Crate rayon [−] [src]
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.
- The
par_sort
method sorts&mut [T]
slices (or vectors) in parallel. par_extend
can be used to efficiently grow collections with items produced by a parallel iterator.
- Custom tasks let you divide your work into parallel tasks yourself.
join
is used to subdivide a task into two pieces.scope
creates a scope within which you can create any number of parallel tasks.ThreadPoolBuilder
can 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 Cargo.toml
and put
extern crate rayon
in your main file (lib.rs
, main.rs
).
Next, to use parallel iterators or the other high-level methods,
you need to import several traits. Those traits are bundled into
the module [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 map
,
for_each
, filter
, fold
, and more.
Crate Layout
Rayon extends many of the types found in the standard library with
parallel iterator implementations. The modules in the rayon
crate mirror 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
the collections
from std
. You will rarely need to access
these submodules unless you need to name iterator types
explicitly.
Other questions?
See the Rayon FAQ.
Modules
collections |
Parallel iterator types for standard collections |
iter |
Traits for writing parallel programs using an iterator-style interface |
option |
Parallel iterator types for options |
prelude |
The rayon prelude imports the various |
range |
Parallel iterator types for ranges,
the type for values created by |
result |
Parallel iterator types for results |
slice |
Parallel iterator types for slices |
str |
Parallel iterator types for strings |
vec |
Parallel iterator types for vectors ( |
Structs
FnContext |
Provides the calling context to a closure called by |
Scope |
Represents a fork-join scope which can be used to spawn any number of tasks. See |
ThreadPool |
Represents a user created thread-pool. |
ThreadPoolBuildError |
Error when initializing a thread pool. |
ThreadPoolBuilder |
Used to create a new |
Functions
current_num_threads |
Returns the number of threads in the current registry. If this code is executing within a Rayon thread-pool, then this will be the number of threads for the thread-pool of the current thread. Otherwise, it will be the number of threads for the global thread-pool. |
join |
Takes two closures and potentially runs them in parallel. It returns a pair of the results from those closures. |
join_context |
Identical to |
scope |
Create a "fork-join" scope |
spawn |
Fires off a task into the Rayon threadpool in the "static" or
"global" scope. Just like a standard thread, this task is not
tied to the current stack frame, and hence it cannot hold any
references other than those with |