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Parallel calculations: Difference between revisions
Add Rust implementation
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(Add Rust implementation) |
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In the future hyper operators, junctions and feeds will be candidates for autothreading.
=={{header|Rust}}==
<lang rust>
//! This solution uses [rayon](https://github.com/rayon-rs/rayon), a data-parallelism library.
//! Since Rust guarantees that a program has no data races, adding parallelism to a sequential
//! computation is as easy as importing the rayon traits and calling the `par_iter()` method.
extern crate rayon;
extern crate prime_decomposition;
use rayon::prelude::*;
/// Returns the largest minimal factor of the numbers in a slice
pub fn largest_min_factor(numbers: &[usize]) -> usize {
numbers
.par_iter()
.map(|n| {
// `factor` returns a sorted vector, so we just take the first element.
prime_decomposition::factor(*n)[0]
})
.max()
.unwrap()
}
fn main() {
let numbers = &[
1_122_725, 1_125_827, 1_122_725, 1_152_800, 1_157_978, 1_099_726,
];
let max = largest_min_factor(numbers);
println!("The largest minimal factor is {}", max);
}
</lang>
{{out}}
<pre>
The largest minimal factor is 23
</pre>
=={{header|SequenceL}}==
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