, let alone fixed.

Can you find a link that substantiates your claim? You're throwing out some heavy accusations here that don't seem to match reality at all.

Case in point, this was fixed in both major C++ libraries:

https://github.com/gcc-mirror/gcc/commit/ebf6175464768983a2d...

https://github.com/llvm/llvm-project/commit/4f67a909902d8ab9...

So what C++ community refused to regard this as an issue and refused to fix it? Where is your supporting evidence for your claims?

For everyone else looking for the magic in this almost 7k lines monster, look at line 6610 [1].

[1] https://github.com/llvm/llvm-project/blob/8ec28af8eaff5acd0d...

Through value tracking. It's actually LLVM that does this, GCC probably does it as well, so in theory explicit bounds checks in regular C code would also be removed by the compiler.

How it works exactly I don't know, and apparently it's so complex that it requires over 9000 lines of C++ to express:

https://github.com/llvm/llvm-project/blob/main/llvm/lib/Anal...

https://github.com/llvm/llvm-project/blob/main/flang/docs/F2...

• Go • Rust • Lua • pure C (sans C++) • 6502 assembly • WebAssembly • compiler backends, like LLVM or Cranelift