Parallelization features for ndarray.

This crate is deprecated and was replaced by equivalent rayon support directly integrated to ndarray.

The array views and references to owned arrays all implement NdarrayIntoParallelIterator (*); the default parallel iterators (each element by reference or mutable reference) have no ordering guarantee in their parallel implementations.

.axis_iter() and .axis_iter_mut() also have parallel counterparts, and their parallel iterators are indexed (and thus ordered) and exact length.

Zip also implements NdarrayIntoParallelIterator, and there is an extension trait so that it can use a method .par_apply directly.

(*) This regime of a custom trait instead of rayon’s own is since we use this intermediate ndarray-parallel crate.

Examples

Arrays and array views

Compute the exponential of each element in an array, parallelized.

extern crate ndarray;
extern crate ndarray_parallel;

use ndarray::Array2;
use ndarray_parallel::prelude::*;

fn main() {
    let mut a = Array2::<f64>::zeros((128, 128));

    // Parallel versions of regular array methods (ParMap trait)
    a.par_map_inplace(|x| *x = x.exp());
    a.par_mapv_inplace(f64::exp);

    // You can also use the parallel iterator directly
    a.par_iter_mut().for_each(|x| *x = x.exp());
}

Axis iterators

Use the parallel .axis_iter() to compute the sum of each row.

extern crate ndarray;
extern crate ndarray_parallel;

use ndarray::Array;
use ndarray::Axis;
use ndarray_parallel::prelude::*;

fn main() {
    let a = Array::linspace(0., 63., 64).into_shape((4, 16)).unwrap();
    let mut sums = Vec::new();
    a.axis_iter(Axis(0))
     .into_par_iter()
     .map(|row| row.scalar_sum())
     .collect_into_vec(&mut sums);

    assert_eq!(sums, [120., 376., 632., 888.]);
}

Zip

Use zip for lock step function application across several arrays

extern crate ndarray;
extern crate ndarray_parallel;

use ndarray::Array3;
use ndarray::Zip;
use ndarray_parallel::prelude::*;

type Array3f64 = Array3<f64>;

fn main() {
    const N: usize = 128;
    let a = Array3f64::from_elem((N, N, N), 1.);
    let b = Array3f64::from_elem(a.dim(), 2.);
    let mut c = Array3f64::zeros(a.dim());

    Zip::from(&mut c)
        .and(&a)
        .and(&b)
        .par_apply(|c, &a, &b| {
            *c += a - b;
        });
}