1//! Parallel iterator types for [slices][std::slice]
2//!
3//! You will rarely need to interact with this module directly unless you need
4//! to name one of the iterator types.
5//!
6//! [std::slice]: https://doc.rust-lang.org/stable/std/slice/
7
8mod chunks;
9mod mergesort;
10mod quicksort;
11mod rchunks;
12
13mod test;
14
15use self::mergesort::par_mergesort;
16use self::quicksort::par_quicksort;
17use crate::iter::plumbing::*;
18use crate::iter::*;
19use crate::split_producer::*;
20use std::cmp;
21use std::cmp::Ordering;
22use std::fmt::{self, Debug};
23use std::mem;
24
25pub use self::chunks::{Chunks, ChunksExact, ChunksExactMut, ChunksMut};
26pub use self::rchunks::{RChunks, RChunksExact, RChunksExactMut, RChunksMut};
27
28/// Parallel extensions for slices.
29pub trait ParallelSlice<T: Sync> {
30 /// Returns a plain slice, which is used to implement the rest of the
31 /// parallel methods.
32 fn as_parallel_slice(&self) -> &[T];
33
34 /// Returns a parallel iterator over subslices separated by elements that
35 /// match the separator.
36 ///
37 /// # Examples
38 ///
39 /// ```
40 /// use rayon::prelude::*;
41 /// let smallest = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9]
42 /// .par_split(|i| *i == 0)
43 /// .map(|numbers| numbers.iter().min().unwrap())
44 /// .min();
45 /// assert_eq!(Some(&1), smallest);
46 /// ```
47 fn par_split<P>(&self, separator: P) -> Split<'_, T, P>
48 where
49 P: Fn(&T) -> bool + Sync + Send,
50 {
51 Split {
52 slice: self.as_parallel_slice(),
53 separator,
54 }
55 }
56
57 /// Returns a parallel iterator over all contiguous windows of length
58 /// `window_size`. The windows overlap.
59 ///
60 /// # Examples
61 ///
62 /// ```
63 /// use rayon::prelude::*;
64 /// let windows: Vec<_> = [1, 2, 3].par_windows(2).collect();
65 /// assert_eq!(vec![[1, 2], [2, 3]], windows);
66 /// ```
67 fn par_windows(&self, window_size: usize) -> Windows<'_, T> {
68 Windows {
69 window_size,
70 slice: self.as_parallel_slice(),
71 }
72 }
73
74 /// Returns a parallel iterator over at most `chunk_size` elements of
75 /// `self` at a time. The chunks do not overlap.
76 ///
77 /// If the number of elements in the iterator is not divisible by
78 /// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
79 /// other chunks will have that exact length.
80 ///
81 /// # Examples
82 ///
83 /// ```
84 /// use rayon::prelude::*;
85 /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_chunks(2).collect();
86 /// assert_eq!(chunks, vec![&[1, 2][..], &[3, 4], &[5]]);
87 /// ```
88 #[track_caller]
89 fn par_chunks(&self, chunk_size: usize) -> Chunks<'_, T> {
90 assert!(chunk_size != 0, "chunk_size must not be zero");
91 Chunks::new(chunk_size, self.as_parallel_slice())
92 }
93
94 /// Returns a parallel iterator over `chunk_size` elements of
95 /// `self` at a time. The chunks do not overlap.
96 ///
97 /// If `chunk_size` does not divide the length of the slice, then the
98 /// last up to `chunk_size-1` elements will be omitted and can be
99 /// retrieved from the remainder function of the iterator.
100 ///
101 /// # Examples
102 ///
103 /// ```
104 /// use rayon::prelude::*;
105 /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_chunks_exact(2).collect();
106 /// assert_eq!(chunks, vec![&[1, 2][..], &[3, 4]]);
107 /// ```
108 #[track_caller]
109 fn par_chunks_exact(&self, chunk_size: usize) -> ChunksExact<'_, T> {
110 assert!(chunk_size != 0, "chunk_size must not be zero");
111 ChunksExact::new(chunk_size, self.as_parallel_slice())
112 }
113
114 /// Returns a parallel iterator over at most `chunk_size` elements of `self` at a time,
115 /// starting at the end. The chunks do not overlap.
116 ///
117 /// If the number of elements in the iterator is not divisible by
118 /// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
119 /// other chunks will have that exact length.
120 ///
121 /// # Examples
122 ///
123 /// ```
124 /// use rayon::prelude::*;
125 /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_rchunks(2).collect();
126 /// assert_eq!(chunks, vec![&[4, 5][..], &[2, 3], &[1]]);
127 /// ```
128 #[track_caller]
129 fn par_rchunks(&self, chunk_size: usize) -> RChunks<'_, T> {
130 assert!(chunk_size != 0, "chunk_size must not be zero");
131 RChunks::new(chunk_size, self.as_parallel_slice())
132 }
133
134 /// Returns a parallel iterator over `chunk_size` elements of `self` at a time,
135 /// starting at the end. The chunks do not overlap.
136 ///
137 /// If `chunk_size` does not divide the length of the slice, then the
138 /// last up to `chunk_size-1` elements will be omitted and can be
139 /// retrieved from the remainder function of the iterator.
140 ///
141 /// # Examples
142 ///
143 /// ```
144 /// use rayon::prelude::*;
145 /// let chunks: Vec<_> = [1, 2, 3, 4, 5].par_rchunks_exact(2).collect();
146 /// assert_eq!(chunks, vec![&[4, 5][..], &[2, 3]]);
147 /// ```
148 #[track_caller]
149 fn par_rchunks_exact(&self, chunk_size: usize) -> RChunksExact<'_, T> {
150 assert!(chunk_size != 0, "chunk_size must not be zero");
151 RChunksExact::new(chunk_size, self.as_parallel_slice())
152 }
153}
154
155impl<T: Sync> ParallelSlice<T> for [T] {
156 #[inline]
157 fn as_parallel_slice(&self) -> &[T] {
158 self
159 }
160}
161
162/// Parallel extensions for mutable slices.
163pub trait ParallelSliceMut<T: Send> {
164 /// Returns a plain mutable slice, which is used to implement the rest of
165 /// the parallel methods.
166 fn as_parallel_slice_mut(&mut self) -> &mut [T];
167
168 /// Returns a parallel iterator over mutable subslices separated by
169 /// elements that match the separator.
170 ///
171 /// # Examples
172 ///
173 /// ```
174 /// use rayon::prelude::*;
175 /// let mut array = [1, 2, 3, 0, 2, 4, 8, 0, 3, 6, 9];
176 /// array.par_split_mut(|i| *i == 0)
177 /// .for_each(|slice| slice.reverse());
178 /// assert_eq!(array, [3, 2, 1, 0, 8, 4, 2, 0, 9, 6, 3]);
179 /// ```
180 fn par_split_mut<P>(&mut self, separator: P) -> SplitMut<'_, T, P>
181 where
182 P: Fn(&T) -> bool + Sync + Send,
183 {
184 SplitMut {
185 slice: self.as_parallel_slice_mut(),
186 separator,
187 }
188 }
189
190 /// Returns a parallel iterator over at most `chunk_size` elements of
191 /// `self` at a time. The chunks are mutable and do not overlap.
192 ///
193 /// If the number of elements in the iterator is not divisible by
194 /// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
195 /// other chunks will have that exact length.
196 ///
197 /// # Examples
198 ///
199 /// ```
200 /// use rayon::prelude::*;
201 /// let mut array = [1, 2, 3, 4, 5];
202 /// array.par_chunks_mut(2)
203 /// .for_each(|slice| slice.reverse());
204 /// assert_eq!(array, [2, 1, 4, 3, 5]);
205 /// ```
206 #[track_caller]
207 fn par_chunks_mut(&mut self, chunk_size: usize) -> ChunksMut<'_, T> {
208 assert!(chunk_size != 0, "chunk_size must not be zero");
209 ChunksMut::new(chunk_size, self.as_parallel_slice_mut())
210 }
211
212 /// Returns a parallel iterator over `chunk_size` elements of
213 /// `self` at a time. The chunks are mutable and do not overlap.
214 ///
215 /// If `chunk_size` does not divide the length of the slice, then the
216 /// last up to `chunk_size-1` elements will be omitted and can be
217 /// retrieved from the remainder function of the iterator.
218 ///
219 /// # Examples
220 ///
221 /// ```
222 /// use rayon::prelude::*;
223 /// let mut array = [1, 2, 3, 4, 5];
224 /// array.par_chunks_exact_mut(3)
225 /// .for_each(|slice| slice.reverse());
226 /// assert_eq!(array, [3, 2, 1, 4, 5]);
227 /// ```
228 #[track_caller]
229 fn par_chunks_exact_mut(&mut self, chunk_size: usize) -> ChunksExactMut<'_, T> {
230 assert!(chunk_size != 0, "chunk_size must not be zero");
231 ChunksExactMut::new(chunk_size, self.as_parallel_slice_mut())
232 }
233
234 /// Returns a parallel iterator over at most `chunk_size` elements of `self` at a time,
235 /// starting at the end. The chunks are mutable and do not overlap.
236 ///
237 /// If the number of elements in the iterator is not divisible by
238 /// `chunk_size`, the last chunk may be shorter than `chunk_size`. All
239 /// other chunks will have that exact length.
240 ///
241 /// # Examples
242 ///
243 /// ```
244 /// use rayon::prelude::*;
245 /// let mut array = [1, 2, 3, 4, 5];
246 /// array.par_rchunks_mut(2)
247 /// .for_each(|slice| slice.reverse());
248 /// assert_eq!(array, [1, 3, 2, 5, 4]);
249 /// ```
250 #[track_caller]
251 fn par_rchunks_mut(&mut self, chunk_size: usize) -> RChunksMut<'_, T> {
252 assert!(chunk_size != 0, "chunk_size must not be zero");
253 RChunksMut::new(chunk_size, self.as_parallel_slice_mut())
254 }
255
256 /// Returns a parallel iterator over `chunk_size` elements of `self` at a time,
257 /// starting at the end. The chunks are mutable and do not overlap.
258 ///
259 /// If `chunk_size` does not divide the length of the slice, then the
260 /// last up to `chunk_size-1` elements will be omitted and can be
261 /// retrieved from the remainder function of the iterator.
262 ///
263 /// # Examples
264 ///
265 /// ```
266 /// use rayon::prelude::*;
267 /// let mut array = [1, 2, 3, 4, 5];
268 /// array.par_rchunks_exact_mut(3)
269 /// .for_each(|slice| slice.reverse());
270 /// assert_eq!(array, [1, 2, 5, 4, 3]);
271 /// ```
272 #[track_caller]
273 fn par_rchunks_exact_mut(&mut self, chunk_size: usize) -> RChunksExactMut<'_, T> {
274 assert!(chunk_size != 0, "chunk_size must not be zero");
275 RChunksExactMut::new(chunk_size, self.as_parallel_slice_mut())
276 }
277
278 /// Sorts the slice in parallel.
279 ///
280 /// This sort is stable (i.e., does not reorder equal elements) and *O*(*n* \* log(*n*)) worst-case.
281 ///
282 /// When applicable, unstable sorting is preferred because it is generally faster than stable
283 /// sorting and it doesn't allocate auxiliary memory.
284 /// See [`par_sort_unstable`](#method.par_sort_unstable).
285 ///
286 /// # Current implementation
287 ///
288 /// The current algorithm is an adaptive merge sort inspired by
289 /// [timsort](https://en.wikipedia.org/wiki/Timsort).
290 /// It is designed to be very fast in cases where the slice is nearly sorted, or consists of
291 /// two or more sorted sequences concatenated one after another.
292 ///
293 /// Also, it allocates temporary storage the same size as `self`, but for very short slices a
294 /// non-allocating insertion sort is used instead.
295 ///
296 /// In order to sort the slice in parallel, the slice is first divided into smaller chunks and
297 /// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending
298 /// or descending runs are concatenated. Finally, the remaining chunks are merged together using
299 /// parallel subdivision of chunks and parallel merge operation.
300 ///
301 /// # Examples
302 ///
303 /// ```
304 /// use rayon::prelude::*;
305 ///
306 /// let mut v = [-5, 4, 1, -3, 2];
307 ///
308 /// v.par_sort();
309 /// assert_eq!(v, [-5, -3, 1, 2, 4]);
310 /// ```
311 fn par_sort(&mut self)
312 where
313 T: Ord,
314 {
315 par_mergesort(self.as_parallel_slice_mut(), T::lt);
316 }
317
318 /// Sorts the slice in parallel with a comparator function.
319 ///
320 /// This sort is stable (i.e., does not reorder equal elements) and *O*(*n* \* log(*n*)) worst-case.
321 ///
322 /// The comparator function must define a total ordering for the elements in the slice. If
323 /// the ordering is not total, the order of the elements is unspecified. An order is a
324 /// total order if it is (for all `a`, `b` and `c`):
325 ///
326 /// * total and antisymmetric: exactly one of `a < b`, `a == b` or `a > b` is true, and
327 /// * transitive, `a < b` and `b < c` implies `a < c`. The same must hold for both `==` and `>`.
328 ///
329 /// For example, while [`f64`] doesn't implement [`Ord`] because `NaN != NaN`, we can use
330 /// `partial_cmp` as our sort function when we know the slice doesn't contain a `NaN`.
331 ///
332 /// ```
333 /// use rayon::prelude::*;
334 ///
335 /// let mut floats = [5f64, 4.0, 1.0, 3.0, 2.0];
336 /// floats.par_sort_by(|a, b| a.partial_cmp(b).unwrap());
337 /// assert_eq!(floats, [1.0, 2.0, 3.0, 4.0, 5.0]);
338 /// ```
339 ///
340 /// When applicable, unstable sorting is preferred because it is generally faster than stable
341 /// sorting and it doesn't allocate auxiliary memory.
342 /// See [`par_sort_unstable_by`](#method.par_sort_unstable_by).
343 ///
344 /// # Current implementation
345 ///
346 /// The current algorithm is an adaptive merge sort inspired by
347 /// [timsort](https://en.wikipedia.org/wiki/Timsort).
348 /// It is designed to be very fast in cases where the slice is nearly sorted, or consists of
349 /// two or more sorted sequences concatenated one after another.
350 ///
351 /// Also, it allocates temporary storage the same size as `self`, but for very short slices a
352 /// non-allocating insertion sort is used instead.
353 ///
354 /// In order to sort the slice in parallel, the slice is first divided into smaller chunks and
355 /// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending
356 /// or descending runs are concatenated. Finally, the remaining chunks are merged together using
357 /// parallel subdivision of chunks and parallel merge operation.
358 ///
359 /// # Examples
360 ///
361 /// ```
362 /// use rayon::prelude::*;
363 ///
364 /// let mut v = [5, 4, 1, 3, 2];
365 /// v.par_sort_by(|a, b| a.cmp(b));
366 /// assert_eq!(v, [1, 2, 3, 4, 5]);
367 ///
368 /// // reverse sorting
369 /// v.par_sort_by(|a, b| b.cmp(a));
370 /// assert_eq!(v, [5, 4, 3, 2, 1]);
371 /// ```
372 fn par_sort_by<F>(&mut self, compare: F)
373 where
374 F: Fn(&T, &T) -> Ordering + Sync,
375 {
376 par_mergesort(self.as_parallel_slice_mut(), |a, b| {
377 compare(a, b) == Ordering::Less
378 });
379 }
380
381 /// Sorts the slice in parallel with a key extraction function.
382 ///
383 /// This sort is stable (i.e., does not reorder equal elements) and *O*(*m* \* *n* \* log(*n*))
384 /// worst-case, where the key function is *O*(*m*).
385 ///
386 /// For expensive key functions (e.g. functions that are not simple property accesses or
387 /// basic operations), [`par_sort_by_cached_key`](#method.par_sort_by_cached_key) is likely to
388 /// be significantly faster, as it does not recompute element keys.
389 ///
390 /// When applicable, unstable sorting is preferred because it is generally faster than stable
391 /// sorting and it doesn't allocate auxiliary memory.
392 /// See [`par_sort_unstable_by_key`](#method.par_sort_unstable_by_key).
393 ///
394 /// # Current implementation
395 ///
396 /// The current algorithm is an adaptive merge sort inspired by
397 /// [timsort](https://en.wikipedia.org/wiki/Timsort).
398 /// It is designed to be very fast in cases where the slice is nearly sorted, or consists of
399 /// two or more sorted sequences concatenated one after another.
400 ///
401 /// Also, it allocates temporary storage the same size as `self`, but for very short slices a
402 /// non-allocating insertion sort is used instead.
403 ///
404 /// In order to sort the slice in parallel, the slice is first divided into smaller chunks and
405 /// all chunks are sorted in parallel. Then, adjacent chunks that together form non-descending
406 /// or descending runs are concatenated. Finally, the remaining chunks are merged together using
407 /// parallel subdivision of chunks and parallel merge operation.
408 ///
409 /// # Examples
410 ///
411 /// ```
412 /// use rayon::prelude::*;
413 ///
414 /// let mut v = [-5i32, 4, 1, -3, 2];
415 ///
416 /// v.par_sort_by_key(|k| k.abs());
417 /// assert_eq!(v, [1, 2, -3, 4, -5]);
418 /// ```
419 fn par_sort_by_key<K, F>(&mut self, f: F)
420 where
421 K: Ord,
422 F: Fn(&T) -> K + Sync,
423 {
424 par_mergesort(self.as_parallel_slice_mut(), |a, b| f(a).lt(&f(b)));
425 }
426
427 /// Sorts the slice in parallel with a key extraction function.
428 ///
429 /// During sorting, the key function is called at most once per element, by using
430 /// temporary storage to remember the results of key evaluation.
431 /// The key function is called in parallel, so the order of calls is completely unspecified.
432 ///
433 /// This sort is stable (i.e., does not reorder equal elements) and *O*(*m* \* *n* + *n* \* log(*n*))
434 /// worst-case, where the key function is *O*(*m*).
435 ///
436 /// For simple key functions (e.g., functions that are property accesses or
437 /// basic operations), [`par_sort_by_key`](#method.par_sort_by_key) is likely to be
438 /// faster.
439 ///
440 /// # Current implementation
441 ///
442 /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
443 /// which combines the fast average case of randomized quicksort with the fast worst case of
444 /// heapsort, while achieving linear time on slices with certain patterns. It uses some
445 /// randomization to avoid degenerate cases, but with a fixed seed to always provide
446 /// deterministic behavior.
447 ///
448 /// In the worst case, the algorithm allocates temporary storage in a `Vec<(K, usize)>` the
449 /// length of the slice.
450 ///
451 /// All quicksorts work in two stages: partitioning into two halves followed by recursive
452 /// calls. The partitioning phase is sequential, but the two recursive calls are performed in
453 /// parallel. Finally, after sorting the cached keys, the item positions are updated sequentially.
454 ///
455 /// [pdqsort]: https://github.com/orlp/pdqsort
456 ///
457 /// # Examples
458 ///
459 /// ```
460 /// use rayon::prelude::*;
461 ///
462 /// let mut v = [-5i32, 4, 32, -3, 2];
463 ///
464 /// v.par_sort_by_cached_key(|k| k.to_string());
465 /// assert!(v == [-3, -5, 2, 32, 4]);
466 /// ```
467 fn par_sort_by_cached_key<K, F>(&mut self, f: F)
468 where
469 F: Fn(&T) -> K + Sync,
470 K: Ord + Send,
471 {
472 let slice = self.as_parallel_slice_mut();
473 let len = slice.len();
474 if len < 2 {
475 return;
476 }
477
478 // Helper macro for indexing our vector by the smallest possible type, to reduce allocation.
479 macro_rules! sort_by_key {
480 ($t:ty) => {{
481 let mut indices: Vec<_> = slice
482 .par_iter_mut()
483 .enumerate()
484 .map(|(i, x)| (f(&*x), i as $t))
485 .collect();
486 // The elements of `indices` are unique, as they are indexed, so any sort will be
487 // stable with respect to the original slice. We use `sort_unstable` here because
488 // it requires less memory allocation.
489 indices.par_sort_unstable();
490 for i in 0..len {
491 let mut index = indices[i].1;
492 while (index as usize) < i {
493 index = indices[index as usize].1;
494 }
495 indices[i].1 = index;
496 slice.swap(i, index as usize);
497 }
498 }};
499 }
500
501 let sz_u8 = mem::size_of::<(K, u8)>();
502 let sz_u16 = mem::size_of::<(K, u16)>();
503 let sz_u32 = mem::size_of::<(K, u32)>();
504 let sz_usize = mem::size_of::<(K, usize)>();
505
506 if sz_u8 < sz_u16 && len <= (std::u8::MAX as usize) {
507 return sort_by_key!(u8);
508 }
509 if sz_u16 < sz_u32 && len <= (std::u16::MAX as usize) {
510 return sort_by_key!(u16);
511 }
512 if sz_u32 < sz_usize && len <= (std::u32::MAX as usize) {
513 return sort_by_key!(u32);
514 }
515 sort_by_key!(usize)
516 }
517
518 /// Sorts the slice in parallel, but might not preserve the order of equal elements.
519 ///
520 /// This sort is unstable (i.e., may reorder equal elements), in-place
521 /// (i.e., does not allocate), and *O*(*n* \* log(*n*)) worst-case.
522 ///
523 /// # Current implementation
524 ///
525 /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
526 /// which combines the fast average case of randomized quicksort with the fast worst case of
527 /// heapsort, while achieving linear time on slices with certain patterns. It uses some
528 /// randomization to avoid degenerate cases, but with a fixed seed to always provide
529 /// deterministic behavior.
530 ///
531 /// It is typically faster than stable sorting, except in a few special cases, e.g., when the
532 /// slice consists of several concatenated sorted sequences.
533 ///
534 /// All quicksorts work in two stages: partitioning into two halves followed by recursive
535 /// calls. The partitioning phase is sequential, but the two recursive calls are performed in
536 /// parallel.
537 ///
538 /// [pdqsort]: https://github.com/orlp/pdqsort
539 ///
540 /// # Examples
541 ///
542 /// ```
543 /// use rayon::prelude::*;
544 ///
545 /// let mut v = [-5, 4, 1, -3, 2];
546 ///
547 /// v.par_sort_unstable();
548 /// assert_eq!(v, [-5, -3, 1, 2, 4]);
549 /// ```
550 fn par_sort_unstable(&mut self)
551 where
552 T: Ord,
553 {
554 par_quicksort(self.as_parallel_slice_mut(), T::lt);
555 }
556
557 /// Sorts the slice in parallel with a comparator function, but might not preserve the order of
558 /// equal elements.
559 ///
560 /// This sort is unstable (i.e., may reorder equal elements), in-place
561 /// (i.e., does not allocate), and *O*(*n* \* log(*n*)) worst-case.
562 ///
563 /// The comparator function must define a total ordering for the elements in the slice. If
564 /// the ordering is not total, the order of the elements is unspecified. An order is a
565 /// total order if it is (for all `a`, `b` and `c`):
566 ///
567 /// * total and antisymmetric: exactly one of `a < b`, `a == b` or `a > b` is true, and
568 /// * transitive, `a < b` and `b < c` implies `a < c`. The same must hold for both `==` and `>`.
569 ///
570 /// For example, while [`f64`] doesn't implement [`Ord`] because `NaN != NaN`, we can use
571 /// `partial_cmp` as our sort function when we know the slice doesn't contain a `NaN`.
572 ///
573 /// ```
574 /// use rayon::prelude::*;
575 ///
576 /// let mut floats = [5f64, 4.0, 1.0, 3.0, 2.0];
577 /// floats.par_sort_unstable_by(|a, b| a.partial_cmp(b).unwrap());
578 /// assert_eq!(floats, [1.0, 2.0, 3.0, 4.0, 5.0]);
579 /// ```
580 ///
581 /// # Current implementation
582 ///
583 /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
584 /// which combines the fast average case of randomized quicksort with the fast worst case of
585 /// heapsort, while achieving linear time on slices with certain patterns. It uses some
586 /// randomization to avoid degenerate cases, but with a fixed seed to always provide
587 /// deterministic behavior.
588 ///
589 /// It is typically faster than stable sorting, except in a few special cases, e.g., when the
590 /// slice consists of several concatenated sorted sequences.
591 ///
592 /// All quicksorts work in two stages: partitioning into two halves followed by recursive
593 /// calls. The partitioning phase is sequential, but the two recursive calls are performed in
594 /// parallel.
595 ///
596 /// [pdqsort]: https://github.com/orlp/pdqsort
597 ///
598 /// # Examples
599 ///
600 /// ```
601 /// use rayon::prelude::*;
602 ///
603 /// let mut v = [5, 4, 1, 3, 2];
604 /// v.par_sort_unstable_by(|a, b| a.cmp(b));
605 /// assert_eq!(v, [1, 2, 3, 4, 5]);
606 ///
607 /// // reverse sorting
608 /// v.par_sort_unstable_by(|a, b| b.cmp(a));
609 /// assert_eq!(v, [5, 4, 3, 2, 1]);
610 /// ```
611 fn par_sort_unstable_by<F>(&mut self, compare: F)
612 where
613 F: Fn(&T, &T) -> Ordering + Sync,
614 {
615 par_quicksort(self.as_parallel_slice_mut(), |a, b| {
616 compare(a, b) == Ordering::Less
617 });
618 }
619
620 /// Sorts the slice in parallel with a key extraction function, but might not preserve the order
621 /// of equal elements.
622 ///
623 /// This sort is unstable (i.e., may reorder equal elements), in-place
624 /// (i.e., does not allocate), and *O*(m \* *n* \* log(*n*)) worst-case,
625 /// where the key function is *O*(*m*).
626 ///
627 /// # Current implementation
628 ///
629 /// The current algorithm is based on [pattern-defeating quicksort][pdqsort] by Orson Peters,
630 /// which combines the fast average case of randomized quicksort with the fast worst case of
631 /// heapsort, while achieving linear time on slices with certain patterns. It uses some
632 /// randomization to avoid degenerate cases, but with a fixed seed to always provide
633 /// deterministic behavior.
634 ///
635 /// Due to its key calling strategy, `par_sort_unstable_by_key` is likely to be slower than
636 /// [`par_sort_by_cached_key`](#method.par_sort_by_cached_key) in cases where the key function
637 /// is expensive.
638 ///
639 /// All quicksorts work in two stages: partitioning into two halves followed by recursive
640 /// calls. The partitioning phase is sequential, but the two recursive calls are performed in
641 /// parallel.
642 ///
643 /// [pdqsort]: https://github.com/orlp/pdqsort
644 ///
645 /// # Examples
646 ///
647 /// ```
648 /// use rayon::prelude::*;
649 ///
650 /// let mut v = [-5i32, 4, 1, -3, 2];
651 ///
652 /// v.par_sort_unstable_by_key(|k| k.abs());
653 /// assert_eq!(v, [1, 2, -3, 4, -5]);
654 /// ```
655 fn par_sort_unstable_by_key<K, F>(&mut self, f: F)
656 where
657 K: Ord,
658 F: Fn(&T) -> K + Sync,
659 {
660 par_quicksort(self.as_parallel_slice_mut(), |a, b| f(a).lt(&f(b)));
661 }
662}
663
664impl<T: Send> ParallelSliceMut<T> for [T] {
665 #[inline]
666 fn as_parallel_slice_mut(&mut self) -> &mut [T] {
667 self
668 }
669}
670
671impl<'data, T: Sync + 'data> IntoParallelIterator for &'data [T] {
672 type Item = &'data T;
673 type Iter = Iter<'data, T>;
674
675 fn into_par_iter(self) -> Self::Iter {
676 Iter { slice: self }
677 }
678}
679
680impl<'data, T: Send + 'data> IntoParallelIterator for &'data mut [T] {
681 type Item = &'data mut T;
682 type Iter = IterMut<'data, T>;
683
684 fn into_par_iter(self) -> Self::Iter {
685 IterMut { slice: self }
686 }
687}
688
689/// Parallel iterator over immutable items in a slice
690#[derive(Debug)]
691pub struct Iter<'data, T: Sync> {
692 slice: &'data [T],
693}
694
695impl<'data, T: Sync> Clone for Iter<'data, T> {
696 fn clone(&self) -> Self {
697 Iter { ..*self }
698 }
699}
700
701impl<'data, T: Sync + 'data> ParallelIterator for Iter<'data, T> {
702 type Item = &'data T;
703
704 fn drive_unindexed<C>(self, consumer: C) -> C::Result
705 where
706 C: UnindexedConsumer<Self::Item>,
707 {
708 bridge(self, consumer)
709 }
710
711 fn opt_len(&self) -> Option<usize> {
712 Some(self.len())
713 }
714}
715
716impl<'data, T: Sync + 'data> IndexedParallelIterator for Iter<'data, T> {
717 fn drive<C>(self, consumer: C) -> C::Result
718 where
719 C: Consumer<Self::Item>,
720 {
721 bridge(self, consumer)
722 }
723
724 fn len(&self) -> usize {
725 self.slice.len()
726 }
727
728 fn with_producer<CB>(self, callback: CB) -> CB::Output
729 where
730 CB: ProducerCallback<Self::Item>,
731 {
732 callback.callback(producer:IterProducer { slice: self.slice })
733 }
734}
735
736struct IterProducer<'data, T: Sync> {
737 slice: &'data [T],
738}
739
740impl<'data, T: 'data + Sync> Producer for IterProducer<'data, T> {
741 type Item = &'data T;
742 type IntoIter = ::std::slice::Iter<'data, T>;
743
744 fn into_iter(self) -> Self::IntoIter {
745 self.slice.iter()
746 }
747
748 fn split_at(self, index: usize) -> (Self, Self) {
749 let (left: &[T], right: &[T]) = self.slice.split_at(mid:index);
750 (IterProducer { slice: left }, IterProducer { slice: right })
751 }
752}
753
754/// Parallel iterator over immutable overlapping windows of a slice
755#[derive(Debug)]
756pub struct Windows<'data, T: Sync> {
757 window_size: usize,
758 slice: &'data [T],
759}
760
761impl<'data, T: Sync> Clone for Windows<'data, T> {
762 fn clone(&self) -> Self {
763 Windows { ..*self }
764 }
765}
766
767impl<'data, T: Sync + 'data> ParallelIterator for Windows<'data, T> {
768 type Item = &'data [T];
769
770 fn drive_unindexed<C>(self, consumer: C) -> C::Result
771 where
772 C: UnindexedConsumer<Self::Item>,
773 {
774 bridge(self, consumer)
775 }
776
777 fn opt_len(&self) -> Option<usize> {
778 Some(self.len())
779 }
780}
781
782impl<'data, T: Sync + 'data> IndexedParallelIterator for Windows<'data, T> {
783 fn drive<C>(self, consumer: C) -> C::Result
784 where
785 C: Consumer<Self::Item>,
786 {
787 bridge(self, consumer)
788 }
789
790 fn len(&self) -> usize {
791 assert!(self.window_size >= 1);
792 self.slice.len().saturating_sub(self.window_size - 1)
793 }
794
795 fn with_producer<CB>(self, callback: CB) -> CB::Output
796 where
797 CB: ProducerCallback<Self::Item>,
798 {
799 callback.callback(producer:WindowsProducer {
800 window_size: self.window_size,
801 slice: self.slice,
802 })
803 }
804}
805
806struct WindowsProducer<'data, T: Sync> {
807 window_size: usize,
808 slice: &'data [T],
809}
810
811impl<'data, T: 'data + Sync> Producer for WindowsProducer<'data, T> {
812 type Item = &'data [T];
813 type IntoIter = ::std::slice::Windows<'data, T>;
814
815 fn into_iter(self) -> Self::IntoIter {
816 self.slice.windows(self.window_size)
817 }
818
819 fn split_at(self, index: usize) -> (Self, Self) {
820 let left_index: usize = cmp::min(self.slice.len(), v2:index + (self.window_size - 1));
821 let left: &[T] = &self.slice[..left_index];
822 let right: &[T] = &self.slice[index..];
823 (
824 WindowsProducer {
825 window_size: self.window_size,
826 slice: left,
827 },
828 WindowsProducer {
829 window_size: self.window_size,
830 slice: right,
831 },
832 )
833 }
834}
835
836/// Parallel iterator over mutable items in a slice
837#[derive(Debug)]
838pub struct IterMut<'data, T: Send> {
839 slice: &'data mut [T],
840}
841
842impl<'data, T: Send + 'data> ParallelIterator for IterMut<'data, T> {
843 type Item = &'data mut T;
844
845 fn drive_unindexed<C>(self, consumer: C) -> C::Result
846 where
847 C: UnindexedConsumer<Self::Item>,
848 {
849 bridge(self, consumer)
850 }
851
852 fn opt_len(&self) -> Option<usize> {
853 Some(self.len())
854 }
855}
856
857impl<'data, T: Send + 'data> IndexedParallelIterator for IterMut<'data, T> {
858 fn drive<C>(self, consumer: C) -> C::Result
859 where
860 C: Consumer<Self::Item>,
861 {
862 bridge(self, consumer)
863 }
864
865 fn len(&self) -> usize {
866 self.slice.len()
867 }
868
869 fn with_producer<CB>(self, callback: CB) -> CB::Output
870 where
871 CB: ProducerCallback<Self::Item>,
872 {
873 callback.callback(producer:IterMutProducer { slice: self.slice })
874 }
875}
876
877struct IterMutProducer<'data, T: Send> {
878 slice: &'data mut [T],
879}
880
881impl<'data, T: 'data + Send> Producer for IterMutProducer<'data, T> {
882 type Item = &'data mut T;
883 type IntoIter = ::std::slice::IterMut<'data, T>;
884
885 fn into_iter(self) -> Self::IntoIter {
886 self.slice.iter_mut()
887 }
888
889 fn split_at(self, index: usize) -> (Self, Self) {
890 let (left: &mut [T], right: &mut [T]) = self.slice.split_at_mut(mid:index);
891 (
892 IterMutProducer { slice: left },
893 IterMutProducer { slice: right },
894 )
895 }
896}
897
898/// Parallel iterator over slices separated by a predicate
899pub struct Split<'data, T, P> {
900 slice: &'data [T],
901 separator: P,
902}
903
904impl<'data, T, P: Clone> Clone for Split<'data, T, P> {
905 fn clone(&self) -> Self {
906 Split {
907 separator: self.separator.clone(),
908 ..*self
909 }
910 }
911}
912
913impl<'data, T: Debug, P> Debug for Split<'data, T, P> {
914 fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
915 f.debug_struct("Split").field(name:"slice", &self.slice).finish()
916 }
917}
918
919impl<'data, T, P> ParallelIterator for Split<'data, T, P>
920where
921 P: Fn(&T) -> bool + Sync + Send,
922 T: Sync,
923{
924 type Item = &'data [T];
925
926 fn drive_unindexed<C>(self, consumer: C) -> C::Result
927 where
928 C: UnindexedConsumer<Self::Item>,
929 {
930 let producer: SplitProducer<'_, P, &'data …> = SplitProducer::new(self.slice, &self.separator);
931 bridge_unindexed(producer, consumer)
932 }
933}
934
935/// Implement support for `SplitProducer`.
936impl<'data, T, P> Fissile<P> for &'data [T]
937where
938 P: Fn(&T) -> bool,
939{
940 fn length(&self) -> usize {
941 self.len()
942 }
943
944 fn midpoint(&self, end: usize) -> usize {
945 end / 2
946 }
947
948 fn find(&self, separator: &P, start: usize, end: usize) -> Option<usize> {
949 self[start..end].iter().position(separator)
950 }
951
952 fn rfind(&self, separator: &P, end: usize) -> Option<usize> {
953 self[..end].iter().rposition(separator)
954 }
955
956 fn split_once(self, index: usize) -> (Self, Self) {
957 let (left, right) = self.split_at(index);
958 (left, &right[1..]) // skip the separator
959 }
960
961 fn fold_splits<F>(self, separator: &P, folder: F, skip_last: bool) -> F
962 where
963 F: Folder<Self>,
964 Self: Send,
965 {
966 let mut split = self.split(separator);
967 if skip_last {
968 split.next_back();
969 }
970 folder.consume_iter(split)
971 }
972}
973
974/// Parallel iterator over mutable slices separated by a predicate
975pub struct SplitMut<'data, T, P> {
976 slice: &'data mut [T],
977 separator: P,
978}
979
980impl<'data, T: Debug, P> Debug for SplitMut<'data, T, P> {
981 fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
982 f&mut DebugStruct<'_, '_>.debug_struct("SplitMut")
983 .field(name:"slice", &self.slice)
984 .finish()
985 }
986}
987
988impl<'data, T, P> ParallelIterator for SplitMut<'data, T, P>
989where
990 P: Fn(&T) -> bool + Sync + Send,
991 T: Send,
992{
993 type Item = &'data mut [T];
994
995 fn drive_unindexed<C>(self, consumer: C) -> C::Result
996 where
997 C: UnindexedConsumer<Self::Item>,
998 {
999 let producer: SplitProducer<'_, P, &'data mut …> = SplitProducer::new(self.slice, &self.separator);
1000 bridge_unindexed(producer, consumer)
1001 }
1002}
1003
1004/// Implement support for `SplitProducer`.
1005impl<'data, T, P> Fissile<P> for &'data mut [T]
1006where
1007 P: Fn(&T) -> bool,
1008{
1009 fn length(&self) -> usize {
1010 self.len()
1011 }
1012
1013 fn midpoint(&self, end: usize) -> usize {
1014 end / 2
1015 }
1016
1017 fn find(&self, separator: &P, start: usize, end: usize) -> Option<usize> {
1018 self[start..end].iter().position(separator)
1019 }
1020
1021 fn rfind(&self, separator: &P, end: usize) -> Option<usize> {
1022 self[..end].iter().rposition(separator)
1023 }
1024
1025 fn split_once(self, index: usize) -> (Self, Self) {
1026 let (left, right) = self.split_at_mut(index);
1027 (left, &mut right[1..]) // skip the separator
1028 }
1029
1030 fn fold_splits<F>(self, separator: &P, folder: F, skip_last: bool) -> F
1031 where
1032 F: Folder<Self>,
1033 Self: Send,
1034 {
1035 let mut split = self.split_mut(separator);
1036 if skip_last {
1037 split.next_back();
1038 }
1039 folder.consume_iter(split)
1040 }
1041}
1042