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Implement feature sort_unstable

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This commit is contained in:
Stjepan Glavina 2017-03-17 15:05:44 +01:00
parent 58c701f5c7
commit f1913e2a30
10 changed files with 983 additions and 129 deletions
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1
src/libcollections/benches/lib.rs
View file

@ -11,6 +11,7 @@
#![deny(warnings)]
#![feature(rand)]
#![feature(sort_unstable)]
#![feature(test)]
extern crate test;

108
src/libcollections/benches/slice.rs
View file

@ -169,6 +169,7 @@ fn random_inserts(b: &mut Bencher) {
}
})
}
#[bench]
fn random_removes(b: &mut Bencher) {
let mut rng = thread_rng();
@ -216,65 +217,76 @@ fn gen_mostly_descending(len: usize) -> Vec<u64> {
v
}
fn gen_strings(len: usize) -> Vec<String> {
let mut rng = thread_rng();
let mut v = vec![];
for _ in 0..len {
let n = rng.gen::<usize>() % 20 + 1;
v.push(rng.gen_ascii_chars().take(n).collect());
}
v
}
fn gen_big_random(len: usize) -> Vec<[u64; 16]> {
let mut rng = thread_rng();
rng.gen_iter().map(|x| [x; 16]).take(len).collect()
}
fn gen_big_ascending(len: usize) -> Vec<[u64; 16]> {
(0..len as u64).map(|x| [x; 16]).take(len).collect()
}
fn gen_big_descending(len: usize) -> Vec<[u64; 16]> {
(0..len as u64).rev().map(|x| [x; 16]).take(len).collect()
}
macro_rules! sort_bench {
($name:ident, $gen:expr, $len:expr) => {
macro_rules! sort {
($f:ident, $name:ident, $gen:expr, $len:expr) => {
#[bench]
fn $name(b: &mut Bencher) {
b.iter(|| $gen($len).sort());
b.iter(|| $gen($len).$f());
b.bytes = $len * mem::size_of_val(&$gen(1)[0]) as u64;
}
}
}
sort_bench!(sort_small_random, gen_random, 10);
sort_bench!(sort_small_ascending, gen_ascending, 10);
sort_bench!(sort_small_descending, gen_descending, 10);
macro_rules! sort_expensive {
($f:ident, $name:ident, $gen:expr, $len:expr) => {
#[bench]
fn $name(b: &mut Bencher) {
b.iter(|| {
let mut v = $gen($len);
let mut count = 0;
v.$f(|a: &u64, b: &u64| {
count += 1;
if count % 1_000_000_000 == 0 {
panic!("should not happen");
}
(*a as f64).cos().partial_cmp(&(*b as f64).cos()).unwrap()
});
black_box(count);
});
b.bytes = $len as u64 * mem::size_of::<u64>() as u64;
}
}
}
sort_bench!(sort_small_big_random, gen_big_random, 10);
sort_bench!(sort_small_big_ascending, gen_big_ascending, 10);
sort_bench!(sort_small_big_descending, gen_big_descending, 10);
sort!(sort, sort_small_ascending, gen_ascending, 10);
sort!(sort, sort_small_descending, gen_descending, 10);
sort!(sort, sort_small_random, gen_random, 10);
sort!(sort, sort_small_big_random, gen_big_random, 10);
sort!(sort, sort_medium_random, gen_random, 100);
sort!(sort, sort_large_ascending, gen_ascending, 10000);
sort!(sort, sort_large_descending, gen_descending, 10000);
sort!(sort, sort_large_mostly_ascending, gen_mostly_ascending, 10000);
sort!(sort, sort_large_mostly_descending, gen_mostly_descending, 10000);
sort!(sort, sort_large_random, gen_random, 10000);
sort!(sort, sort_large_big_random, gen_big_random, 10000);
sort!(sort, sort_large_strings, gen_strings, 10000);
sort_expensive!(sort_by, sort_large_random_expensive, gen_random, 10000);
sort_bench!(sort_medium_random, gen_random, 100);
sort_bench!(sort_medium_ascending, gen_ascending, 100);
sort_bench!(sort_medium_descending, gen_descending, 100);
sort_bench!(sort_large_random, gen_random, 10000);
sort_bench!(sort_large_ascending, gen_ascending, 10000);
sort_bench!(sort_large_descending, gen_descending, 10000);
sort_bench!(sort_large_mostly_ascending, gen_mostly_ascending, 10000);
sort_bench!(sort_large_mostly_descending, gen_mostly_descending, 10000);
sort_bench!(sort_large_big_random, gen_big_random, 10000);
sort_bench!(sort_large_big_ascending, gen_big_ascending, 10000);
sort_bench!(sort_large_big_descending, gen_big_descending, 10000);
#[bench]
fn sort_large_random_expensive(b: &mut Bencher) {
let len = 10000;
b.iter(|| {
let mut v = gen_random(len);
let mut count = 0;
v.sort_by(|a: &u64, b: &u64| {
count += 1;
if count % 1_000_000_000 == 0 {
panic!("should not happen");
}
(*a as f64).cos().partial_cmp(&(*b as f64).cos()).unwrap()
});
black_box(count);
});
b.bytes = len as u64 * mem::size_of::<u64>() as u64;
}
sort!(sort_unstable, sort_unstable_small_ascending, gen_ascending, 10);
sort!(sort_unstable, sort_unstable_small_descending, gen_descending, 10);
sort!(sort_unstable, sort_unstable_small_random, gen_random, 10);
sort!(sort_unstable, sort_unstable_small_big_random, gen_big_random, 10);
sort!(sort_unstable, sort_unstable_medium_random, gen_random, 100);
sort!(sort_unstable, sort_unstable_large_ascending, gen_ascending, 10000);
sort!(sort_unstable, sort_unstable_large_descending, gen_descending, 10000);
sort!(sort_unstable, sort_unstable_large_mostly_ascending, gen_mostly_ascending, 10000);
sort!(sort_unstable, sort_unstable_large_mostly_descending, gen_mostly_descending, 10000);
sort!(sort_unstable, sort_unstable_large_random, gen_random, 10000);
sort!(sort_unstable, sort_unstable_large_big_random, gen_big_random, 10000);
sort!(sort_unstable, sort_unstable_large_strings, gen_strings, 10000);
sort_expensive!(sort_unstable_by, sort_unstable_large_random_expensive, gen_random, 10000);

1
src/libcollections/lib.rs
View file

@ -52,6 +52,7 @@
#![feature(shared)]
#![feature(slice_get_slice)]
#![feature(slice_patterns)]
#![feature(sort_unstable)]
#![feature(specialization)]
#![feature(staged_api)]
#![feature(str_internals)]

190
src/libcollections/slice.rs
View file

@ -1092,36 +1092,6 @@ impl<T> [T] {
merge_sort(self, |a, b| a.lt(b));
}
/// Sorts the slice using `f` to extract a key to compare elements by.
///
/// This sort is stable (i.e. does not reorder equal elements) and `O(n log n)` worst-case.
///
/// # Current implementation
///
/// The current algorithm is an adaptive, iterative merge sort inspired by
/// [timsort](https://en.wikipedia.org/wiki/Timsort).
/// 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 half the size of `self`, but for short slices a
/// non-allocating insertion sort is used instead.
///
/// # Examples
///
/// ```
/// let mut v = [-5i32, 4, 1, -3, 2];
///
/// v.sort_by_key(|k| k.abs());
/// assert!(v == [1, 2, -3, 4, -5]);
/// ```
#[stable(feature = "slice_sort_by_key", since = "1.7.0")]
#[inline]
pub fn sort_by_key<B, F>(&mut self, mut f: F)
where F: FnMut(&T) -> B, B: Ord
{
merge_sort(self, |a, b| f(a).lt(&f(b)));
}
/// Sorts the slice using `compare` to compare elements.
///
/// This sort is stable (i.e. does not reorder equal elements) and `O(n log n)` worst-case.
@ -1155,6 +1125,144 @@ impl<T> [T] {
merge_sort(self, |a, b| compare(a, b) == Less);
}
/// Sorts the slice using `f` to extract a key to compare elements by.
///
/// This sort is stable (i.e. does not reorder equal elements) and `O(n log n)` worst-case.
///
/// # Current implementation
///
/// The current algorithm is an adaptive, iterative merge sort inspired by
/// [timsort](https://en.wikipedia.org/wiki/Timsort).
/// 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 half the size of `self`, but for short slices a
/// non-allocating insertion sort is used instead.
///
/// # Examples
///
/// ```
/// let mut v = [-5i32, 4, 1, -3, 2];
///
/// v.sort_by_key(|k| k.abs());
/// assert!(v == [1, 2, -3, 4, -5]);
/// ```
#[stable(feature = "slice_sort_by_key", since = "1.7.0")]
#[inline]
pub fn sort_by_key<B, F>(&mut self, mut f: F)
where F: FnMut(&T) -> B, B: Ord
{
merge_sort(self, |a, b| f(a).lt(&f(b)));
}
/// Sorts the slice, but may 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 Orson Peters' [pdqsort][pattern-defeating quicksort],
/// which is a quicksort variant designed to be very fast on certain kinds of patterns,
/// sometimes achieving linear time. It is randomized but deterministic, and falls back to
/// heapsort on degenerate inputs.
///
/// It is generally faster than stable sorting, except in a few special cases, e.g. when the
/// slice consists of several concatenated sorted sequences.
///
/// # Examples
///
/// ```
/// let mut v = [-5, 4, 1, -3, 2];
///
/// v.sort_unstable();
/// assert!(v == [-5, -3, 1, 2, 4]);
/// ```
///
/// [pdqsort]: https://github.com/orlp/pdqsort
// FIXME #40585: Mention `sort_unstable` in the documentation for `sort`.
#[unstable(feature = "sort_unstable", issue = "40585")]
#[inline]
pub fn sort_unstable(&mut self)
where T: Ord
{
core_slice::SliceExt::sort_unstable(self);
}
/// Sorts the slice using `compare` to compare elements, but may 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 Orson Peters' [pdqsort][pattern-defeating quicksort],
/// which is a quicksort variant designed to be very fast on certain kinds of patterns,
/// sometimes achieving linear time. It is randomized but deterministic, and falls back to
/// heapsort on degenerate inputs.
///
/// It is generally faster than stable sorting, except in a few special cases, e.g. when the
/// slice consists of several concatenated sorted sequences.
///
/// # Examples
///
/// ```
/// let mut v = [5, 4, 1, 3, 2];
/// v.sort_unstable_by(|a, b| a.cmp(b));
/// assert!(v == [1, 2, 3, 4, 5]);
///
/// // reverse sorting
/// v.sort_unstable_by(|a, b| b.cmp(a));
/// assert!(v == [5, 4, 3, 2, 1]);
/// ```
///
/// [pdqsort]: https://github.com/orlp/pdqsort
// FIXME #40585: Mention `sort_unstable_by` in the documentation for `sort_by`.
#[unstable(feature = "sort_unstable", issue = "40585")]
#[inline]
pub fn sort_unstable_by<F>(&mut self, compare: F)
where F: FnMut(&T, &T) -> Ordering
{
core_slice::SliceExt::sort_unstable_by(self, compare);
}
/// Sorts the slice using `f` to extract a key to compare elements by, but may 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 Orson Peters' [pdqsort][pattern-defeating quicksort],
/// which is a quicksort variant designed to be very fast on certain kinds of patterns,
/// sometimes achieving linear time. It is randomized but deterministic, and falls back to
/// heapsort on degenerate inputs.
///
/// It is generally faster than stable sorting, except in a few special cases, e.g. when the
/// slice consists of several concatenated sorted sequences.
///
/// # Examples
///
/// ```
/// let mut v = [-5i32, 4, 1, -3, 2];
///
/// v.sort_unstable_by_key(|k| k.abs());
/// assert!(v == [1, 2, -3, 4, -5]);
///
/// [pdqsort]: https://github.com/orlp/pdqsort
/// ```
// FIXME #40585: Mention `sort_unstable_by_key` in the documentation for `sort_by_key`.
#[unstable(feature = "sort_unstable", issue = "40585")]
#[inline]
pub fn sort_unstable_by_key<B, F>(&mut self, f: F)
where F: FnMut(&T) -> B,
B: Ord
{
core_slice::SliceExt::sort_unstable_by_key(self, f);
}
/// Copies the elements from `src` into `self`.
///
/// The length of `src` must be the same as `self`.
@ -1553,28 +1661,20 @@ unsafe fn merge<T, F>(v: &mut [T], mid: usize, buf: *mut T, is_less: &mut F)
fn merge_sort<T, F>(v: &mut [T], mut is_less: F)
where F: FnMut(&T, &T) -> bool
{
// Slices of up to this length get sorted using insertion sort.
const MAX_INSERTION: usize = 16;
// Very short runs are extended using insertion sort to span at least this many elements.
const MIN_RUN: usize = 8;
// Sorting has no meaningful behavior on zero-sized types.
if size_of::<T>() == 0 {
return;
}
// FIXME #12092: These numbers are platform-specific and need more extensive testing/tuning.
//
// If `v` has length up to `max_insertion`, simply switch to insertion sort because it is going
// to perform better than merge sort. For bigger types `T`, the threshold is smaller.
//
// Short runs are extended using insertion sort to span at least `min_run` elements, in order
// to improve performance.
let (max_insertion, min_run) = if size_of::<T>() <= 2 * mem::size_of::<usize>() {
(64, 32)
} else {
(32, 16)
};
let len = v.len();
// Short arrays get sorted in-place via insertion sort to avoid allocations.
if len <= max_insertion {
if len <= MAX_INSERTION {
if len >= 2 {
for i in (0..len-1).rev() {
insert_head(&mut v[i..], &mut is_less);
@ -1618,7 +1718,7 @@ fn merge_sort<T, F>(v: &mut [T], mut is_less: F)
// Insert some more elements into the run if it's too short. Insertion sort is faster than
// merge sort on short sequences, so this significantly improves performance.
while start > 0 && end - start < min_run {
while start > 0 && end - start < MIN_RUN {
start -= 1;
insert_head(&mut v[start..end], &mut is_less);
}

14
src/libcollectionstest/slice.rs
View file

@ -399,9 +399,10 @@ fn test_sort() {
}
}
// shouldn't panic
let mut v: [i32; 0] = [];
v.sort();
// Should not panic.
[0i32; 0].sort();
[(); 10].sort();
[(); 100].sort();
let mut v = [0xDEADBEEFu64];
v.sort();
@ -441,13 +442,6 @@ fn test_sort_stability() {
}
}
#[test]
fn test_sort_zero_sized_type() {
// Should not panic.
[(); 10].sort();
[(); 100].sort();
}
#[test]
fn test_concat() {
let v: [Vec<i32>; 0] = [];

11
src/libcore/lib.rs
View file

@ -71,26 +71,27 @@
#![feature(asm)]
#![feature(associated_type_defaults)]
#![feature(cfg_target_feature)]
#![feature(cfg_target_has_atomic)]
#![feature(concat_idents)]
#![feature(const_fn)]
#![feature(cfg_target_has_atomic)]
#![feature(custom_attribute)]
#![feature(fundamental)]
#![feature(i128_type)]
#![feature(inclusive_range_syntax)]
#![feature(intrinsics)]
#![feature(lang_items)]
#![feature(never_type)]
#![feature(no_core)]
#![feature(on_unimplemented)]
#![feature(optin_builtin_traits)]
#![feature(unwind_attributes)]
#![feature(prelude_import)]
#![feature(repr_simd, platform_intrinsics)]
#![feature(rustc_attrs)]
#![feature(specialization)]
#![feature(staged_api)]
#![feature(unboxed_closures)]
#![feature(never_type)]
#![feature(i128_type)]
#![feature(prelude_import)]
#![feature(untagged_unions)]
#![feature(unwind_attributes)]
#[prelude_import]
#[allow(unused)]

112
src/libcore/slice.rs → src/libcore/slice/mod.rs
View file

@ -1,4 +1,4 @@
// Copyright 2012-2014 The Rust Project Developers. See the COPYRIGHT
// Copyright 2012-2017 The Rust Project Developers. See the COPYRIGHT
// file at the top-level directory of this distribution and at
// http://rust-lang.org/COPYRIGHT.
//
@ -51,6 +51,8 @@ use mem;
use marker::{Copy, Send, Sync, Sized, self};
use iter_private::TrustedRandomAccess;
mod sort;
#[repr(C)]
struct Repr<T> {
pub data: *const T,
@ -71,86 +73,119 @@ pub trait SliceExt {
#[stable(feature = "core", since = "1.6.0")]
fn split_at(&self, mid: usize) -> (&[Self::Item], &[Self::Item]);
#[stable(feature = "core", since = "1.6.0")]
fn iter(&self) -> Iter<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn split<P>(&self, pred: P) -> Split<Self::Item, P>
where P: FnMut(&Self::Item) -> bool;
where P: FnMut(&Self::Item) -> bool;
#[stable(feature = "core", since = "1.6.0")]
fn splitn<P>(&self, n: usize, pred: P) -> SplitN<Self::Item, P>
where P: FnMut(&Self::Item) -> bool;
where P: FnMut(&Self::Item) -> bool;
#[stable(feature = "core", since = "1.6.0")]
fn rsplitn<P>(&self, n: usize, pred: P) -> RSplitN<Self::Item, P>
where P: FnMut(&Self::Item) -> bool;
where P: FnMut(&Self::Item) -> bool;
#[stable(feature = "core", since = "1.6.0")]
fn windows(&self, size: usize) -> Windows<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn chunks(&self, size: usize) -> Chunks<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn get<I>(&self, index: I) -> Option<&I::Output>
where I: SliceIndex<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn first(&self) -> Option<&Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn split_first(&self) -> Option<(&Self::Item, &[Self::Item])>;
#[stable(feature = "core", since = "1.6.0")]
fn split_last(&self) -> Option<(&Self::Item, &[Self::Item])>;
#[stable(feature = "core", since = "1.6.0")]
fn last(&self) -> Option<&Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
unsafe fn get_unchecked<I>(&self, index: I) -> &I::Output
where I: SliceIndex<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn as_ptr(&self) -> *const Self::Item;
#[stable(feature = "core", since = "1.6.0")]
fn binary_search<Q: ?Sized>(&self, x: &Q) -> Result<usize, usize>
where Self::Item: Borrow<Q>,
Q: Ord;
#[stable(feature = "core", since = "1.6.0")]
fn binary_search_by<'a, F>(&'a self, f: F) -> Result<usize, usize>
where F: FnMut(&'a Self::Item) -> Ordering;
#[stable(feature = "slice_binary_search_by_key", since = "1.10.0")]
fn binary_search_by_key<'a, B, F, Q: ?Sized>(&'a self, b: &Q, f: F) -> Result<usize, usize>
where F: FnMut(&'a Self::Item) -> B,
B: Borrow<Q>,
Q: Ord;
#[stable(feature = "core", since = "1.6.0")]
fn len(&self) -> usize;
#[stable(feature = "core", since = "1.6.0")]
fn is_empty(&self) -> bool { self.len() == 0 }
#[stable(feature = "core", since = "1.6.0")]
fn get_mut<I>(&mut self, index: I) -> Option<&mut I::Output>
where I: SliceIndex<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn iter_mut(&mut self) -> IterMut<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn first_mut(&mut self) -> Option<&mut Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn split_first_mut(&mut self) -> Option<(&mut Self::Item, &mut [Self::Item])>;
#[stable(feature = "core", since = "1.6.0")]
fn split_last_mut(&mut self) -> Option<(&mut Self::Item, &mut [Self::Item])>;
#[stable(feature = "core", since = "1.6.0")]
fn last_mut(&mut self) -> Option<&mut Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn split_mut<P>(&mut self, pred: P) -> SplitMut<Self::Item, P>
where P: FnMut(&Self::Item) -> bool;
where P: FnMut(&Self::Item) -> bool;
#[stable(feature = "core", since = "1.6.0")]
fn splitn_mut<P>(&mut self, n: usize, pred: P) -> SplitNMut<Self::Item, P>
where P: FnMut(&Self::Item) -> bool;
where P: FnMut(&Self::Item) -> bool;
#[stable(feature = "core", since = "1.6.0")]
fn rsplitn_mut<P>(&mut self, n: usize, pred: P) -> RSplitNMut<Self::Item, P>
where P: FnMut(&Self::Item) -> bool;
where P: FnMut(&Self::Item) -> bool;
#[stable(feature = "core", since = "1.6.0")]
fn chunks_mut(&mut self, chunk_size: usize) -> ChunksMut<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn swap(&mut self, a: usize, b: usize);
#[stable(feature = "core", since = "1.6.0")]
fn split_at_mut(&mut self, mid: usize) -> (&mut [Self::Item], &mut [Self::Item]);
#[stable(feature = "core", since = "1.6.0")]
fn reverse(&mut self);
#[stable(feature = "core", since = "1.6.0")]
unsafe fn get_unchecked_mut<I>(&mut self, index: I) -> &mut I::Output
where I: SliceIndex<Self::Item>;
#[stable(feature = "core", since = "1.6.0")]
fn as_mut_ptr(&mut self) -> *mut Self::Item;
@ -165,8 +200,22 @@ pub trait SliceExt {
#[stable(feature = "clone_from_slice", since = "1.7.0")]
fn clone_from_slice(&mut self, src: &[Self::Item]) where Self::Item: Clone;
#[stable(feature = "copy_from_slice", since = "1.9.0")]
fn copy_from_slice(&mut self, src: &[Self::Item]) where Self::Item: Copy;
#[unstable(feature = "sort_unstable", issue = "40585")]
fn sort_unstable(&mut self)
where Self::Item: Ord;
#[unstable(feature = "sort_unstable", issue = "40585")]
fn sort_unstable_by<F>(&mut self, compare: F)
where F: FnMut(&Self::Item, &Self::Item) -> Ordering;
#[unstable(feature = "sort_unstable", issue = "40585")]
fn sort_unstable_by_key<B, F>(&mut self, f: F)
where F: FnMut(&Self::Item) -> B,
B: Ord;
}
// Use macros to be generic over const/mut
@ -238,7 +287,9 @@ impl<T> SliceExt for [T] {
}
#[inline]
fn split<P>(&self, pred: P) -> Split<T, P> where P: FnMut(&T) -> bool {
fn split<P>(&self, pred: P) -> Split<T, P>
where P: FnMut(&T) -> bool
{
Split {
v: self,
pred: pred,
@ -247,8 +298,8 @@ impl<T> SliceExt for [T] {
}
#[inline]
fn splitn<P>(&self, n: usize, pred: P) -> SplitN<T, P> where
P: FnMut(&T) -> bool,
fn splitn<P>(&self, n: usize, pred: P) -> SplitN<T, P>
where P: FnMut(&T) -> bool
{
SplitN {
inner: GenericSplitN {
@ -260,8 +311,8 @@ impl<T> SliceExt for [T] {
}
#[inline]
fn rsplitn<P>(&self, n: usize, pred: P) -> RSplitN<T, P> where
P: FnMut(&T) -> bool,
fn rsplitn<P>(&self, n: usize, pred: P) -> RSplitN<T, P>
where P: FnMut(&T) -> bool
{
RSplitN {
inner: GenericSplitN {
@ -422,13 +473,15 @@ impl<T> SliceExt for [T] {
}
#[inline]
fn split_mut<P>(&mut self, pred: P) -> SplitMut<T, P> where P: FnMut(&T) -> bool {
fn split_mut<P>(&mut self, pred: P) -> SplitMut<T, P>
where P: FnMut(&T) -> bool
{
SplitMut { v: self, pred: pred, finished: false }
}
#[inline]
fn splitn_mut<P>(&mut self, n: usize, pred: P) -> SplitNMut<T, P> where
P: FnMut(&T) -> bool
fn splitn_mut<P>(&mut self, n: usize, pred: P) -> SplitNMut<T, P>
where P: FnMut(&T) -> bool
{
SplitNMut {
inner: GenericSplitN {
@ -450,7 +503,7 @@ impl<T> SliceExt for [T] {
invert: true
}
}
}
}
#[inline]
fn chunks_mut(&mut self, chunk_size: usize) -> ChunksMut<T> {
@ -512,7 +565,10 @@ impl<T> SliceExt for [T] {
m >= n && needle == &self[m-n..]
}
fn binary_search<Q: ?Sized>(&self, x: &Q) -> Result<usize, usize> where T: Borrow<Q>, Q: Ord {
fn binary_search<Q: ?Sized>(&self, x: &Q) -> Result<usize, usize>
where T: Borrow<Q>,
Q: Ord
{
self.binary_search_by(|p| p.borrow().cmp(x))
}
@ -548,6 +604,28 @@ impl<T> SliceExt for [T] {
{
self.binary_search_by(|k| f(k).borrow().cmp(b))
}
#[inline]
fn sort_unstable(&mut self)
where Self::Item: Ord
{
sort::quicksort(self, |a, b| a.lt(b));
}
#[inline]
fn sort_unstable_by<F>(&mut self, mut compare: F)
where F: FnMut(&Self::Item, &Self::Item) -> Ordering
{
sort::quicksort(self, |a, b| compare(a, b) == Ordering::Less);
}
#[inline]
fn sort_unstable_by_key<B, F>(&mut self, mut f: F)
where F: FnMut(&Self::Item) -> B,
B: Ord
{
sort::quicksort(self, |a, b| f(a).lt(&f(b)));
}
}
#[stable(feature = "rust1", since = "1.0.0")]

628
src/libcore/slice/sort.rs Normal file
View file

@ -0,0 +1,628 @@
// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
// file at the top-level directory of this distribution and at
// http://rust-lang.org/COPYRIGHT.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.
//! Slice sorting
//!
//! This module contains an sort algorithm based on Orson Peters' pattern-defeating quicksort,
//! published at: https://github.com/orlp/pdqsort
//!
//! Unstable sorting is compatible with libcore because it doesn't allocate memory, unlike our
//! stable sorting implementation.
#![unstable(feature = "sort_unstable", issue = "40585")]
use cmp;
use mem;
use ptr;
/// Holds a value, but never drops it.
#[allow(unions_with_drop_fields)]
union NoDrop<T> {
value: T
}
/// When dropped, copies from `src` into `dest`.
struct CopyOnDrop<T> {
src: *mut T,
dest: *mut T,
}
impl<T> Drop for CopyOnDrop<T> {
fn drop(&mut self) {
unsafe { ptr::copy_nonoverlapping(self.src, self.dest, 1); }
}
}
/// Sorts a slice using insertion sort, which is `O(n^2)` worst-case.
fn insertion_sort<T, F>(v: &mut [T], is_less: &mut F)
where F: FnMut(&T, &T) -> bool
{
let len = v.len();
for i in 1..len {
unsafe {
if is_less(v.get_unchecked(i), v.get_unchecked(i - 1)) {
// There are three ways to implement insertion here:
//
// 1. Swap adjacent elements until the first one gets to its final destination.
// However, this way we copy data around more than is necessary. If elements are
// big structures (costly to copy), this method will be slow.
//
// 2. Iterate until the right place for the first element is found. Then shift the
// elements succeeding it to make room for it and finally place it into the
// remaining hole. This is a good method.
//
// 3. Copy the first element into a temporary variable. Iterate until the right
// place for it is found. As we go along, copy every traversed element into the
// slot preceding it. Finally, copy data from the temporary variable into the
// remaining hole. This method is very good. Benchmarks demonstrated slightly
// better performance than with the 2nd method.
//
// All methods were benchmarked, and the 3rd showed best results. So we chose that
// one.
let mut tmp = NoDrop { value: ptr::read(v.get_unchecked(i)) };
// Intermediate state of the insertion process is always tracked by `hole`, which
// serves two purposes:
// 1. Protects integrity of `v` from panics in `is_less`.
// 2. Fills the remaining hole in `v` in the end.
//
// Panic safety:
//
// If `is_less` panics at any point during the process, `hole` will get dropped and
// fill the hole in `v` with `tmp`, thus ensuring that `v` still holds every object
// it initially held exactly once.
let mut hole = CopyOnDrop {
src: &mut tmp.value,
dest: v.get_unchecked_mut(i - 1),
};
ptr::copy_nonoverlapping(v.get_unchecked(i - 1), v.get_unchecked_mut(i), 1);
for h in (0..i-1).rev() {
if !is_less(&tmp.value, v.get_unchecked(h)) {
break;
}
ptr::copy_nonoverlapping(v.get_unchecked(h), v.get_unchecked_mut(h + 1), 1);
hole.dest = v.get_unchecked_mut(h);
}
// `hole` gets dropped and thus copies `tmp` into the remaining hole in `v`.
}
}
}
}
/// Sorts `v` using heapsort, which guarantees `O(n log n)` worst-case.
#[cold]
fn heapsort<T, F>(v: &mut [T], is_less: &mut F)
where F: FnMut(&T, &T) -> bool
{
// This binary heap respects the invariant `parent >= child`.
let mut sift_down = |v: &mut [T], mut node| {
loop {
// Children of `node`:
let left = 2 * node + 1;
let right = 2 * node + 2;
// Choose the greater child.
let greater = if right < v.len() && is_less(&v[left], &v[right]) {
right
} else {
left
};
// Stop if the invariant holds at `node`.
if greater >= v.len() || !is_less(&v[node], &v[greater]) {
break;
}
// Swap `node` with the greater child, move one step down, and continue sifting.
v.swap(node, greater);
node = greater;
}
};
// Build the heap in linear time.
for i in (0 .. v.len() / 2).rev() {
sift_down(v, i);
}
// Pop maximal elements from the heap.
for i in (1 .. v.len()).rev() {
v.swap(0, i);
sift_down(&mut v[..i], 0);
}
}
/// Partitions `v` into elements smaller than `pivot`, followed by elements greater than or equal
/// to `pivot`.
///
/// Returns the number of elements smaller than `pivot`.
///
/// Partitioning is performed block-by-block in order to minimize the cost of branching operations.
/// This idea is presented in the [BlockQuicksort][pdf] paper.
///
/// [pdf]: http://drops.dagstuhl.de/opus/volltexte/2016/6389/pdf/LIPIcs-ESA-2016-38.pdf
fn partition_in_blocks<T, F>(v: &mut [T], pivot: &T, is_less: &mut F) -> usize
where F: FnMut(&T, &T) -> bool
{
// Number of elements in a typical block.
const BLOCK: usize = 128;
// The partitioning algorithm repeats the following steps until completion:
//
// 1. Trace a block from the left side to identify elements greater than or equal to the pivot.
// 2. Trace a block from the right side to identify elements less than the pivot.
// 3. Exchange the identified elements between the left and right side.
//
// We keep the following variables for a block of elements:
//
// 1. `block` - Number of elements in the block.
// 2. `start` - Start pointer into the `offsets` array.
// 3. `end` - End pointer into the `offsets` array.
// 4. `offsets - Indices of out-of-order elements within the block.
// The current block on the left side: `v[l .. l + block_l]`.
let mut l = v.as_mut_ptr();
let mut block_l = BLOCK;
let mut start_l = ptr::null_mut();
let mut end_l = ptr::null_mut();
let mut offsets_l: [u8; BLOCK] = unsafe { mem::uninitialized() };
// The current block on the right side: `v[r - block_r .. r]`.
let mut r = unsafe { l.offset(v.len() as isize) };
let mut block_r = BLOCK;
let mut start_r = ptr::null_mut();
let mut end_r = ptr::null_mut();
let mut offsets_r: [u8; BLOCK] = unsafe { mem::uninitialized() };
// Returns the number of elements between pointers `l` (inclusive) and `r` (exclusive).
fn width<T>(l: *mut T, r: *mut T) -> usize {
assert!(mem::size_of::<T>() > 0);
(r as usize - l as usize) / mem::size_of::<T>()
}
loop {
// We are done with partitioning block-by-block when `l` and `r` get very close. Then we do
// some patch-up work in order to partition the remaining elements in between.
let is_done = width(l, r) <= 2 * BLOCK;
if is_done {
// Number of remaining elements (still not compared to the pivot).
let mut rem = width(l, r);
if start_l < end_l || start_r < end_r {
rem -= BLOCK;
}
// Adjust block sizes so that the left and right block don't overlap, but get perfectly
// aligned to cover the whole remaining gap.
if start_l < end_l {
block_r = rem;
} else if start_r < end_r {
block_l = rem;
} else {
block_l = rem / 2;
block_r = rem - block_l;
}
debug_assert!(block_l <= BLOCK && block_r <= BLOCK);
debug_assert!(width(l, r) == block_l + block_r);
}
if start_l == end_l {
// Trace `block_l` elements from the left side.
start_l = offsets_l.as_mut_ptr();
end_l = offsets_l.as_mut_ptr();
let mut elem = l;
for i in 0..block_l {
unsafe {
// Branchless comparison.
*end_l = i as u8;
end_l = end_l.offset(!is_less(&*elem, pivot) as isize);
elem = elem.offset(1);
}
}
}
if start_r == end_r {
// Trace `block_r` elements from the right side.
start_r = offsets_r.as_mut_ptr();
end_r = offsets_r.as_mut_ptr();
let mut elem = r;
for i in 0..block_r {
unsafe {
// Branchless comparison.
elem = elem.offset(-1);
*end_r = i as u8;
end_r = end_r.offset(is_less(&*elem, pivot) as isize);
}
}
}
// Number of out-of-order elements to swap between the left and right side.
let count = cmp::min(width(start_l, end_l), width(start_r, end_r));
if count > 0 {
macro_rules! left { () => { l.offset(*start_l as isize) } }
macro_rules! right { () => { r.offset(-(*start_r as isize) - 1) } }
// Instead of swapping one pair at the time, it is more efficient to perform a cyclic
// permutation. This is not strictly equivalent to swapping, but produces a similar
// result using fewer memory operations.
unsafe {
let tmp = ptr::read(left!());
ptr::copy_nonoverlapping(right!(), left!(), 1);
for _ in 1..count {
start_l = start_l.offset(1);
ptr::copy_nonoverlapping(left!(), right!(), 1);
start_r = start_r.offset(1);
ptr::copy_nonoverlapping(right!(), left!(), 1);
}
ptr::copy_nonoverlapping(&tmp, right!(), 1);
mem::forget(tmp);
start_l = start_l.offset(1);
start_r = start_r.offset(1);
}
}
if start_l == end_l {
// All out-of-order elements in the left block were moved. Move to the next block.
l = unsafe { l.offset(block_l as isize) };
}
if start_r == end_r {
// All out-of-order elements in the right block were moved. Move to the previous block.
r = unsafe { r.offset(-(block_r as isize)) };
}
if is_done {
break;
}
}
// All that remains now is at most one block (either the left or the right) with out-of-order
// elements that need to be moved. Such remaining elements can be simply shifted to the end
// within their block.
if start_l < end_l {
// The left block remains.
// Move it's remaining out-of-order elements to the far right.
debug_assert_eq!(width(l, r), block_l);
while start_l < end_l {
unsafe {
end_l = end_l.offset(-1);
ptr::swap(l.offset(*end_l as isize), r.offset(-1));
r = r.offset(-1);
}
}
width(v.as_mut_ptr(), r)
} else if start_r < end_r {
// The right block remains.
// Move it's remaining out-of-order elements to the far left.
debug_assert_eq!(width(l, r), block_r);
while start_r < end_r {
unsafe {
end_r = end_r.offset(-1);
ptr::swap(l, r.offset(-(*end_r as isize) - 1));
l = l.offset(1);
}
}
width(v.as_mut_ptr(), l)
} else {
// Nothing else to do, we're done.
width(v.as_mut_ptr(), l)
}
}
/// Partitions `v` into elements smaller than `v[pivot]`, followed by elements greater than or
/// equal to `v[pivot]`.
///
/// Returns a tuple of:
///
/// 1. Number of elements smaller than `v[pivot]`.
/// 2. True if `v` was already partitioned.
fn partition<T, F>(v: &mut [T], pivot: usize, is_less: &mut F) -> (usize, bool)
where F: FnMut(&T, &T) -> bool
{
let (mid, was_partitioned) = {
// Place the pivot at the beginning of slice.
v.swap(0, pivot);
let (pivot, v) = v.split_at_mut(1);
let pivot = &mut pivot[0];
// Read the pivot into a stack-allocated variable for efficiency. If a following comparison
// operation panics, the pivot will be automatically written back into the slice.
let mut tmp = NoDrop { value: unsafe { ptr::read(pivot) } };
let _pivot_guard = CopyOnDrop {
src: unsafe { &mut tmp.value },
dest: pivot,
};
let pivot = unsafe { &tmp.value };
// Find the first pair of out-of-order elements.
let mut l = 0;
let mut r = v.len();
unsafe {
// Find the first element greater then or equal to the pivot.
while l < r && is_less(v.get_unchecked(l), pivot) {
l += 1;
}
// Find the last element lesser that the pivot.
while l < r && !is_less(v.get_unchecked(r - 1), pivot) {
r -= 1;
}
}
(l + partition_in_blocks(&mut v[l..r], pivot, is_less), l >= r)
// `_pivot_guard` goes out of scope and writes the pivot (which is a stack-allocated
// variable) back into the slice where it originally was. This step is critical in ensuring
// safety!
};
// Place the pivot between the two partitions.
v.swap(0, mid);
(mid, was_partitioned)
}
/// Partitions `v` into elements equal to `v[pivot]` followed by elements greater than `v[pivot]`.
///
/// Returns the number of elements equal to the pivot. It is assumed that `v` does not contain
/// elements smaller than the pivot.
fn partition_equal<T, F>(v: &mut [T], pivot: usize, is_less: &mut F) -> usize
where F: FnMut(&T, &T) -> bool
{
// Place the pivot at the beginning of slice.
v.swap(0, pivot);
let (pivot, v) = v.split_at_mut(1);
let pivot = &mut pivot[0];
// Read the pivot into a stack-allocated variable for efficiency. If a following comparison
// operation panics, the pivot will be automatically written back into the slice.
let mut tmp = NoDrop { value: unsafe { ptr::read(pivot) } };
let _pivot_guard = CopyOnDrop {
src: unsafe { &mut tmp.value },
dest: pivot,
};
let pivot = unsafe { &tmp.value };
// Now partition the slice.
let mut l = 0;
let mut r = v.len();
loop {
unsafe {
// Find the first element greater that the pivot.
while l < r && !is_less(pivot, v.get_unchecked(l)) {
l += 1;
}
// Find the last element equal to the pivot.
while l < r && is_less(pivot, v.get_unchecked(r - 1)) {
r -= 1;
}
// Are we done?
if l >= r {
break;
}
// Swap the found pair of out-of-order elements.
r -= 1;
ptr::swap(v.get_unchecked_mut(l), v.get_unchecked_mut(r));
l += 1;
}
}
// We found `l` elements equal to the pivot. Add 1 to account for the pivot itself.
l + 1
// `_pivot_guard` goes out of scope and writes the pivot (which is a stack-allocated variable)
// back into the slice where it originally was. This step is critical in ensuring safety!
}
/// Scatters some elements around in an attempt to break patterns that might cause imbalanced
/// partitions in quicksort.
#[cold]
fn break_patterns<T>(v: &mut [T]) {
let len = v.len();
if len >= 8 {
// A random number will be taken modulo this one. The modulus is a power of two so that we
// can simply take bitwise "and", thus avoiding costly CPU operations.
let modulus = (len / 4).next_power_of_two();
debug_assert!(modulus >= 1 && modulus <= len / 2);
// Pseudorandom number generation from the "Xorshift RNGs" paper by George Marsaglia.
let mut random = len;
random ^= random << 13;
random ^= random >> 17;
random ^= random << 5;
random &= modulus - 1;
debug_assert!(random < len / 2);
// The first index.
let a = len / 4 * 2;
debug_assert!(a >= 1 && a < len - 2);
// The second index.
let b = len / 4 + random;
debug_assert!(b >= 1 && b < len - 2);
// Swap neighbourhoods of `a` and `b`.
for i in 0..3 {
v.swap(a - 1 + i, b - 1 + i);
}
}
}
/// Chooses a pivot in `v` and returns it's index.
///
/// Elements in `v` might be reordered in the process.
fn choose_pivot<T, F>(v: &mut [T], is_less: &mut F) -> usize
where F: FnMut(&T, &T) -> bool
{
// Minimal length to choose the median-of-medians method.
// Shorter slices use the simple median-of-three method.
const SHORTEST_MEDIAN_OF_MEDIANS: usize = 90;
// Maximal number of swaps that can be performed in this function.
const MAX_SWAPS: usize = 4 * 3;
let len = v.len();
// Three indices near which we are going to choose a pivot.
let mut a = len / 4 * 1;
let mut b = len / 4 * 2;
let mut c = len / 4 * 3;
// Counts the total number of swaps we are about to perform while sorting indices.
let mut swaps = 0;
if len >= 8 {
// Swaps indices so that `v[a] <= v[b]`.
let mut sort2 = |a: &mut usize, b: &mut usize| unsafe {
if is_less(v.get_unchecked(*b), v.get_unchecked(*a)) {
ptr::swap(a, b);
swaps += 1;
}
};
// Swaps indices so that `v[a] <= v[b] <= v[c]`.
let mut sort3 = |a: &mut usize, b: &mut usize, c: &mut usize| {
sort2(a, b);
sort2(b, c);
sort2(a, b);
};
if len >= SHORTEST_MEDIAN_OF_MEDIANS {
// Finds the median of `v[a - 1], v[a], v[a + 1]` and stores the index into `a`.
let mut sort_adjacent = |a: &mut usize| {
let tmp = *a;
sort3(&mut (tmp - 1), a, &mut (tmp + 1));
};
// Find medians in the neighborhoods of `a`, `b`, and `c`.
sort_adjacent(&mut a);
sort_adjacent(&mut b);
sort_adjacent(&mut c);
}
// Find the median among `a`, `b`, and `c`.
sort3(&mut a, &mut b, &mut c);
}
if swaps < MAX_SWAPS {
b
} else {
// The maximal number of swaps was performed. Chances are the slice is descending or mostly
// descending, so reversing will probably help sort it faster.
v.reverse();
len - 1 - b
}
}
/// Sorts `v` recursively.
///
/// If the slice had a predecessor in the original array, it is specified as `pred`.
///
/// `limit` is the number of allowed imbalanced partitions before switching to `heapsort`. If zero,
/// this function will immediately switch to heapsort.
fn recurse<'a, T, F>(mut v: &'a mut [T], is_less: &mut F, mut pred: Option<&'a T>, mut limit: usize)
where F: FnMut(&T, &T) -> bool
{
// Slices of up to this length get sorted using insertion sort.
const MAX_INSERTION: usize = 16;
// This is `true` if the last partitioning was balanced.
let mut was_balanced = true;
loop {
let len = v.len();
// Very short slices get sorted using insertion sort.
if len <= MAX_INSERTION {
insertion_sort(v, is_less);
return;
}
// If too many bad pivot choices were made, simply fall back to heapsort in order to
// guarantee `O(n log n)` worst-case.
if limit == 0 {
heapsort(v, is_less);
return;
}
// If the last partitioning was imbalanced, try breaking patterns in the slice by shuffling
// some elements around. Hopefully we'll choose a better pivot this time.
if !was_balanced {
break_patterns(v);
limit -= 1;
}
let pivot = choose_pivot(v, is_less);
// If the chosen pivot is equal to the predecessor, then it's the smallest element in the
// slice. Partition the slice into elements equal to and elements greater than the pivot.
// This case is usually hit when the slice contains many duplicate elements.
if let Some(p) = pred {
if !is_less(p, &v[pivot]) {
let mid = partition_equal(v, pivot, is_less);
// Continue sorting elements greater than the pivot.
v = &mut {v}[mid..];
continue;
}
}
let (mid, was_partitioned) = partition(v, pivot, is_less);
was_balanced = cmp::min(mid, len - mid) >= len / 8;
// If the partitioning is decently balanced and the slice was already partitioned, there
// are good chances it is also completely sorted. If so, we're done.
if was_balanced && was_partitioned && v.windows(2).all(|w| !is_less(&w[1], &w[0])) {
return;
}
// Split the slice into `left`, `pivot`, and `right`.
let (left, right) = {v}.split_at_mut(mid);
let (pivot, right) = right.split_at_mut(1);
let pivot = &pivot[0];
// Recurse into the shorter side only in order to minimize the total number of recursive
// calls and consume less stack space. Then just continue with the longer side (this is
// akin to tail recursion).
if left.len() < right.len() {
recurse(left, is_less, pred, limit);
v = right;
pred = Some(pivot);
} else {
recurse(right, is_less, Some(pivot), limit);
v = left;
}
}
}
/// Sorts `v` using pattern-defeating quicksort, which is `O(n log n)` worst-case.
pub fn quicksort<T, F>(v: &mut [T], mut is_less: F)
where F: FnMut(&T, &T) -> bool
{
// Sorting has no meaningful behavior on zero-sized types.
if mem::size_of::<T>() == 0 {
return;
}
// Limit the number of imbalanced partitions to `floor(log2(len)) + 2`.
let limit = mem::size_of::<usize>() * 8 - v.len().leading_zeros() as usize + 1;
recurse(v, &mut is_less, None, limit);
}

6
src/libcoretest/lib.rs
View file

@ -19,18 +19,22 @@
#![feature(decode_utf8)]
#![feature(fixed_size_array)]
#![feature(flt2dec)]
#![feature(fmt_internals)]
#![feature(libc)]
#![feature(move_cell)]
#![feature(nonzero)]
#![feature(ordering_chaining)]
#![feature(ptr_unaligned)]
#![feature(rand)]
#![feature(raw)]
#![feature(sip_hash_13)]
#![feature(slice_patterns)]
#![feature(sort_unstable)]
#![feature(step_by)]
#![feature(test)]
#![feature(try_from)]
#![feature(unicode)]
#![feature(unique)]
#![feature(fmt_internals)]
extern crate core;
extern crate test;

41
src/libcoretest/slice.rs
View file

@ -9,6 +9,7 @@
// except according to those terms.
use core::result::Result::{Ok, Err};
use rand::{Rng, XorShiftRng};
#[test]
fn test_binary_search() {
@ -139,9 +140,6 @@ fn test_chunks_mut_last() {
assert_eq!(c2.last().unwrap()[0], 4);
}
#[test]
fn test_windows_count() {
let v: &[i32] = &[0, 1, 2, 3, 4, 5];
@ -224,3 +222,40 @@ fn get_unchecked_mut_range() {
assert_eq!(v.get_unchecked_mut(1..4), &mut [1, 2, 3][..]);
}
}
#[test]
fn sort_unstable() {
let mut v = [0; 600];
let mut v1 = [0; 600];
let mut rng = XorShiftRng::new_unseeded();
for len in (2..25).chain(500..510) {
for &modulus in &[10, 1000] {
for _ in 0..100 {
for i in 0..len {
let num = rng.gen::<i32>() % modulus;
v[i] = num;
v1[i] = num;
}
v.sort_unstable();
assert!(v.windows(2).all(|w| w[0] <= w[1]));
v1.sort_unstable_by(|a, b| a.cmp(b));
assert!(v1.windows(2).all(|w| w[0] <= w[1]));
v1.sort_unstable_by(|a, b| b.cmp(a));
assert!(v1.windows(2).all(|w| w[0] >= w[1]));
}
}
}
// Should not panic.
[0i32; 0].sort_unstable();
[(); 10].sort_unstable();
[(); 100].sort_unstable();
let mut v = [0xDEADBEEFu64];
v.sort_unstable();
assert!(v == [0xDEADBEEF]);
}