Parallel Tasks

Mutate the elements of an array in parallel

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The example uses the rayon crate, which is a data parallelism library for Rust. rayon provides the par_iter_mut method for any parallel iterable data type. This is an iterator-like chain that potentially executes in parallel.

extern crate rayon;

use rayon::prelude::*;

fn main() {
    let mut arr = [0, 7, 9, 11];
    arr.par_iter_mut().for_each(|p| *p -= 1);
    println!("{:?}", arr);

Test in parallel if any or all elements of a collection match a given predicate

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This example demonstrates using the rayon::any and rayon::all methods, which are parallelized counterparts to std::any and std::all. rayon::any checks in parallel whether any element of the iterator matches the predicate, and returns as soon as one is found. rayon::all checks in parallel whether all elements of the iterator match the predicate, and returns as soon as a non-matching element is found.

extern crate rayon;

use rayon::prelude::*;

fn main() {
    let mut vec = vec![2, 4, 6, 8];

    assert!(!vec.par_iter().any(|n| (*n % 2) != 0));
    assert!(vec.par_iter().all(|n| (*n % 2) == 0));
    assert!(!vec.par_iter().any(|n| *n > 8 ));
    assert!(vec.par_iter().all(|n| *n <= 8 ));


    assert!(vec.par_iter().any(|n| (*n % 2) != 0));
    assert!(!vec.par_iter().all(|n| (*n % 2) == 0));
    assert!(vec.par_iter().any(|n| *n > 8 ));
    assert!(!vec.par_iter().all(|n| *n <= 8 )); 

Search items using given predicate in parallel

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This example uses rayon::find_any and par_iter to search a vector in parallel for an element satisfying the predicate in the given closure.

If there are multiple elements satisfying the predicate defined in the closure argument of rayon::find_any, rayon returns the first one found, not necessarily the first one.

Also note that the argument to the closure is a reference to a reference (&&x). See the discussion on std::find for additional details.

extern crate rayon;

use rayon::prelude::*;

fn main() {
    let v = vec![6, 2, 1, 9, 3, 8, 11];

    let f1 = v.par_iter().find_any(|&&x| x == 9);
    let f2 = v.par_iter().find_any(|&&x| x % 2 == 0 && x > 6);
    let f3 = v.par_iter().find_any(|&&x| x > 8);

    assert_eq!(f1, Some(&9));
    assert_eq!(f2, Some(&8));
    assert!(f3 > Some(&8));

Sort a vector in parallel

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This example will sort in parallel a vector of Strings.

Allocate a vector of empty Strings. par_iter_mut().for_each populates random values in parallel. Although multiple options exist to sort an enumerable data type, par_sort_unstable is usually faster than stable sorting algorithms.

extern crate rand;
extern crate rayon;

use rand::{Rng, thread_rng};
use rand::distributions::Alphanumeric;
use rayon::prelude::*;

fn main() {
  let mut vec = vec![String::new(); 100_000];
  vec.par_iter_mut().for_each(|p| {
    let mut rng = thread_rng();
    *p = (0..5).map(|_| rng.sample(&Alphanumeric)).collect()

Map-reduce in parallel

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This example uses rayon::filter, rayon::map, and rayon::reduce to calculate the average age of Person objects whose age is over 30.

rayon::filter returns elements from a collection that satisfy the given predicate. rayon::map performs an operation on every element, creating a new iteration, and rayon::reduce performs an operation given the previous reduction and the current element. Also shows use of rayon::sum, which has the same result as the reduce operation in this example.

extern crate rayon;

use rayon::prelude::*;

struct Person {
    age: u32,

fn main() {
    let v: Vec<Person> = vec![
        Person { age: 23 },
        Person { age: 19 },
        Person { age: 42 },
        Person { age: 17 },
        Person { age: 17 },
        Person { age: 31 },
        Person { age: 30 },

    let num_over_30 = v.par_iter().filter(|&x| x.age > 30).count() as f32;
    let sum_over_30 = v.par_iter()
        .map(|x| x.age)
        .filter(|&x| x > 30)
        .reduce(|| 0, |x, y| x + y);

    let alt_sum_30: u32 = v.par_iter()
        .map(|x| x.age)
        .filter(|&x| x > 30)

    let avg_over_30 = sum_over_30 as f32 / num_over_30;
    let alt_avg_over_30 = alt_sum_30 as f32/ num_over_30;

    assert!((avg_over_30 - alt_avg_over_30).abs() < std::f32::EPSILON);
    println!("The average age of people older than 30 is {}", avg_over_30);

Generate jpg thumbnails in parallel

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This example generates thumbnails for all .jpg files in the current directory then saves them in a new folder called thumbnails.

glob::glob_with finds jpeg files in current directory. rayon resizes images in parallel using par_iter calling DynamicImage::resize.

# #[macro_use]
# extern crate error_chain;
extern crate glob;
extern crate image;
extern crate rayon;

use std::path::Path;
use std::fs::create_dir_all;

# use error_chain::ChainedError;
use glob::{glob_with, MatchOptions};
use image::{FilterType, ImageError};
use rayon::prelude::*;

# error_chain! {
#     foreign_links {
#         Image(ImageError);
#         Io(std::io::Error);
#         Glob(glob::PatternError);
#     }
# }

fn run() -> Result<()> {
    let options: MatchOptions = Default::default();
    let files: Vec<_> = glob_with("*.jpg", &options)?
        .filter_map(|x| x.ok())

    if files.len() == 0 {
        bail!("No .jpg files found in current directory");

    let thumb_dir = "thumbnails";

    println!("Saving {} thumbnails into '{}'...", files.len(), thumb_dir);

    let image_failures: Vec<_> = files
        .map(|path| {
            make_thumbnail(path, thumb_dir, 300)
                .map_err(|e| e.chain_err(|| path.display().to_string()))
        .filter_map(|x| x.err())

    image_failures.iter().for_each(|x| println!("{}", x.display_chain()));

    println!("{} thumbnails saved successfully", files.len() - image_failures.len());

fn make_thumbnail<PA, PB>(original: PA, thumb_dir: PB, longest_edge: u32) -> Result<()>
    PA: AsRef<Path>,
    PB: AsRef<Path>,
    let img = image::open(original.as_ref())?;
    let file_path = thumb_dir.as_ref().join(original);

    Ok(img.resize(longest_edge, longest_edge, FilterType::Nearest)
# quick_main!(run);