libcore: sort_unstable: improve randomization in break_patterns.
Select 3 random points instead of just 1. Also the code now compiles on 16bit architectures.
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@ -498,32 +498,40 @@ fn partition_equal<T, F>(v: &mut [T], pivot: usize, is_less: &mut F) -> usize
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#[cold]
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fn break_patterns<T>(v: &mut [T]) {
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let len = v.len();
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if len >= 8 {
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// A random number will be taken modulo this one. The modulus is a power of two so that we
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// can simply take bitwise "and", thus avoiding costly CPU operations.
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let modulus = (len / 4).next_power_of_two();
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debug_assert!(modulus >= 1 && modulus <= len / 2);
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// Pseudorandom number generator from the "Xorshift RNGs" paper by George Marsaglia.
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let mut random = len as u32;
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let mut gen_u32 = || {
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random ^= random << 13;
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random ^= random >> 17;
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random ^= random << 5;
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random
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};
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let mut gen_usize = || {
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if mem::size_of::<usize>() <= 4 {
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gen_u32() as usize
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} else {
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(((gen_u32() as u64) << 32) | (gen_u32() as u64)) as usize
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}
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};
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// Pseudorandom number generation from the "Xorshift RNGs" paper by George Marsaglia.
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let mut random = len;
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random ^= random << 13;
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random ^= random >> 17;
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random ^= random << 5;
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random &= modulus - 1;
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debug_assert!(random < len / 2);
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// Take random numbers modulo this number.
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// The number fits into `usize` because `len` is not greater than `isize::MAX`.
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let modulus = len.next_power_of_two();
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// The first index.
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let a = len / 4 * 2;
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debug_assert!(a >= 1 && a < len - 2);
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// Some pivot candidates will be in the nearby of this index. Let's randomize them.
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let pos = len / 4 * 2;
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// The second index.
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let b = len / 4 + random;
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debug_assert!(b >= 1 && b < len - 2);
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// Swap neighbourhoods of `a` and `b`.
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for i in 0..3 {
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v.swap(a - 1 + i, b - 1 + i);
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// Generate a random number modulo `len`. However, in order to avoid costly operations
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// we first take it modulo a power of two, and then decrease by `len` until it fits
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// into the range `[0, len - 1]`.
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let mut other = gen_usize() & (modulus - 1);
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while other >= len {
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other -= len;
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}
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v.swap(pos - 1 + i, other);
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}
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}
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}
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