DXR is a code search and navigation tool aimed at making sense of large projects. It supports full-text and regex searches as well as structural queries.

Mercurial (409f3966645a)

VCS Links

Line Code
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132
# Rayon

[![Rayon crate](https://img.shields.io/crates/v/rayon.svg)](https://crates.io/crates/rayon)
[![Rayon documentation](https://docs.rs/rayon/badge.svg)](https://docs.rs/rayon)
[![Travis Status](https://travis-ci.org/rayon-rs/rayon.svg?branch=master)](https://travis-ci.org/rayon-rs/rayon)
[![Appveyor status](https://ci.appveyor.com/api/projects/status/wre5dkx08gayy8hc/branch/master?svg=true)](https://ci.appveyor.com/project/cuviper/rayon/branch/master)
[![Join the chat at https://gitter.im/rayon-rs/Lobby](https://badges.gitter.im/rayon-rs/Lobby.svg)](https://gitter.im/rayon-rs/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

Rayon is a data-parallelism library for Rust. It is extremely
lightweight and makes it easy to convert a sequential computation into
a parallel one. It also guarantees data-race freedom. (You may also
enjoy [this blog post][blog] about Rayon, which gives more background
and details about how it works, or [this video][video], from the Rust
Belt Rust conference.) Rayon is
[available on crates.io](https://crates.io/crates/rayon), and
[API Documentation is available on docs.rs](https://docs.rs/rayon/).

[blog]: http://smallcultfollowing.com/babysteps/blog/2015/12/18/rayon-data-parallelism-in-rust/
[video]: https://www.youtube.com/watch?v=gof_OEv71Aw

## Parallel iterators and more

Rayon makes it drop-dead simple to convert sequential iterators into
parallel ones: usually, you just change your `foo.iter()` call into
`foo.par_iter()`, and Rayon does the rest:

use rayon::prelude::*;
fn sum_of_squares(input: &[i32]) -> i32 {
    input.par_iter() // <-- just change that!
         .map(|&i| i * i)

[Parallel iterators] take care of deciding how to divide your data
into tasks; it will dynamically adapt for maximum performance. If you
need more flexibility than that, Rayon also offers the [join] and
[scope] functions, which let you create parallel tasks on your own.
For even more control, you can create [custom threadpools] rather than
using Rayon's default, global threadpool.

[Parallel iterators]: https://docs.rs/rayon/*/rayon/iter/index.html
[join]: https://docs.rs/rayon/*/rayon/fn.join.html
[scope]: https://docs.rs/rayon/*/rayon/fn.scope.html
[custom threadpools]: https://docs.rs/rayon/*/rayon/struct.ThreadPool.html

## No data races

You may have heard that parallel execution can produce all kinds of
crazy bugs. Well, rest easy. Rayon's APIs all guarantee **data-race
freedom**, which generally rules out most parallel bugs (though not
all). In other words, **if your code compiles**, it typically does the
same thing it did before.

For the most, parallel iterators in particular are guaranteed to
produce the same results as their sequential counterparts. One caevat:
If your iterator has side effects (for example, sending methods to
other threads through a [Rust channel] or writing to disk), those side
effects may occur in a different order. Note also that, in some cases,
parallel iterators offer alternative versions of the sequential
iterator methods that can have higher performance.

[Rust channel]: https://doc.rust-lang.org/std/sync/mpsc/fn.channel.html

## Using Rayon

[Rayon is available on crates.io](https://crates.io/crates/rayon). The
recommended way to use it is to add a line into your Cargo.toml such

rayon = "1.0"

and then add the following to to your `lib.rs`:

extern crate rayon;

To use the Parallel Iterator APIs, a number of traits have to be in
scope. The easiest way to bring those things into scope is to use the
[Rayon prelude](https://docs.rs/rayon/*/rayon/prelude/index.html).  In
each module where you would like to use the parallel iterator APIs,
just add:

use rayon::prelude::*;

Rayon currently requires `rustc 1.13.0` or greater.

## Contribution

Rayon is an open source project! If you'd like to contribute to Rayon, check out [the list of "help wanted" issues](https://github.com/rayon-rs/rayon/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22). These are all (or should be) issues that are suitable for getting started, and they generally include a detailed set of instructions for what to do. Please ask questions if anything is unclear! Also, check out the [Guide to Development](https://github.com/rayon-rs/rayon/wiki/Guide-to-Development) page on the wiki. Note that all code submitted in PRs to Rayon is assumed to [be licensed under Rayon's dual MIT/Apache2 licensing](https://github.com/rayon-rs/rayon/blob/master/README.md#license).

## Quick demo

To see Rayon in action, check out the `rayon-demo` directory, which
includes a number of demos of code using Rayon. For example, run this
command to get a visualization of an nbody simulation. To see the
effect of using Rayon, press `s` to run sequentially and `p` to run in

> cd rayon-demo
> cargo +nightly run --release -- nbody visualize

For more information on demos, try:

> cd rayon-demo
> cargo +nightly run --release -- --help

**Note:** While Rayon is usable as a library with the stable compiler, running demos or executing tests requires nightly Rust.

## Other questions?

See [the Rayon FAQ][faq].

[faq]: https://github.com/rayon-rs/rayon/blob/master/FAQ.md

## License

Rayon is distributed under the terms of both the MIT license and the
Apache License (Version 2.0). See [LICENSE-APACHE](LICENSE-APACHE) and
[LICENSE-MIT](LICENSE-MIT) for details. Opening a pull requests is
assumed to signal agreement with these licensing terms.