parallel-hashmap VS libunifex

Standard library maps/unordered_maps are themselves notoriously slow anyway. A sparse_hash_map from abseil or parallel-hashmaps[1] would be better.

[1] https://github.com/greg7mdp/parallel-hashmap

Cool! Looking forward to you trying my phmap - and please let me know if you have any question.

I do this as well in my phmap and gtl implementations. It makes the tables look worse in benchmarks like the above, but prevents really bad surprises occasionally.

Thanks a lot for the great benchmark, Martin. Glad you used different hash functions, because I do sacrifice some speed to make sure that the performance of my hash maps doesn't degrade drastically with poor hash functions. Happy to see that my phmap and gtl (the C++20 version) performed well.

I use https://github.com/greg7mdp/parallel-hashmap when I need better performance of maps and sets.

Then they should really try https://github.com/greg7mdp/parallel-hashmap, the current state of the art.

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

std::unordered_map is notorious for being slow. Use a better implementation (I like the flat naps from here, which are the same as abseil’s). The question that needs to be asked too is if you need to use a map.

A comparison against phmap would also be nice.

std::unordered_map is perfectly fine. You can do better with external libraries, like parallel hashmap, but these tend to be drop-in replacements

I'm curious what led you to this conclusion. If you ran into scalability issues with its static_thread_pool, then that's a known issue. If it's something else, the authors (of which I'm one) would love to know.

Instead of calling add_executable you will call add_library. Here is a (only moderately complicated) production example of a library that can be built standalone (along with tests and example executables), or as a subproject, where it builds only the library

My original thought was to build my DDS implementation on top of libunifex in anticipation for standardization: https://github.com/facebookexperimental/libunifex

I'm trying to understand how to work with the proposed executors in a project, but after watching Eric Niebler's cppcon talks (https://youtu.be/xLboNIf7BTg) and looking at the libunifex examples (https://github.com/facebookexperimental/libunifex/tree/main/examples) I still have a hard time wrapping my head around how to employ the sender/receiver pattern in a larger project.

You have coroutines in C++20 but there is also the executives proposal that's making it's way into C++23 that is available as a library under the name unifex that only requires C++14

Asio-grpc makes exactly that possible by providing an Asio execution_context compatible interface to the CompletionQueue. It supports all types of RPCs (including generic ones), completion tokens, cancellation, as well as libunifex sender/receiver (if you want to try out what might become std::execution). The latest release (v1.7.0) also introduced a GrpcStream class for writing Rust/Golang select-style code.

P2300: they are trying to standardize facebookexperimental/libunifex

All the network handling for Instagram and all other Meta apps on all platforms is handled by their own C++ library https://github.com/facebookexperimental/libunifex.

This. I'd also like to mention libunifex. It's entirely based on tag_invoke and is a testament as to how much power it actually provides. On the other hand, it also proves how cumbersome it is to define CPOs with tag_invoke. But IMO it's a lot better than anything else anyone has ever created, and users usually don't need to define new CPOs, only library writers do, so there's that.

A work in progress implementation of the library: https://github.com/facebookexperimental/libunifex