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.
There are two ways to use Rayon:
- High-level parallel constructs are the simplest way to use Rayon and also typically the most efficient.
- Custom tasks let you divide your work into parallel tasks yourself.
First, you will need to add
rayon to your
Cargo.toml and put
extern crate rayon in your main file (
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.
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
See the Rayon FAQ.
Parallel iterator types for standard collections
Traits for writing parallel programs using an iterator-style interface
Parallel iterator types for options
The rayon prelude imports the various
Parallel iterator types for ranges,
the type for values created by
Parallel iterator types for results
Parallel iterator types for slices
Parallel iterator types for strings
Parallel iterator types for vectors (
Provides the calling context to a closure called by
Represents a fork-join scope which can be used to spawn any number of tasks. See
Represents a user created thread-pool.
Error when initializing a thread pool.
Used to create a new
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.
Takes two closures and potentially runs them in parallel. It returns a pair of the results from those closures.
Create a "fork-join" scope
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