abseil-cpp
countwords
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abseil-cpp | countwords | |
---|---|---|
54 | 43 | |
13,917 | 209 | |
2.4% | - | |
9.5 | 5.9 | |
4 days ago | about 2 years ago | |
C++ | Rust | |
Apache License 2.0 | MIT License |
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.
abseil-cpp
- Sane C++ Libraries
- Open source collection of Google's C++ libraries
- Is Ada safer than Rust?
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Appending to an std:string character-by-character: how does the capacity grow?
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.
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Shaving 40% Off Google’s B-Tree Implementation with Go Generics
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...
- Faster Sorting Beyond DeepMind’s AlphaDev
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“Once” one-time concurrent initialization with an integer
An implementation of call_once that accommodates callbacks that throw: https://github.com/abseil/abseil-cpp/blob/master/absl/base/c...
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[R] AlphaDev discovers faster sorting algorithms
I wouldn't say it's that cryptic. It's just a few bitwise rotations/shifts/xor operations.
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Deepmind Alphadev: Faster sorting algorithms discovered using deep RL
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.
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Backward compatible implementations of newer standards constructs?
Check out https://abseil.io. It offers absl::optional, which is a backport of std::optional.
countwords
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How fast is really ASP.NET Core?
"dang, I didn't know that was 50x faster than the idiomatic way" or "hey, I didn't know that this implementation in the stdlib prioritized this over that and made this so slow, that's interesting" -- .e.g, there's some kinda neat language details to be found in something like Ben Hoyt's community word count benchmarks repo and 'simple' vs 'optimal' code: https://github.com/benhoyt/countwords
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Correct name for word matching problem
It benchmarks programs that count the total number of unique words in some input. It's not exactly equivalent to your problem, but it's similarish. All of the programs used some kind of hash map for lookups, but I contributed a program that used a trie. Its performance in my experience varies depending on the CPU interestingly enough. On my old CPU (i7-6900K) it was a little slower, but on my new cpu (i9-12900KS) it was faster.
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Performance comparison: counting words in Python, C/C++, Awk, Rust, and more
Why not read the source code? :-)
I wrote comments explaining things: https://github.com/benhoyt/countwords/blob/8553c8f600c40a462...
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do you guys prefer functional programming style when using rust?
My own code example of a drastic speed up (~25%) simply replacing a couple of for loops with iters: https://github.com/benhoyt/countwords/pull/115
- Ripen scripting engine (Similar to RetroForth, but tiny)
- Performance comparison: counting words in Python, Go, C++, C, AWK, Forth, and Rust
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The difference between Go and Rust
And yet Go was faster than Rust in a simple app that count words: https://benhoyt.com/writings/count-words/
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How to Rapidly Improve at Any Programming Language
> but the performance profiles & characteristics that we must know about in order to make a choice on which tool to use. And it shouldn't be that each user has to figure it out on their own, dig into PR's or whatever.
That's an interesting take – I like the idea of a catalog of standard tasks with implementations in several languages as well as their performance characteristics. I suppose Rosetta Code gets the ball rolling with this, but it's missing some performance metrics. It reminds me of [Ben Hoyt's piece](https://benhoyt.com/writings/count-words/) on counting unique words in the KJV Bible in different languages.
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Faster string keyed maps in Go
This article shows that map lookups can be optimized by using the (unintuitive) pattern:
- Go beats out several top languages including Rust in this performance matchup
What are some alternatives?
Folly - An open-source C++ library developed and used at Facebook.
CPython - The Python programming language
Boost - Super-project for modularized Boost
coreutils - upstream mirror
spdlog - Fast C++ logging library.
llfio - P1031 low level file i/o and filesystem library for the C++ standard
Qt - Qt Base (Core, Gui, Widgets, Network, ...)
parallel-hashmap - A family of header-only, very fast and memory-friendly hashmap and btree containers.
EASTL - Obsolete repo, please go to: https://github.com/electronicarts/EASTL
securitytxt.org - Static website for security.txt.
BDE - Basic Development Environment - a set of foundational C++ libraries used at Bloomberg.
leocad - A CAD application for creating virtual LEGO models