perhash
flat_hash_map
perhash | flat_hash_map | |
---|---|---|
1 | 10 | |
0 | 1,680 | |
- | - | |
0.0 | 0.0 | |
over 2 years ago | 8 months ago | |
C++ | C++ | |
- | - |
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.
perhash
-
A truly fast Map implementation?
So I created a monstrosity that serves my purpose, but it is riddled with antipatterns and quirks to allow native vector performance on data retrieval and insertions. https://github.com/perara/perhash
flat_hash_map
-
Effortless Performance Improvements in C++: std:unordered_map
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.
- Inside boost::unordered_flat_map
-
A fast & densely stored hashmap and hashset based on robin-hood backward shift deletion
When int64 is the key, then the winner remains the unorder map from Malte Skarupke if (and only if) associated with a custom allocator.
-
boost::unordered map is a new king of data structures
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
-
Updating map_benchmarks: Send your hashmaps!
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.
-
Will std::set and std::unordered_set implement extra optimizations for low-number of expected unique values with too many duplicates?
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).
-
A truly fast Map implementation?
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.
-
Dolphin Emulator - Dolphin MEGA Progress Report: April and May 2021
You may want to give a try to Skarupke's HashMaps.
-
Fast insert-only hash map
You can either use Abseil's Swiss Table, or Facebook's F14, or Skarupke's flat_hash_map.
-
C++: How a simple question helped me form a New Year's Resolution
The state of the art for hash-based containers would be either Abseil's or Skarupke's.
What are some alternatives?
parallel-hashmap - A family of header-only, very fast and memory-friendly hashmap and btree containers.
unordered - Boost.org unordered module
dense_hash_map - A simple replacement for std::unordered_map
robin-map - C++ implementation of a fast hash map and hash set using robin hood hashing
unordered_dense - A fast & densely stored hashmap and hashset based on robin-hood backward shift deletion
robin-hood-hashing - Fast & memory efficient hashtable based on robin hood hashing for C++11/14/17/20
sparsepp - A fast, memory efficient hash map for C++
gtl - Greg's Template Library of useful classes.
Hopscotch map - C++ implementation of a fast hash map and hash set using hopscotch hashing
llvm-project - The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
fph-table - Flash Perfect Hash Table: an implementation of a dynamic perfect hash table, extremely fast for lookup