prorab
parallel-hashmap
prorab | parallel-hashmap | |
---|---|---|
7 | 31 | |
25 | 2,326 | |
- | - | |
7.1 | 7.8 | |
7 days ago | 29 days ago | |
Makefile | C++ | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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prorab
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what's the hate for CMake all about? Is CMake really that bad?
Take a look at https://github.com/cppfw/prorab it is a framework for simplifying makefiles, perhaps with it your makefiles will be shorter than CMake files.
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prorab: non-recursive GNU make build system
actually, I think what might be reasonable to do is to add a check into prorab.mk that it is run with GNU make. Created issue https://github.com/cppfw/prorab/issues/69
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C/C++ build system, I use xmake
same here, plus https://github.com/cppfw/prorab
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What testing and project management tools are most used in the industry
In case you decide to go with pure GNU make I can suggest a non-recursive makefiles template library https://github.com/cppfw/prorab.
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I am trying to replace CMake with c++
The only thing is that my own build system is GNU make-based, so it's syntax is also not easy :P.
parallel-hashmap
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The One Billion Row Challenge in CUDA: from 17 minutes to 17 seconds
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
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My own Concurrent Hash Map picks
Cool! Looking forward to you trying my phmap - and please let me know if you have any question.
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Boost 1.81 will have boost::unordered_flat_map...
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.
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Comprehensive C++ Hashmap Benchmarks 2022
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.
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Can C++ maps be as efficient as Python dictionaries ?
I use https://github.com/greg7mdp/parallel-hashmap when I need better performance of maps and sets.
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How to build a Chess Engine, an interactive guide
Then they should really try https://github.com/greg7mdp/parallel-hashmap, the current state of the art.
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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
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Is A* just always slow?
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.
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New Boost.Unordered containers have BIG improvements!
A comparison against phmap would also be nice.
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How to implement static typing in a C++ bytecode VM?
std::unordered_map is perfectly fine. You can do better with external libraries, like parallel hashmap, but these tend to be drop-in replacements
What are some alternatives?
cmake-init - The missing CMake project initializer
Folly - An open-source C++ library developed and used at Facebook.
xmake-vscode - 🍩 A XMake integration in Visual Studio Code
robin-hood-hashing - Fast & memory efficient hashtable based on robin hood hashing for C++11/14/17/20
cmakesl - An attempt to introduce a new scripting language for CMake
libcuckoo - A high-performance, concurrent hash table
Sharpmake - Sharpmake is an open-source C#-based solution for generating project definition files, such as Visual Studio projects and solutions, GNU makefiles, Xcode projects, etc.
rust-phf - Compile time static maps for Rust
meson - The Meson Build System
flat_hash_map - A very fast hashtable
HMake - C++ build system that uses C++ for build configuration.
tracy - Frame profiler