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Diffstat (limited to 'src/slice/quicksort.rs')
-rw-r--r-- | src/slice/quicksort.rs | 800 |
1 files changed, 800 insertions, 0 deletions
diff --git a/src/slice/quicksort.rs b/src/slice/quicksort.rs new file mode 100644 index 0000000..b985073 --- /dev/null +++ b/src/slice/quicksort.rs @@ -0,0 +1,800 @@ +//! Parallel quicksort. +//! +//! This implementation is copied verbatim from `std::slice::sort_unstable` and then parallelized. +//! The only difference from the original is that calls to `recurse` are executed in parallel using +//! `rayon_core::join`. + +use std::cmp; +use std::mem; +use std::ptr; + +/// When dropped, takes the value out of `Option` and writes it into `dest`. +/// +/// This allows us to safely read the pivot into a stack-allocated variable for efficiency, and +/// write it back into the slice after partitioning. This way we ensure that the write happens +/// even if `is_less` panics in the meantime. +struct WriteOnDrop<T> { + value: Option<T>, + dest: *mut T, +} + +impl<T> Drop for WriteOnDrop<T> { + fn drop(&mut self) { + unsafe { + ptr::write(self.dest, self.value.take().unwrap()); + } + } +} + +/// Holds a value, but never drops it. +struct NoDrop<T> { + value: Option<T>, +} + +impl<T> Drop for NoDrop<T> { + fn drop(&mut self) { + mem::forget(self.value.take()); + } +} + +/// 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); + } + } +} + +/// Shifts the first element to the right until it encounters a greater or equal element. +fn shift_head<T, F>(v: &mut [T], is_less: &F) +where + F: Fn(&T, &T) -> bool, +{ + let len = v.len(); + unsafe { + // If the first two elements are out-of-order... + if len >= 2 && is_less(v.get_unchecked(1), v.get_unchecked(0)) { + // Read the first element into a stack-allocated variable. If a following comparison + // operation panics, `hole` will get dropped and automatically write the element back + // into the slice. + let mut tmp = NoDrop { + value: Some(ptr::read(v.get_unchecked(0))), + }; + let mut hole = CopyOnDrop { + src: tmp.value.as_mut().unwrap(), + dest: v.get_unchecked_mut(1), + }; + ptr::copy_nonoverlapping(v.get_unchecked(1), v.get_unchecked_mut(0), 1); + + for i in 2..len { + if !is_less(v.get_unchecked(i), tmp.value.as_ref().unwrap()) { + break; + } + + // Move `i`-th element one place to the left, thus shifting the hole to the right. + ptr::copy_nonoverlapping(v.get_unchecked(i), v.get_unchecked_mut(i - 1), 1); + hole.dest = v.get_unchecked_mut(i); + } + // `hole` gets dropped and thus copies `tmp` into the remaining hole in `v`. + } + } +} + +/// Shifts the last element to the left until it encounters a smaller or equal element. +fn shift_tail<T, F>(v: &mut [T], is_less: &F) +where + F: Fn(&T, &T) -> bool, +{ + let len = v.len(); + unsafe { + // If the last two elements are out-of-order... + if len >= 2 && is_less(v.get_unchecked(len - 1), v.get_unchecked(len - 2)) { + // Read the last element into a stack-allocated variable. If a following comparison + // operation panics, `hole` will get dropped and automatically write the element back + // into the slice. + let mut tmp = NoDrop { + value: Some(ptr::read(v.get_unchecked(len - 1))), + }; + let mut hole = CopyOnDrop { + src: tmp.value.as_mut().unwrap(), + dest: v.get_unchecked_mut(len - 2), + }; + ptr::copy_nonoverlapping(v.get_unchecked(len - 2), v.get_unchecked_mut(len - 1), 1); + + for i in (0..len - 2).rev() { + if !is_less(&tmp.value.as_ref().unwrap(), v.get_unchecked(i)) { + break; + } + + // Move `i`-th element one place to the right, thus shifting the hole to the left. + ptr::copy_nonoverlapping(v.get_unchecked(i), v.get_unchecked_mut(i + 1), 1); + hole.dest = v.get_unchecked_mut(i); + } + // `hole` gets dropped and thus copies `tmp` into the remaining hole in `v`. + } + } +} + +/// Partially sorts a slice by shifting several out-of-order elements around. +/// +/// Returns `true` if the slice is sorted at the end. This function is `O(n)` worst-case. +#[cold] +fn partial_insertion_sort<T, F>(v: &mut [T], is_less: &F) -> bool +where + F: Fn(&T, &T) -> bool, +{ + // Maximum number of adjacent out-of-order pairs that will get shifted. + const MAX_STEPS: usize = 5; + // If the slice is shorter than this, don't shift any elements. + const SHORTEST_SHIFTING: usize = 50; + + let len = v.len(); + let mut i = 1; + + for _ in 0..MAX_STEPS { + unsafe { + // Find the next pair of adjacent out-of-order elements. + while i < len && !is_less(v.get_unchecked(i), v.get_unchecked(i - 1)) { + i += 1; + } + } + + // Are we done? + if i == len { + return true; + } + + // Don't shift elements on short arrays, that has a performance cost. + if len < SHORTEST_SHIFTING { + return false; + } + + // Swap the found pair of elements. This puts them in correct order. + v.swap(i - 1, i); + + // Shift the smaller element to the left. + shift_tail(&mut v[..i], is_less); + // Shift the greater element to the right. + shift_head(&mut v[i..], is_less); + } + + // Didn't manage to sort the slice in the limited number of steps. + false +} + +/// Sorts a slice using insertion sort, which is `O(n^2)` worst-case. +fn insertion_sort<T, F>(v: &mut [T], is_less: &F) +where + F: Fn(&T, &T) -> bool, +{ + for i in 1..v.len() { + shift_tail(&mut v[..=i], is_less); + } +} + +/// Sorts `v` using heapsort, which guarantees `O(n log n)` worst-case. +#[cold] +fn heapsort<T, F>(v: &mut [T], is_less: &F) +where + F: Fn(&T, &T) -> bool, +{ + // This binary heap respects the invariant `parent >= child`. + let 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: &F) -> usize +where + F: Fn(&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 smaller 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 (from `l` to `l.offset(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 = [0u8; BLOCK]; + + // The current block on the right side (from `r.offset(-block_r)` to `r`). + let mut r = unsafe { l.add(v.len()) }; + let mut block_r = BLOCK; + let mut start_r = ptr::null_mut(); + let mut end_r = ptr::null_mut(); + let mut offsets_r = [0u8; BLOCK]; + + // 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.add(block_l) }; + } + + if start_r == end_r { + // All out-of-order elements in the right block were moved. Move to the previous block. + r = unsafe { r.sub(block_r) }; + } + + 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: &F) -> (usize, bool) +where + F: Fn(&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 write_on_drop = WriteOnDrop { + value: unsafe { Some(ptr::read(pivot)) }, + dest: pivot, + }; + let pivot = write_on_drop.value.as_ref().unwrap(); + + // 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 smaller 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, + ) + + // `write_on_drop` 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: &F) -> usize +where + F: Fn(&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 write_on_drop = WriteOnDrop { + value: unsafe { Some(ptr::read(pivot)) }, + dest: pivot, + }; + let pivot = write_on_drop.value.as_ref().unwrap(); + + // 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 + + // `write_on_drop` 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 { + // Pseudorandom number generator from the "Xorshift RNGs" paper by George Marsaglia. + let mut random = len as u32; + let mut gen_u32 = || { + random ^= random << 13; + random ^= random >> 17; + random ^= random << 5; + random + }; + let mut gen_usize = || { + if mem::size_of::<usize>() <= 4 { + gen_u32() as usize + } else { + ((u64::from(gen_u32()) << 32) | u64::from(gen_u32())) as usize + } + }; + + // Take random numbers modulo this number. + // The number fits into `usize` because `len` is not greater than `isize::MAX`. + let modulus = len.next_power_of_two(); + + // Some pivot candidates will be in the nearby of this index. Let's randomize them. + let pos = len / 4 * 2; + + for i in 0..3 { + // Generate a random number modulo `len`. However, in order to avoid costly operations + // we first take it modulo a power of two, and then decrease by `len` until it fits + // into the range `[0, len - 1]`. + let mut other = gen_usize() & (modulus - 1); + + // `other` is guaranteed to be less than `2 * len`. + if other >= len { + other -= len; + } + + v.swap(pos - 1 + i, other); + } + } +} + +/// Chooses a pivot in `v` and returns the index and `true` if the slice is likely already sorted. +/// +/// Elements in `v` might be reordered in the process. +fn choose_pivot<T, F>(v: &mut [T], is_less: &F) -> (usize, bool) +where + F: Fn(&T, &T) -> bool, +{ + // Minimum length to choose the median-of-medians method. + // Shorter slices use the simple median-of-three method. + const SHORTEST_MEDIAN_OF_MEDIANS: usize = 50; + // Maximum 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, swaps == 0) + } else { + // The maximum 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, true) + } +} + +/// 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: &F, mut pred: Option<&'a mut T>, mut limit: usize) +where + T: Send, + F: Fn(&T, &T) -> bool + Sync, +{ + // Slices of up to this length get sorted using insertion sort. + const MAX_INSERTION: usize = 20; + // If both partitions are up to this length, we continue sequentially. This number is as small + // as possible but so that the overhead of Rayon's task scheduling is still negligible. + const MAX_SEQUENTIAL: usize = 2000; + + // True if the last partitioning was reasonably balanced. + let mut was_balanced = true; + // True if the last partitioning didn't shuffle elements (the slice was already partitioned). + let mut was_partitioned = 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; + } + + // Choose a pivot and try guessing whether the slice is already sorted. + let (pivot, likely_sorted) = choose_pivot(v, is_less); + + // If the last partitioning was decently balanced and didn't shuffle elements, and if pivot + // selection predicts the slice is likely already sorted... + if was_balanced && was_partitioned && likely_sorted { + // Try identifying several out-of-order elements and shifting them to correct + // positions. If the slice ends up being completely sorted, we're done. + if partial_insertion_sort(v, is_less) { + return; + } + } + + // 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(ref 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; + } + } + + // Partition the slice. + let (mid, was_p) = partition(v, pivot, is_less); + was_balanced = cmp::min(mid, len - mid) >= len / 8; + was_partitioned = was_p; + + // 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 = &mut pivot[0]; + + if cmp::max(left.len(), right.len()) <= MAX_SEQUENTIAL { + // 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; + } + } else { + // Sort the left and right half in parallel. + rayon_core::join( + || recurse(left, is_less, pred, limit), + || recurse(right, is_less, Some(pivot), limit), + ); + break; + } + } +} + +/// Sorts `v` using pattern-defeating quicksort in parallel. +/// +/// The algorithm is unstable, in-place, and `O(n log n)` worst-case. +pub(super) fn par_quicksort<T, F>(v: &mut [T], is_less: F) +where + T: Send, + F: Fn(&T, &T) -> bool + Sync, +{ + // 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)) + 1`. + let limit = mem::size_of::<usize>() * 8 - v.len().leading_zeros() as usize; + + recurse(v, &is_less, None, limit); +} + +#[cfg(test)] +mod tests { + use super::heapsort; + use rand::distributions::Uniform; + use rand::{thread_rng, Rng}; + + #[test] + fn test_heapsort() { + let rng = thread_rng(); + + for len in (0..25).chain(500..501) { + for &modulus in &[5, 10, 100] { + let dist = Uniform::new(0, modulus); + for _ in 0..100 { + let v: Vec<i32> = rng.sample_iter(&dist).take(len).collect(); + + // Test heapsort using `<` operator. + let mut tmp = v.clone(); + heapsort(&mut tmp, &|a, b| a < b); + assert!(tmp.windows(2).all(|w| w[0] <= w[1])); + + // Test heapsort using `>` operator. + let mut tmp = v.clone(); + heapsort(&mut tmp, &|a, b| a > b); + assert!(tmp.windows(2).all(|w| w[0] >= w[1])); + } + } + } + + // Sort using a completely random comparison function. + // This will reorder the elements *somehow*, but won't panic. + let mut v: Vec<_> = (0..100).collect(); + heapsort(&mut v, &|_, _| thread_rng().gen()); + heapsort(&mut v, &|a, b| a < b); + + for i in 0..v.len() { + assert_eq!(v[i], i); + } + } +} |