Revision control
Copy as Markdown
Other Tools
//! Parallel iterator types for [slices][std::slice]
//!
//! You will rarely need to interact with this module directly unless you need
//! to name one of the iterator types.
//!
mod chunk_by;
mod chunks;
mod mergesort;
mod quicksort;
mod rchunks;
mod test;
use self::mergesort::par_mergesort;
use self::quicksort::par_quicksort;
use crate::iter::plumbing::*;
use crate::iter::*;
use crate::split_producer::*;
use std::cmp::Ordering;
use std::fmt::{self, Debug};
use std::mem;
pub use self::chunk_by::{ChunkBy, ChunkByMut};
pub use self::chunks::{Chunks, ChunksExact, ChunksExactMut, ChunksMut};
pub use self::rchunks::{RChunks, RChunksExact, RChunksExactMut, RChunksMut};
/// Parallel extensions for slices.
pub trait ParallelSlice<T: Sync> {
/// Returns a plain slice, which is used to implement the rest of the
/// parallel methods.
fn as_parallel_slice(&self) -> &[T];
/// Returns a parallel iterator over subslices separated by elements that
/// match the separator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let products: Vec<_> = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9]
/// .par_split(|i| *i == 0)
/// .map(|numbers| numbers.iter().product::<i32>())
/// .collect();
/// assert_eq!(products, [6, 64, 162]);
/// ```
fn par_split<P>(&self, separator: P) -> Split<'_, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
{
Split {
slice: self.as_parallel_slice(),
separator,
}
}
/// Returns a parallel iterator over subslices separated by elements that
/// match the separator, including the matched part as a terminator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let lengths: Vec<_> = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9]
/// .par_split_inclusive(|i| *i == 0)
/// .map(|numbers| numbers.len())
/// .collect();
/// assert_eq!(lengths, [4, 4, 3]);
/// ```
fn par_split_inclusive<P>(&self, separator: P) -> SplitInclusive<'_, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
{
SplitInclusive {
slice: self.as_parallel_slice(),
separator,
}
}
/// Returns a parallel iterator over all contiguous windows of length
/// `window_size`. The windows overlap.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let windows: Vec<_> = [1, 2, 3].par_windows(2).collect();
/// assert_eq!(vec![[1, 2], [2, 3]], windows);
/// ```
fn par_windows(&self, window_size: usize) -> Windows<'_, T> {
Windows {
window_size,
slice: self.as_parallel_slice(),
}
}
/// Returns a parallel iterator over at most `chunk_size` elements of
/// `self` at a time. The chunks do not overlap.
///
/// If the number of elements in the iterator is not divisible by
/// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
/// other chunks will have that exact length.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_chunks(2).collect();
/// assert_eq!(chunks, vec![&[1, 2][..], &[3, 4], &[5]]);
/// ```
#[track_caller]
fn par_chunks(&self, chunk_size: usize) -> Chunks<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
Chunks::new(chunk_size, self.as_parallel_slice())
}
/// Returns a parallel iterator over `chunk_size` elements of
/// `self` at a time. The chunks do not overlap.
///
/// If `chunk_size` does not divide the length of the slice, then the
/// last up to `chunk_size-1` elements will be omitted and can be
/// retrieved from the remainder function of the iterator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_chunks_exact(2).collect();
/// assert_eq!(chunks, vec![&[1, 2][..], &[3, 4]]);
/// ```
#[track_caller]
fn par_chunks_exact(&self, chunk_size: usize) -> ChunksExact<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
ChunksExact::new(chunk_size, self.as_parallel_slice())
}
/// Returns a parallel iterator over at most `chunk_size` elements of `self` at a time,
/// starting at the end. The chunks do not overlap.
///
/// If the number of elements in the iterator is not divisible by
/// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
/// other chunks will have that exact length.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_rchunks(2).collect();
/// assert_eq!(chunks, vec![&[4, 5][..], &[2, 3], &[1]]);
/// ```
#[track_caller]
fn par_rchunks(&self, chunk_size: usize) -> RChunks<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
RChunks::new(chunk_size, self.as_parallel_slice())
}
/// Returns a parallel iterator over `chunk_size` elements of `self` at a time,
/// starting at the end. The chunks do not overlap.
///
/// If `chunk_size` does not divide the length of the slice, then the
/// last up to `chunk_size-1` elements will be omitted and can be
/// retrieved from the remainder function of the iterator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_rchunks_exact(2).collect();
/// assert_eq!(chunks, vec![&[4, 5][..], &[2, 3]]);
/// ```
#[track_caller]
fn par_rchunks_exact(&self, chunk_size: usize) -> RChunksExact<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
RChunksExact::new(chunk_size, self.as_parallel_slice())
}
/// Returns a parallel iterator over the slice producing non-overlapping runs
/// of elements using the predicate to separate them.
///
/// The predicate is called on two elements following themselves,
/// it means the predicate is called on `slice[0]` and `slice[1]`
/// then on `slice[1]` and `slice[2]` and so on.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let chunks: Vec<_> = [1, 2, 2, 3, 3, 3].par_chunk_by(|&x, &y| x == y).collect();
/// assert_eq!(chunks[0], &[1]);
/// assert_eq!(chunks[1], &[2, 2]);
/// assert_eq!(chunks[2], &[3, 3, 3]);
/// ```
fn par_chunk_by<F>(&self, pred: F) -> ChunkBy<'_, T, F>
where
F: Fn(&T, &T) -> bool + Send + Sync,
{
ChunkBy::new(self.as_parallel_slice(), pred)
}
}
impl<T: Sync> ParallelSlice<T> for [T] {
#[inline]
fn as_parallel_slice(&self) -> &[T] {
self
}
}
/// Parallel extensions for mutable slices.
pub trait ParallelSliceMut<T: Send> {
/// Returns a plain mutable slice, which is used to implement the rest of
/// the parallel methods.
fn as_parallel_slice_mut(&mut self) -> &mut [T];
/// Returns a parallel iterator over mutable subslices separated by
/// elements that match the separator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let mut array = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9];
/// array.par_split_mut(|i| *i == 0)
/// .for_each(|slice| slice.reverse());
/// assert_eq!(array, [3, 2, 1, 0, 8, 4, 2, 0, 9, 6, 3]);
/// ```
fn par_split_mut<P>(&mut self, separator: P) -> SplitMut<'_, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
{
SplitMut {
slice: self.as_parallel_slice_mut(),
separator,
}
}
/// Returns a parallel iterator over mutable subslices separated by elements
/// that match the separator, including the matched part as a terminator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let mut array = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9];
/// array.par_split_inclusive_mut(|i| *i == 0)
/// .for_each(|slice| slice.reverse());
/// assert_eq!(array, [0, 3, 2, 1, 0, 8, 4, 2, 9, 6, 3]);
/// ```
fn par_split_inclusive_mut<P>(&mut self, separator: P) -> SplitInclusiveMut<'_, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
{
SplitInclusiveMut {
slice: self.as_parallel_slice_mut(),
separator,
}
}
/// Returns a parallel iterator over at most `chunk_size` elements of
/// `self` at a time. The chunks are mutable and do not overlap.
///
/// If the number of elements in the iterator is not divisible by
/// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
/// other chunks will have that exact length.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let mut array = [1, 2, 3, 4, 5];
/// array.par_chunks_mut(2)
/// .for_each(|slice| slice.reverse());
/// assert_eq!(array, [2, 1, 4, 3, 5]);
/// ```
#[track_caller]
fn par_chunks_mut(&mut self, chunk_size: usize) -> ChunksMut<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
ChunksMut::new(chunk_size, self.as_parallel_slice_mut())
}
/// Returns a parallel iterator over `chunk_size` elements of
/// `self` at a time. The chunks are mutable and do not overlap.
///
/// If `chunk_size` does not divide the length of the slice, then the
/// last up to `chunk_size-1` elements will be omitted and can be
/// retrieved from the remainder function of the iterator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let mut array = [1, 2, 3, 4, 5];
/// array.par_chunks_exact_mut(3)
/// .for_each(|slice| slice.reverse());
/// assert_eq!(array, [3, 2, 1, 4, 5]);
/// ```
#[track_caller]
fn par_chunks_exact_mut(&mut self, chunk_size: usize) -> ChunksExactMut<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
ChunksExactMut::new(chunk_size, self.as_parallel_slice_mut())
}
/// Returns a parallel iterator over at most `chunk_size` elements of `self` at a time,
/// starting at the end. The chunks are mutable and do not overlap.
///
/// If the number of elements in the iterator is not divisible by
/// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
/// other chunks will have that exact length.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let mut array = [1, 2, 3, 4, 5];
/// array.par_rchunks_mut(2)
/// .for_each(|slice| slice.reverse());
/// assert_eq!(array, [1, 3, 2, 5, 4]);
/// ```
#[track_caller]
fn par_rchunks_mut(&mut self, chunk_size: usize) -> RChunksMut<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
RChunksMut::new(chunk_size, self.as_parallel_slice_mut())
}
/// Returns a parallel iterator over `chunk_size` elements of `self` at a time,
/// starting at the end. The chunks are mutable and do not overlap.
///
/// If `chunk_size` does not divide the length of the slice, then the
/// last up to `chunk_size-1` elements will be omitted and can be
/// retrieved from the remainder function of the iterator.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let mut array = [1, 2, 3, 4, 5];
/// array.par_rchunks_exact_mut(3)
/// .for_each(|slice| slice.reverse());
/// assert_eq!(array, [1, 2, 5, 4, 3]);
/// ```
#[track_caller]
fn par_rchunks_exact_mut(&mut self, chunk_size: usize) -> RChunksExactMut<'_, T> {
assert!(chunk_size != 0, "chunk_size must not be zero");
RChunksExactMut::new(chunk_size, self.as_parallel_slice_mut())
}
/// Sorts the slice in parallel.
///
/// This sort is stable (i.e., does not reorder equal elements) and *O*(*n* \* log(*n*)) worst-case.
///
/// When applicable, unstable sorting is preferred because it is generally faster than stable
/// sorting and it doesn't allocate auxiliary memory.
/// See [`par_sort_unstable`](#method.par_sort_unstable).
///
/// # Current implementation
///
/// The current algorithm is an adaptive merge sort inspired by
/// It is designed to be very fast in cases where the slice is nearly sorted, or consists of
/// two or more sorted sequences concatenated one after another.
///
/// Also, it allocates temporary storage the same size as `self`, but for very short slices a
/// non-allocating insertion sort is used instead.
///
/// In order to sort the slice in parallel, the slice is first divided into smaller chunks and
/// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending
/// or descending runs are concatenated. Finally, the remaining chunks are merged together using
/// parallel subdivision of chunks and parallel merge operation.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
///
/// let mut v = [-5, 4, 1, -3, 2];
///
/// v.par_sort();
/// assert_eq!(v, [-5, -3, 1, 2, 4]);
/// ```
fn par_sort(&mut self)
where
T: Ord,
{
par_mergesort(self.as_parallel_slice_mut(), T::lt);
}
/// Sorts the slice in parallel with a comparator function.
///
/// This sort is stable (i.e., does not reorder equal elements) and *O*(*n* \* log(*n*)) worst-case.
///
/// The comparator function must define a total ordering for the elements in the slice. If
/// the ordering is not total, the order of the elements is unspecified. An order is a
/// total order if it is (for all `a`, `b` and `c`):
///
/// * total and antisymmetric: exactly one of `a < b`, `a == b` or `a > b` is true, and
/// * transitive, `a < b` and `b < c` implies `a < c`. The same must hold for both `==` and `>`.
///
/// For example, while [`f64`] doesn't implement [`Ord`] because `NaN != NaN`, we can use
/// `partial_cmp` as our sort function when we know the slice doesn't contain a `NaN`.
///
/// ```
/// use rayon::prelude::*;
///
/// let mut floats = [5f64, 4.0, 1.0, 3.0, 2.0];
/// floats.par_sort_by(|a, b| a.partial_cmp(b).unwrap());
/// assert_eq!(floats, [1.0, 2.0, 3.0, 4.0, 5.0]);
/// ```
///
/// When applicable, unstable sorting is preferred because it is generally faster than stable
/// sorting and it doesn't allocate auxiliary memory.
/// See [`par_sort_unstable_by`](#method.par_sort_unstable_by).
///
/// # Current implementation
///
/// The current algorithm is an adaptive merge sort inspired by
/// It is designed to be very fast in cases where the slice is nearly sorted, or consists of
/// two or more sorted sequences concatenated one after another.
///
/// Also, it allocates temporary storage the same size as `self`, but for very short slices a
/// non-allocating insertion sort is used instead.
///
/// In order to sort the slice in parallel, the slice is first divided into smaller chunks and
/// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending
/// or descending runs are concatenated. Finally, the remaining chunks are merged together using
/// parallel subdivision of chunks and parallel merge operation.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
///
/// let mut v = [5, 4, 1, 3, 2];
/// v.par_sort_by(|a, b| a.cmp(b));
/// assert_eq!(v, [1, 2, 3, 4, 5]);
///
/// // reverse sorting
/// v.par_sort_by(|a, b| b.cmp(a));
/// assert_eq!(v, [5, 4, 3, 2, 1]);
/// ```
fn par_sort_by<F>(&mut self, compare: F)
where
F: Fn(&T, &T) -> Ordering + Sync,
{
par_mergesort(self.as_parallel_slice_mut(), |a, b| {
compare(a, b) == Ordering::Less
});
}
/// Sorts the slice in parallel with a key extraction function.
///
/// This sort is stable (i.e., does not reorder equal elements) and *O*(*m* \* *n* \* log(*n*))
/// worst-case, where the key function is *O*(*m*).
///
/// For expensive key functions (e.g. functions that are not simple property accesses or
/// basic operations), [`par_sort_by_cached_key`](#method.par_sort_by_cached_key) is likely to
/// be significantly faster, as it does not recompute element keys.
///
/// When applicable, unstable sorting is preferred because it is generally faster than stable
/// sorting and it doesn't allocate auxiliary memory.
/// See [`par_sort_unstable_by_key`](#method.par_sort_unstable_by_key).
///
/// # Current implementation
///
/// The current algorithm is an adaptive merge sort inspired by
/// It is designed to be very fast in cases where the slice is nearly sorted, or consists of
/// two or more sorted sequences concatenated one after another.
///
/// Also, it allocates temporary storage the same size as `self`, but for very short slices a
/// non-allocating insertion sort is used instead.
///
/// In order to sort the slice in parallel, the slice is first divided into smaller chunks and
/// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending
/// or descending runs are concatenated. Finally, the remaining chunks are merged together using
/// parallel subdivision of chunks and parallel merge operation.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
///
/// let mut v = [-5i32, 4, 1, -3, 2];
///
/// v.par_sort_by_key(|k| k.abs());
/// assert_eq!(v, [1, 2, -3, 4, -5]);
/// ```
fn par_sort_by_key<K, F>(&mut self, f: F)
where
K: Ord,
F: Fn(&T) -> K + Sync,
{
par_mergesort(self.as_parallel_slice_mut(), |a, b| f(a).lt(&f(b)));
}
/// Sorts the slice in parallel with a key extraction function.
///
/// During sorting, the key function is called at most once per element, by using
/// temporary storage to remember the results of key evaluation.
/// The key function is called in parallel, so the order of calls is completely unspecified.
///
/// This sort is stable (i.e., does not reorder equal elements) and *O*(*m* \* *n* + *n* \* log(*n*))
/// worst-case, where the key function is *O*(*m*).
///
/// For simple key functions (e.g., functions that are property accesses or
/// basic operations), [`par_sort_by_key`](#method.par_sort_by_key) is likely to be
/// faster.
///
/// # Current implementation
///
/// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
/// which combines the fast average case of randomized quicksort with the fast worst case of
/// heapsort, while achieving linear time on slices with certain patterns. It uses some
/// randomization to avoid degenerate cases, but with a fixed seed to always provide
/// deterministic behavior.
///
/// In the worst case, the algorithm allocates temporary storage in a `Vec<(K, usize)>` the
/// length of the slice.
///
/// All quicksorts work in two stages: partitioning into two halves followed by recursive
/// calls. The partitioning phase is sequential, but the two recursive calls are performed in
/// parallel. Finally, after sorting the cached keys, the item positions are updated sequentially.
///
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
///
/// let mut v = [-5i32, 4, 32, -3, 2];
///
/// v.par_sort_by_cached_key(|k| k.to_string());
/// assert!(v == [-3, -5, 2, 32, 4]);
/// ```
fn par_sort_by_cached_key<K, F>(&mut self, f: F)
where
F: Fn(&T) -> K + Sync,
K: Ord + Send,
{
let slice = self.as_parallel_slice_mut();
let len = slice.len();
if len < 2 {
return;
}
// Helper macro for indexing our vector by the smallest possible type, to reduce allocation.
macro_rules! sort_by_key {
($t:ty) => {{
let mut indices: Vec<_> = slice
.par_iter_mut()
.enumerate()
.map(|(i, x)| (f(&*x), i as $t))
.collect();
// The elements of `indices` are unique, as they are indexed, so any sort will be
// stable with respect to the original slice. We use `sort_unstable` here because
// it requires less memory allocation.
indices.par_sort_unstable();
for i in 0..len {
let mut index = indices[i].1;
while (index as usize) < i {
index = indices[index as usize].1;
}
indices[i].1 = index;
slice.swap(i, index as usize);
}
}};
}
let sz_u8 = mem::size_of::<(K, u8)>();
let sz_u16 = mem::size_of::<(K, u16)>();
let sz_u32 = mem::size_of::<(K, u32)>();
let sz_usize = mem::size_of::<(K, usize)>();
if sz_u8 < sz_u16 && len <= (std::u8::MAX as usize) {
return sort_by_key!(u8);
}
if sz_u16 < sz_u32 && len <= (std::u16::MAX as usize) {
return sort_by_key!(u16);
}
if sz_u32 < sz_usize && len <= (std::u32::MAX as usize) {
return sort_by_key!(u32);
}
sort_by_key!(usize)
}
/// Sorts the slice in parallel, but might not preserve the order of equal elements.
///
/// This sort is unstable (i.e., may reorder equal elements), in-place
/// (i.e., does not allocate), and *O*(*n* \* log(*n*)) worst-case.
///
/// # Current implementation
///
/// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
/// which combines the fast average case of randomized quicksort with the fast worst case of
/// heapsort, while achieving linear time on slices with certain patterns. It uses some
/// randomization to avoid degenerate cases, but with a fixed seed to always provide
/// deterministic behavior.
///
/// It is typically faster than stable sorting, except in a few special cases, e.g., when the
/// slice consists of several concatenated sorted sequences.
///
/// All quicksorts work in two stages: partitioning into two halves followed by recursive
/// calls. The partitioning phase is sequential, but the two recursive calls are performed in
/// parallel.
///
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
///
/// let mut v = [-5, 4, 1, -3, 2];
///
/// v.par_sort_unstable();
/// assert_eq!(v, [-5, -3, 1, 2, 4]);
/// ```
fn par_sort_unstable(&mut self)
where
T: Ord,
{
par_quicksort(self.as_parallel_slice_mut(), T::lt);
}
/// Sorts the slice in parallel with a comparator function, but might not preserve the order of
/// equal elements.
///
/// This sort is unstable (i.e., may reorder equal elements), in-place
/// (i.e., does not allocate), and *O*(*n* \* log(*n*)) worst-case.
///
/// The comparator function must define a total ordering for the elements in the slice. If
/// the ordering is not total, the order of the elements is unspecified. An order is a
/// total order if it is (for all `a`, `b` and `c`):
///
/// * total and antisymmetric: exactly one of `a < b`, `a == b` or `a > b` is true, and
/// * transitive, `a < b` and `b < c` implies `a < c`. The same must hold for both `==` and `>`.
///
/// For example, while [`f64`] doesn't implement [`Ord`] because `NaN != NaN`, we can use
/// `partial_cmp` as our sort function when we know the slice doesn't contain a `NaN`.
///
/// ```
/// use rayon::prelude::*;
///
/// let mut floats = [5f64, 4.0, 1.0, 3.0, 2.0];
/// floats.par_sort_unstable_by(|a, b| a.partial_cmp(b).unwrap());
/// assert_eq!(floats, [1.0, 2.0, 3.0, 4.0, 5.0]);
/// ```
///
/// # Current implementation
///
/// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
/// which combines the fast average case of randomized quicksort with the fast worst case of
/// heapsort, while achieving linear time on slices with certain patterns. It uses some
/// randomization to avoid degenerate cases, but with a fixed seed to always provide
/// deterministic behavior.
///
/// It is typically faster than stable sorting, except in a few special cases, e.g., when the
/// slice consists of several concatenated sorted sequences.
///
/// All quicksorts work in two stages: partitioning into two halves followed by recursive
/// calls. The partitioning phase is sequential, but the two recursive calls are performed in
/// parallel.
///
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
///
/// let mut v = [5, 4, 1, 3, 2];
/// v.par_sort_unstable_by(|a, b| a.cmp(b));
/// assert_eq!(v, [1, 2, 3, 4, 5]);
///
/// // reverse sorting
/// v.par_sort_unstable_by(|a, b| b.cmp(a));
/// assert_eq!(v, [5, 4, 3, 2, 1]);
/// ```
fn par_sort_unstable_by<F>(&mut self, compare: F)
where
F: Fn(&T, &T) -> Ordering + Sync,
{
par_quicksort(self.as_parallel_slice_mut(), |a, b| {
compare(a, b) == Ordering::Less
});
}
/// Sorts the slice in parallel with a key extraction function, but might not preserve the order
/// of equal elements.
///
/// This sort is unstable (i.e., may reorder equal elements), in-place
/// (i.e., does not allocate), and *O*(m \* *n* \* log(*n*)) worst-case,
/// where the key function is *O*(*m*).
///
/// # Current implementation
///
/// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
/// which combines the fast average case of randomized quicksort with the fast worst case of
/// heapsort, while achieving linear time on slices with certain patterns. It uses some
/// randomization to avoid degenerate cases, but with a fixed seed to always provide
/// deterministic behavior.
///
/// Due to its key calling strategy, `par_sort_unstable_by_key` is likely to be slower than
/// [`par_sort_by_cached_key`](#method.par_sort_by_cached_key) in cases where the key function
/// is expensive.
///
/// All quicksorts work in two stages: partitioning into two halves followed by recursive
/// calls. The partitioning phase is sequential, but the two recursive calls are performed in
/// parallel.
///
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
///
/// let mut v = [-5i32, 4, 1, -3, 2];
///
/// v.par_sort_unstable_by_key(|k| k.abs());
/// assert_eq!(v, [1, 2, -3, 4, -5]);
/// ```
fn par_sort_unstable_by_key<K, F>(&mut self, f: F)
where
K: Ord,
F: Fn(&T) -> K + Sync,
{
par_quicksort(self.as_parallel_slice_mut(), |a, b| f(a).lt(&f(b)));
}
/// Returns a parallel iterator over the slice producing non-overlapping mutable
/// runs of elements using the predicate to separate them.
///
/// The predicate is called on two elements following themselves,
/// it means the predicate is called on `slice[0]` and `slice[1]`
/// then on `slice[1]` and `slice[2]` and so on.
///
/// # Examples
///
/// ```
/// use rayon::prelude::*;
/// let mut xs = [1, 2, 2, 3, 3, 3];
/// let chunks: Vec<_> = xs.par_chunk_by_mut(|&x, &y| x == y).collect();
/// assert_eq!(chunks[0], &mut [1]);
/// assert_eq!(chunks[1], &mut [2, 2]);
/// assert_eq!(chunks[2], &mut [3, 3, 3]);
/// ```
fn par_chunk_by_mut<F>(&mut self, pred: F) -> ChunkByMut<'_, T, F>
where
F: Fn(&T, &T) -> bool + Send + Sync,
{
ChunkByMut::new(self.as_parallel_slice_mut(), pred)
}
}
impl<T: Send> ParallelSliceMut<T> for [T] {
#[inline]
fn as_parallel_slice_mut(&mut self) -> &mut [T] {
self
}
}
impl<'data, T: Sync + 'data> IntoParallelIterator for &'data [T] {
type Item = &'data T;
type Iter = Iter<'data, T>;
fn into_par_iter(self) -> Self::Iter {
Iter { slice: self }
}
}
impl<'data, T: Send + 'data> IntoParallelIterator for &'data mut [T] {
type Item = &'data mut T;
type Iter = IterMut<'data, T>;
fn into_par_iter(self) -> Self::Iter {
IterMut { slice: self }
}
}
/// Parallel iterator over immutable items in a slice
#[derive(Debug)]
pub struct Iter<'data, T: Sync> {
slice: &'data [T],
}
impl<'data, T: Sync> Clone for Iter<'data, T> {
fn clone(&self) -> Self {
Iter { ..*self }
}
}
impl<'data, T: Sync + 'data> ParallelIterator for Iter<'data, T> {
type Item = &'data T;
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>,
{
bridge(self, consumer)
}
fn opt_len(&self) -> Option<usize> {
Some(self.len())
}
}
impl<'data, T: Sync + 'data> IndexedParallelIterator for Iter<'data, T> {
fn drive<C>(self, consumer: C) -> C::Result
where
C: Consumer<Self::Item>,
{
bridge(self, consumer)
}
fn len(&self) -> usize {
self.slice.len()
}
fn with_producer<CB>(self, callback: CB) -> CB::Output
where
CB: ProducerCallback<Self::Item>,
{
callback.callback(IterProducer { slice: self.slice })
}
}
struct IterProducer<'data, T: Sync> {
slice: &'data [T],
}
impl<'data, T: 'data + Sync> Producer for IterProducer<'data, T> {
type Item = &'data T;
type IntoIter = ::std::slice::Iter<'data, T>;
fn into_iter(self) -> Self::IntoIter {
self.slice.iter()
}
fn split_at(self, index: usize) -> (Self, Self) {
let (left, right) = self.slice.split_at(index);
(IterProducer { slice: left }, IterProducer { slice: right })
}
}
/// Parallel iterator over immutable overlapping windows of a slice
#[derive(Debug)]
pub struct Windows<'data, T: Sync> {
window_size: usize,
slice: &'data [T],
}
impl<'data, T: Sync> Clone for Windows<'data, T> {
fn clone(&self) -> Self {
Windows { ..*self }
}
}
impl<'data, T: Sync + 'data> ParallelIterator for Windows<'data, T> {
type Item = &'data [T];
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>,
{
bridge(self, consumer)
}
fn opt_len(&self) -> Option<usize> {
Some(self.len())
}
}
impl<'data, T: Sync + 'data> IndexedParallelIterator for Windows<'data, T> {
fn drive<C>(self, consumer: C) -> C::Result
where
C: Consumer<Self::Item>,
{
bridge(self, consumer)
}
fn len(&self) -> usize {
assert!(self.window_size >= 1);
self.slice.len().saturating_sub(self.window_size - 1)
}
fn with_producer<CB>(self, callback: CB) -> CB::Output
where
CB: ProducerCallback<Self::Item>,
{
callback.callback(WindowsProducer {
window_size: self.window_size,
slice: self.slice,
})
}
}
struct WindowsProducer<'data, T: Sync> {
window_size: usize,
slice: &'data [T],
}
impl<'data, T: 'data + Sync> Producer for WindowsProducer<'data, T> {
type Item = &'data [T];
type IntoIter = ::std::slice::Windows<'data, T>;
fn into_iter(self) -> Self::IntoIter {
self.slice.windows(self.window_size)
}
fn split_at(self, index: usize) -> (Self, Self) {
let left_index = Ord::min(self.slice.len(), index + (self.window_size - 1));
let left = &self.slice[..left_index];
let right = &self.slice[index..];
(
WindowsProducer {
window_size: self.window_size,
slice: left,
},
WindowsProducer {
window_size: self.window_size,
slice: right,
},
)
}
}
/// Parallel iterator over mutable items in a slice
#[derive(Debug)]
pub struct IterMut<'data, T: Send> {
slice: &'data mut [T],
}
impl<'data, T: Send + 'data> ParallelIterator for IterMut<'data, T> {
type Item = &'data mut T;
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>,
{
bridge(self, consumer)
}
fn opt_len(&self) -> Option<usize> {
Some(self.len())
}
}
impl<'data, T: Send + 'data> IndexedParallelIterator for IterMut<'data, T> {
fn drive<C>(self, consumer: C) -> C::Result
where
C: Consumer<Self::Item>,
{
bridge(self, consumer)
}
fn len(&self) -> usize {
self.slice.len()
}
fn with_producer<CB>(self, callback: CB) -> CB::Output
where
CB: ProducerCallback<Self::Item>,
{
callback.callback(IterMutProducer { slice: self.slice })
}
}
struct IterMutProducer<'data, T: Send> {
slice: &'data mut [T],
}
impl<'data, T: 'data + Send> Producer for IterMutProducer<'data, T> {
type Item = &'data mut T;
type IntoIter = ::std::slice::IterMut<'data, T>;
fn into_iter(self) -> Self::IntoIter {
self.slice.iter_mut()
}
fn split_at(self, index: usize) -> (Self, Self) {
let (left, right) = self.slice.split_at_mut(index);
(
IterMutProducer { slice: left },
IterMutProducer { slice: right },
)
}
}
/// Parallel iterator over slices separated by a predicate
pub struct Split<'data, T, P> {
slice: &'data [T],
separator: P,
}
impl<'data, T, P: Clone> Clone for Split<'data, T, P> {
fn clone(&self) -> Self {
Split {
separator: self.separator.clone(),
..*self
}
}
}
impl<'data, T: Debug, P> Debug for Split<'data, T, P> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("Split").field("slice", &self.slice).finish()
}
}
impl<'data, T, P> ParallelIterator for Split<'data, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
T: Sync,
{
type Item = &'data [T];
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>,
{
let producer = SplitProducer::new(self.slice, &self.separator);
bridge_unindexed(producer, consumer)
}
}
/// Parallel iterator over slices separated by a predicate,
/// including the matched part as a terminator.
pub struct SplitInclusive<'data, T, P> {
slice: &'data [T],
separator: P,
}
impl<'data, T, P: Clone> Clone for SplitInclusive<'data, T, P> {
fn clone(&self) -> Self {
SplitInclusive {
separator: self.separator.clone(),
..*self
}
}
}
impl<'data, T: Debug, P> Debug for SplitInclusive<'data, T, P> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("SplitInclusive")
.field("slice", &self.slice)
.finish()
}
}
impl<'data, T, P> ParallelIterator for SplitInclusive<'data, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
T: Sync,
{
type Item = &'data [T];
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>,
{
let producer = SplitInclusiveProducer::new_incl(self.slice, &self.separator);
bridge_unindexed(producer, consumer)
}
}
/// Implement support for `SplitProducer`.
impl<'data, T, P> Fissile<P> for &'data [T]
where
P: Fn(&T) -> bool,
{
fn length(&self) -> usize {
self.len()
}
fn midpoint(&self, end: usize) -> usize {
end / 2
}
fn find(&self, separator: &P, start: usize, end: usize) -> Option<usize> {
self[start..end].iter().position(separator)
}
fn rfind(&self, separator: &P, end: usize) -> Option<usize> {
self[..end].iter().rposition(separator)
}
fn split_once<const INCL: bool>(self, index: usize) -> (Self, Self) {
if INCL {
// include the separator in the left side
self.split_at(index + 1)
} else {
let (left, right) = self.split_at(index);
(left, &right[1..]) // skip the separator
}
}
fn fold_splits<F, const INCL: bool>(self, separator: &P, folder: F, skip_last: bool) -> F
where
F: Folder<Self>,
Self: Send,
{
if INCL {
debug_assert!(!skip_last);
folder.consume_iter(self.split_inclusive(separator))
} else {
let mut split = self.split(separator);
if skip_last {
split.next_back();
}
folder.consume_iter(split)
}
}
}
/// Parallel iterator over mutable slices separated by a predicate
pub struct SplitMut<'data, T, P> {
slice: &'data mut [T],
separator: P,
}
impl<'data, T: Debug, P> Debug for SplitMut<'data, T, P> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("SplitMut")
.field("slice", &self.slice)
.finish()
}
}
impl<'data, T, P> ParallelIterator for SplitMut<'data, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
T: Send,
{
type Item = &'data mut [T];
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>,
{
let producer = SplitProducer::new(self.slice, &self.separator);
bridge_unindexed(producer, consumer)
}
}
/// Parallel iterator over mutable slices separated by a predicate,
/// including the matched part as a terminator.
pub struct SplitInclusiveMut<'data, T, P> {
slice: &'data mut [T],
separator: P,
}
impl<'data, T: Debug, P> Debug for SplitInclusiveMut<'data, T, P> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
f.debug_struct("SplitInclusiveMut")
.field("slice", &self.slice)
.finish()
}
}
impl<'data, T, P> ParallelIterator for SplitInclusiveMut<'data, T, P>
where
P: Fn(&T) -> bool + Sync + Send,
T: Send,
{
type Item = &'data mut [T];
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>,
{
let producer = SplitInclusiveProducer::new_incl(self.slice, &self.separator);
bridge_unindexed(producer, consumer)
}
}
/// Implement support for `SplitProducer`.
impl<'data, T, P> Fissile<P> for &'data mut [T]
where
P: Fn(&T) -> bool,
{
fn length(&self) -> usize {
self.len()
}
fn midpoint(&self, end: usize) -> usize {
end / 2
}
fn find(&self, separator: &P, start: usize, end: usize) -> Option<usize> {
self[start..end].iter().position(separator)
}
fn rfind(&self, separator: &P, end: usize) -> Option<usize> {
self[..end].iter().rposition(separator)
}
fn split_once<const INCL: bool>(self, index: usize) -> (Self, Self) {
if INCL {
// include the separator in the left side
self.split_at_mut(index + 1)
} else {
let (left, right) = self.split_at_mut(index);
(left, &mut right[1..]) // skip the separator
}
}
fn fold_splits<F, const INCL: bool>(self, separator: &P, folder: F, skip_last: bool) -> F
where
F: Folder<Self>,
Self: Send,
{
if INCL {
debug_assert!(!skip_last);
folder.consume_iter(self.split_inclusive_mut(separator))
} else {
let mut split = self.split_mut(separator);
if skip_last {
split.next_back();
}
folder.consume_iter(split)
}
}
}