9 unstable releases
|0.5.0||Mar 24, 2023|
|0.4.0||May 27, 2022|
|0.3.2||Jan 10, 2022|
|0.3.1||Mar 11, 2021|
|0.1.1||Jun 6, 2018|
#52 in Concurrency
13,738 downloads per month
Used in 59 crates (6 directly)
rustc-rayon is a fork of the Rayon crate. It adds a few "in progress" features that rustc is using, mostly around deadlock detection. These features are not stable and should not be used by others -- though they may find their way into rayon proper at some point. In general, if you are not rustc, you should be using the real rayon crate, not rustc-rayon. =)
rustc-rayon is a fork of rayon. rayon is distributed under the terms of both the MIT license and the Apache License (Version 2.0). See LICENSE-APACHE and LICENSE-MIT for details. Opening a pull request is assumed to signal agreement with these licensing terms.
Data-parallelism library that makes it easy to convert sequential computations into parallel
Rayon is lightweight and convenient for introducing parallelism into existing code. It guarantees data-race free executions and takes advantage of parallelism when sensible, based on work-load at runtime.
How to use Rayon
There are two ways to use Rayon:
- High-level parallel constructs are the simplest way to use Rayon and also
typically the most efficient.
- Parallel iterators make it easy to convert a sequential iterator to
execute in parallel.
ParallelIteratortrait defines general methods for all parallel iterators.
IndexedParallelIteratortrait adds methods for iterators that support random access.
&mut [T]slices (or vectors) in parallel.
par_extendcan be used to efficiently grow collections with items produced by a parallel iterator.
- Parallel iterators make it easy to convert a sequential iterator to execute in parallel.
- Custom tasks let you divide your work into parallel tasks yourself.
joinis used to subdivide a task into two pieces.
scopecreates a scope within which you can create any number of parallel tasks.
ThreadPoolBuildercan be used to create your own thread pools or customize the global one.
Basic usage and the Rayon prelude
First, you will need to add
rayon to your
Next, to use parallel iterators or the other high-level methods,
you need to import several traits. Those traits are bundled into
rayon::prelude. It is recommended that you import
all of these traits at once by adding
use rayon::prelude::* at
the top of each module that uses Rayon methods.
These traits give you access to the
par_iter method which provides
parallel implementations of many iterative functions such as
fold, and more.
Rayon extends many of the types found in the standard library with
parallel iterator implementations. The modules in the
std itself: so, e.g., the
option module in
Rayon contains parallel iterators for the
Option type, which is
found in the
option module of
std. Similarly, the
collections module in Rayon offers parallel iterator types for
std. You will rarely need to access
these submodules unless you need to name iterator types
Targets without threading
Rayon has limited support for targets without
std threading implementations.
See the [
rayon_core] documentation for more information about its global fallback.
See the Rayon FAQ.