fast_float
rust
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fast_float | rust | |
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15 | 2674 | |
1,256 | 91,922 | |
2.5% | 2.8% | |
8.8 | 10.0 | |
8 days ago | 3 days ago | |
C++ | Rust | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
fast_float
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Parquet: More than just “Turbo CSV”
> Google put in significant engineering effort into "Ryu", a parsing library for double-precision floating point numbers: https://github.com/ulfjack/ryu
It's not a parsing library, but a printing one, i.e., double -> string. https://github.com/fastfloat/fast_float is a parsing library, i.e., string -> double, not by Google though, but was indeed motivated by parsing JSON fast https://lemire.me/blog/2020/03/10/fast-float-parsing-in-prac...
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Can sanitizers find the two bugs I wrote in C++?
This makes sense for integers but betware floating point from_chars - libc++ still doesn't implement it and libstdc++ implements it by wrapping locale-dependent libc functions which involves temporarily changing the thread locale and possibly memory allocation to make the passed string 0-terminated. IMO libstdc++'s checkbox "solution" is worse than not implementing it at all - user's are better off using Lemire's API-compatible fast_float implementation [0].
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Parsing can become accidentally quadratic because of sscanf
Just above this comment is a merged PR, which references fast_float library: https://github.com/fastfloat/fast_float
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Making Rust Float Parsing Fast: libcore Edition
Daniel Lemire @lemire (creator of the algorithm, author of the C++ implementation, and provided constant feedback to help guide the PR).
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RapidObj v0.1 - A fast, header-only, C++17 library for parsing Wavefront .obj files.
And out of 6,000 lines in the file, at least 3000 are other people's code: earcut for polygon triangulation and fast_float because .obj files typically contain a lot of floating point numbers so it's important to parse them quickly.
This library is optimised for loading and parsing big .obj files. Optimisation techniques used are: asynchronous IO, parallel parsing, and it uses fast_float to quickly parse floating point numbers.
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First release of dragonbox, a fast float-to-string conversion algorithm, is available
How this compares to https://github.com/fastfloat/fast_float ?
- Eisel-Lemire Algorithm
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Ask HN: C++17/C++20 example projects with good style?
Some fun things I've figured out:
* https://github.com/fastfloat/fast_float <-- for fast float/double conversion
* from_chars() for fast integer conversion (though I benchmarked a few that were faster)
* https://github.com/simdjson/simdjson <-- apparently the fastest JSON library
It's hard to program normal anymore, for every little thing I ask myself "but I want it faster!" TypeScript was so much easier, other than O(n) and friends, I never cared xD
- Fastfloat – C++ library 4x faster than strtod
rust
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What Are Const Generics and How Are They Used in Rust?
The above Assert<{N % 2 == 1}> requires #![feature(generic_const_exprs)] and the nightly toolchain. See https://github.com/rust-lang/rust/issues/76560 for more info.
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Algorithms for Modern Hardware
There’s also other reasons. For example, take binary search:
* prefetch + cmov. These should be part of the STL but languages and compilers struggle to emit the cmov properly (Rust’s been broken for 6 years: https://github.com/rust-lang/rust/issues/53823). Prefetch is an interesting one because while you do optimize the binary search in a micro benchmark, you’re potentially putting extra pressure on the cache with “garbage” data which means it’s a greedy optimization that might hurt surrounding code. Probably should have separate implementations as binary search isn’t necessarily always in the hot path.
* Eytzinger layout has additional limitations that are often not discussed when pointing out “hey this is faster”. Adding elements is non-trivial since you first have to add + sort (as you would for binary search) and then rebuild a new parallel eytzinger layout from scratch (i.e. you’d have it be an index of pointers rather than the values themselves which adds memory overhead + indirection for the comparisons). You can’t find the “insertion” position for non-existent elements which means it can’t be used for std::lower_bound (i.e. if the element doesn’t exist, you just get None back instead of Err(position where it can be slotted in to maintain order).
Basically, optimizations can sometimes rely on changing the problem domain so that you can trade off features of the algorithm against the runtime. These kinds of algorithms can be a bad fit for a standard library which aims to be a toolbox of “good enough” algorithms and data structures for problems that appear very very frequently. Or they could be part of the standard library toolkit just under a different name but you also have to balance that against maintenance concerns.
- Rust: Actix-web and Daily Logging
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Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
But that said, - and again I might be a bit biased - Groovy is too slow for me! I compared it to Rust in this LinkedIn post and it was waaaaay slow. Keep in mind that subjectively comparing programming languages might be a tricky business. But at the end, it will be up to your use case/project to prefer a language over the other.
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Top Paying Programming Technologies 2024
13. Rust - $87,012
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Dada, an Experiement by the Creators of Rust
Yes, actually.
https://github.com/rust-lang/rust/blob/d0ea1d767925d53b2230e...
Limited to the rust codebase itself, but I'm sure the developers would force it on everyone else if they thought they could get away with it.
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7 Programming Languages Every Cloud Engineer Should Know in 2024!
Rust is gaining momentum in the cloud computing domain due to its emphasis on safety, speed, and concurrency without a garbage collector. These features make Rust an appealing choice for cloud engineers looking to develop high-performance, secure, and reliable cloud services and infrastructure. Rust's memory safety guarantees and efficient compilation to machine code position it as an ideal language for system-level and embedded applications in cloud environments, where performance and security are paramount.
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Borrow Checking Without Lifetimes
> I'm not sure what's neutered about Rust's current plans for generators
They're neutered because they can't suspend and transfer control to a function other than the one that called them ("Note also that "coroutines" here are really "semicoroutines" since they can only yield back to their caller." https://lang-team.rust-lang.org/design_notes/general_corouti...) and you can't pass values into resume and get them out from the yield statement in the coroutine (https://github.com/rust-lang/rust/issues/43122#issuecomment-...).
> and they aren't separate from async, they're the foundation that async desugars to.
Yeah I just looked it up again and I don't know why I had it in my head that they were separate, you're correct, they are the same thing under the hood, so honestly that eliminates my biggest problem with them.
> 'm also not sure what your objection is to Polonius, which, so far, is still just a strictly more permissive version of the borrow checker, with nothing new to learn on the user end.
The entire model is different under the hood, though, since it switches from lifetimes+borrows to loans, and so in order to fully understand its behavior the user really would have to change their mental model, and as I said above I'm a huge fan of the lifetimes model and less so of the loan model. I just feel like it's much more natural to treat the ownership of a memory object and therefore amount of time in your code that object lives as the fixed point, and borrows as wrong for outliving what they refer to, then to treat borrows as the fixed point, and objects as wrong for going out of scope and being dropped before the borrow ends, because the fundamental memory management model of Rust is single ownership of objects, moves, and scope based RAII via Drop, so the lifetime of an object kind of is the more basic building block of the memory model, with borrows sort of conceptually orbiting around that and naturally being adjusted to fit that, with the checker being a way to force you to adhere to that. The loan based way of thinking would make more sense for an ARC-based language where references actually are more basic because objects really do only live for as long as there are references to them.
> you can't pass values into resume and get them out from the yield statement in the coroutine
I think that the linked comment is out of date, and that this is supported now (hard to tell because it hasn't been close enough to stabilization to be properly documented): https://github.com/rust-lang/rust/pull/68524
As for Polonius changing the underlying mental model, I think this is a natural progression. Rust 1.0 tried to present a simple lexical model of borrowing, and then enough people complained that it has long since replaced the simple model with non-lexical lifetimes in order to trade simplicity for "do what I mean". And since it's not allowed to break any old code, if you want to continue treating borrowing like it has the previous model then that shouldn't present any difficulties.
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Why do we need for an Undefined Behavior Annex to C++
I don't see where those methods are getting called from a Unix signal handler but the code is complex enough that it's easy to miss, especially perusing through github instead of vscode.
AFAICT those methods are called from `guard::current`. In turn, `guard::current` is used to initialize TLS data when a thread is spawned before a signal is generated (& right after the signal handler is installed): https://github.com/rust-lang/rust/blob/26907374b9478d84d766a...
It doesn't look like there's any UB behavior being relied upon but I could very easily be misreading. If I missed it, please give me some more pointers cause this should be a github issue if it's the case - calling non async-safe methods from a signal handler typically can result in a deadlock which is no bueno.
What are some alternatives?
carbon-lang - Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
Odin - Odin Programming Language
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
Rustup - The Rust toolchain installer
rust-analyzer - A Rust compiler front-end for IDEs [Moved to: https://github.com/rust-lang/rust-analyzer]
go - The Go programming language
mimalloc - mimalloc is a compact general purpose allocator with excellent performance.
scala - Scala 2 compiler and standard library. Bugs at https://github.com/scala/bug; Scala 3 at https://github.com/scala/scala3
dragonbox - Reference implementation of Dragonbox in C++
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python