Rust has built-in support for parallelism, which is the ability to execute multiple tasks simultaneously on multiple processors or cores.
Rust's support for parallelism is especially easy to use by adding the Rust rayon crate, which provides a high-level API for parallel programming. The rayon crate allows developers to easily parallelize data processing tasks, such as iterating over large collections, by abstracting away the low-level details of thread creation and synchronization.
Here is an example code snippet that demonstrates Rust parallelism using rayon:
use rayon::prelude::*;
fn main() {
let numbers = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
let sum = numbers.par_iter().sum::<i32>();
println!("Sum is {}", sum);
}In this example, the par_iter() method creates a parallel iterator over a vector of numbers. The sum() method is then called on the iterator to calculate the sum of all the numbers in the vector.
rayon automatically divides the work among multiple threads, using as many threads as there are processors or cores available on the system. The code is executed in parallel, with each thread processing a subset of the data.
The par_iter() method can be used with many other methods of the standard library, such as map(), filter(), and reduce(), to parallelize various data processing tasks.