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// Copyright 2014-2016 bluss and ndarray developers.
//
// 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.
use std::cmp;
use crate::LinalgScalar;
/// Fold over the manually unrolled `xs` with `f`
pub fn unrolled_fold<A, I, F>(mut xs: &[A], init: I, f: F) -> A
where
A: Clone,
I: Fn() -> A,
F: Fn(A, A) -> A,
{
// eightfold unrolled so that floating point can be vectorized
// (even with strict floating point accuracy semantics)
let mut acc = init();
let (mut p0, mut p1, mut p2, mut p3, mut p4, mut p5, mut p6, mut p7) = (
init(),
init(),
init(),
init(),
init(),
init(),
init(),
init(),
);
while xs.len() >= 8 {
p0 = f(p0, xs[0].clone());
p1 = f(p1, xs[1].clone());
p2 = f(p2, xs[2].clone());
p3 = f(p3, xs[3].clone());
p4 = f(p4, xs[4].clone());
p5 = f(p5, xs[5].clone());
p6 = f(p6, xs[6].clone());
p7 = f(p7, xs[7].clone());
xs = &xs[8..];
}
acc = f(acc.clone(), f(p0, p4));
acc = f(acc.clone(), f(p1, p5));
acc = f(acc.clone(), f(p2, p6));
acc = f(acc.clone(), f(p3, p7));
// make it clear to the optimizer that this loop is short
// and can not be autovectorized.
for (i, x) in xs.iter().enumerate() {
if i >= 7 {
break;
}
acc = f(acc.clone(), x.clone())
}
acc
}
/// Compute the dot product.
///
/// `xs` and `ys` must be the same length
pub fn unrolled_dot<A>(xs: &[A], ys: &[A]) -> A
where
A: LinalgScalar,
{
debug_assert_eq!(xs.len(), ys.len());
// eightfold unrolled so that floating point can be vectorized
// (even with strict floating point accuracy semantics)
let len = cmp::min(xs.len(), ys.len());
let mut xs = &xs[..len];
let mut ys = &ys[..len];
let mut sum = A::zero();
let (mut p0, mut p1, mut p2, mut p3, mut p4, mut p5, mut p6, mut p7) = (
A::zero(),
A::zero(),
A::zero(),
A::zero(),
A::zero(),
A::zero(),
A::zero(),
A::zero(),
);
while xs.len() >= 8 {
p0 = p0 + xs[0] * ys[0];
p1 = p1 + xs[1] * ys[1];
p2 = p2 + xs[2] * ys[2];
p3 = p3 + xs[3] * ys[3];
p4 = p4 + xs[4] * ys[4];
p5 = p5 + xs[5] * ys[5];
p6 = p6 + xs[6] * ys[6];
p7 = p7 + xs[7] * ys[7];
xs = &xs[8..];
ys = &ys[8..];
}
sum = sum + (p0 + p4);
sum = sum + (p1 + p5);
sum = sum + (p2 + p6);
sum = sum + (p3 + p7);
for (i, (&x, &y)) in xs.iter().zip(ys).enumerate() {
if i >= 7 {
break;
}
sum = sum + x * y;
}
sum
}
/// Compute pairwise equality
///
/// `xs` and `ys` must be the same length
pub fn unrolled_eq<A, B>(xs: &[A], ys: &[B]) -> bool
where
A: PartialEq<B>,
{
debug_assert_eq!(xs.len(), ys.len());
// eightfold unrolled for performance (this is not done by llvm automatically)
let len = cmp::min(xs.len(), ys.len());
let mut xs = &xs[..len];
let mut ys = &ys[..len];
while xs.len() >= 8 {
if (xs[0] != ys[0])
| (xs[1] != ys[1])
| (xs[2] != ys[2])
| (xs[3] != ys[3])
| (xs[4] != ys[4])
| (xs[5] != ys[5])
| (xs[6] != ys[6])
| (xs[7] != ys[7])
{
return false;
}
xs = &xs[8..];
ys = &ys[8..];
}
for i in 0..xs.len() {
if xs[i] != ys[i] {
return false;
}
}
true
}