2021-01-27 15:20:00 -06:00
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//! These helpers provide a way to easily emulate a vectorized SIMD op on two SIMD vectors,
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//! except using scalar ops that iterate through each lane, one at a time, so as to remove
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//! the vagaries of compilation.
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//!
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//! Do note, however, that when testing that vectorized operations #[should_panic], these
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//! "scalarized SIMD ops" will trigger scalar code paths that may also normally panic.
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2020-10-11 13:32:46 -05:00
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pub fn apply_unary_lanewise<T1: Copy, T2: Copy, V1: AsRef<[T1]>, V2: AsMut<[T2]> + Default>(
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x: V1,
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f: impl Fn(T1) -> T2,
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) -> V2 {
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let mut y = V2::default();
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assert_eq!(x.as_ref().len(), y.as_mut().len());
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for (x, y) in x.as_ref().iter().zip(y.as_mut().iter_mut()) {
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*y = f(*x);
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2020-10-01 21:50:15 -05:00
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}
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2020-10-11 13:32:46 -05:00
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y
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2020-10-01 21:50:15 -05:00
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}
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pub fn apply_binary_lanewise<T: Copy, V: AsRef<[T]> + AsMut<[T]> + Default>(
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a: V,
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b: V,
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f: impl Fn(T, T) -> T,
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) -> V {
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let mut out = V::default();
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let out_slice = out.as_mut();
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let a_slice = a.as_ref();
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let b_slice = b.as_ref();
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for (o, (a, b)) in out_slice.iter_mut().zip(a_slice.iter().zip(b_slice.iter())) {
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*o = f(*a, *b);
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}
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out
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}
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pub fn apply_binary_scalar_rhs_lanewise<T: Copy, V: AsRef<[T]> + AsMut<[T]> + Default>(
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a: V,
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b: T,
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f: impl Fn(T, T) -> T,
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) -> V {
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let mut out = V::default();
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let out_slice = out.as_mut();
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let a_slice = a.as_ref();
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for (o, a) in out_slice.iter_mut().zip(a_slice.iter()) {
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*o = f(*a, b);
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}
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out
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}
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pub fn apply_binary_scalar_lhs_lanewise<T: Copy, V: AsRef<[T]> + AsMut<[T]> + Default>(
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a: T,
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b: V,
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f: impl Fn(T, T) -> T,
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) -> V {
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let mut out = V::default();
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let out_slice = out.as_mut();
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let b_slice = b.as_ref();
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for (o, b) in out_slice.iter_mut().zip(b_slice.iter()) {
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*o = f(a, *b);
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}
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out
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}
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