Auto merge of #40601 - stjepang:sort-unstable, r=alexcrichton

Implement feature sort_unstable

Tracking issue for the feature: #40585

This is essentially integration of [pdqsort](https://github.com/stjepang/pdqsort) into libcore.

There's plenty of unsafe blocks to review. The heart of pdqsort is `fn partition_in_blocks` and is probably the most challenging function to understand. It requires some patience, but let me know if you find it too difficult - comments could always be improved.

#### Changes

* Added `sort_unstable` feature.
* Tweaked insertion sort constants for stable sort. Sorting integers is now up to 5% slower, but sorting big elements is much faster (in particular, `sort_large_big_random` is 35% faster). The old constants were highly optimized for sorting integers, so overall the configuration is more balanced now. A minor regression in case of integers is forgivable as we recently had performance improvements (#39538) that completely make up for it.
* Removed some uninteresting sort benchmarks.
* Added a new sort benchmark for string sorting.

#### Benchmarks

The following table compares stable and unstable sorting:
```
name                                 stable ns/iter        unstable ns/iter     diff ns/iter   diff %
slice::sort_large_ascending          7,240 (11049 MB/s)    7,380 (10840 MB/s)            140    1.93%
slice::sort_large_big_random         1,454,138 (880 MB/s)  910,269 (1406 MB/s)      -543,869  -37.40%
slice::sort_large_descending         13,450 (5947 MB/s)    10,895 (7342 MB/s)         -2,555  -19.00%
slice::sort_large_mostly_ascending   204,041 (392 MB/s)    88,639 (902 MB/s)        -115,402  -56.56%
slice::sort_large_mostly_descending  217,109 (368 MB/s)    99,009 (808 MB/s)        -118,100  -54.40%
slice::sort_large_random             477,257 (167 MB/s)    346,028 (231 MB/s)       -131,229  -27.50%
slice::sort_large_random_expensive   21,670,537 (3 MB/s)   22,710,238 (3 MB/s)     1,039,701    4.80%
slice::sort_large_strings            6,284,499 (38 MB/s)   6,410,896 (37 MB/s)       126,397    2.01%
slice::sort_medium_random            3,515 (227 MB/s)      3,327 (240 MB/s)             -188   -5.35%
slice::sort_small_ascending          42 (1904 MB/s)        41 (1951 MB/s)                 -1   -2.38%
slice::sort_small_big_random         503 (2544 MB/s)       514 (2490 MB/s)                11    2.19%
slice::sort_small_descending         72 (1111 MB/s)        69 (1159 MB/s)                 -3   -4.17%
slice::sort_small_random             369 (216 MB/s)        367 (217 MB/s)                 -2   -0.54%
```

Interesting cases:
* Expensive comparison function and string sorting - it's a really close race, but timsort performs a slightly smaller number of comparisons. This is a natural difference of bottom-up merging versus top-down partitioning.
* `large_descending` - unstable sort is faster, but both sorts should have equivalent performance. Both just check whether the slice is descending and if so, they reverse it. I blame LLVM for the discrepancy.

r? @alexcrichton

Author: bors

src/libcollections/benches/lib.rs

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

src/libcollections/benches/slice.rs

@@ -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_big_random(len: usize) -> Vec<[u64; 16]> {
+fn gen_strings(len: usize) -> Vec<String> {
     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()
+    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_descending(len: usize) -> Vec<[u64; 16]> {
-    (0..len as u64).rev().map(|x| [x; 16]).take(len).collect()
+fn gen_big_random(len: usize) -> Vec<[u64; 16]> {
+    let mut rng = thread_rng();
+    rng.gen_iter().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);
-
-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_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);
+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;
+        }
+    }
+}
 
-#[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, 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!(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);

src/libcollections/lib.rs

@@ -52,6 +52,7 @@
 #![feature(shared)]
 #![feature(slice_get_slice)]
 #![feature(slice_patterns)]
+#![cfg_attr(not(test), feature(sort_unstable))]
 #![feature(specialization)]
 #![feature(staged_api)]
 #![feature(str_internals)]

src/libcollections/slice.rs

@@ -1092,6 +1092,39 @@ impl<T> [T] {
         merge_sort(self, |a, b| a.lt(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.
+    ///
+    /// # 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 = [5, 4, 1, 3, 2];
+    /// v.sort_by(|a, b| a.cmp(b));
+    /// assert!(v == [1, 2, 3, 4, 5]);
+    ///
+    /// // reverse sorting
+    /// v.sort_by(|a, b| b.cmp(a));
+    /// assert!(v == [5, 4, 3, 2, 1]);
+    /// ```
+    #[stable(feature = "rust1", since = "1.0.0")]
+    #[inline]
+    pub fn sort_by<F>(&mut self, mut compare: F)
+        where F: FnMut(&T, &T) -> Ordering
+    {
+        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.
@@ -1122,37 +1155,118 @@ impl<T> [T] {
         merge_sort(self, |a, b| f(a).lt(&f(b)));
     }
 
-    /// Sorts the slice using `compare` to compare elements.
+    /// Sorts the slice, but may not preserve the order of equal elements.
     ///
-    /// This sort is stable (i.e. does not reorder equal elements) and `O(n log n)` worst-case.
+    /// 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 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.
+    /// 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.
     ///
-    /// Also, it allocates temporary storage half the size of `self`, but for short slices a
-    /// non-allocating insertion sort is used instead.
+    /// 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
     ///
     /// ```
+    /// #![feature(sort_unstable)]
+    ///
+    /// 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
+    ///
+    /// ```
+    /// #![feature(sort_unstable)]
+    ///
     /// let mut v = [5, 4, 1, 3, 2];
-    /// v.sort_by(|a, b| a.cmp(b));
+    /// v.sort_unstable_by(|a, b| a.cmp(b));
     /// assert!(v == [1, 2, 3, 4, 5]);
     ///
     /// // reverse sorting
-    /// v.sort_by(|a, b| b.cmp(a));
+    /// v.sort_unstable_by(|a, b| b.cmp(a));
     /// assert!(v == [5, 4, 3, 2, 1]);
     /// ```
-    #[stable(feature = "rust1", since = "1.0.0")]
+    ///
+    /// [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_by<F>(&mut self, mut compare: F)
+    pub fn sort_unstable_by<F>(&mut self, compare: F)
         where F: FnMut(&T, &T) -> Ordering
     {
-        merge_sort(self, |a, b| compare(a, b) == Less);
+        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
+    ///
+    /// ```
+    /// #![feature(sort_unstable)]
+    ///
+    /// 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`.
@@ -1553,28 +1667,20 @@ 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 = 20;
+    // Very short runs are extended using insertion sort to span at least this many elements.
+    const MIN_RUN: usize = 10;
+
     // 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 +1724,7 @@ 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);
         }

src/libcollectionstest/slice.rs

@@ -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] = [];