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b00666ed09
This also updates the existing iter::Copied::next_chunk benchmark so that the thing it benches doesn't get masked by the ArrayChunks specialization
440 lines
10 KiB
Rust
440 lines
10 KiB
Rust
use core::borrow::Borrow;
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use core::iter::*;
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use core::mem;
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use core::num::Wrapping;
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use test::{black_box, Bencher};
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#[bench]
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fn bench_rposition(b: &mut Bencher) {
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let it: Vec<usize> = (0..300).collect();
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b.iter(|| {
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it.iter().rposition(|&x| x <= 150);
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});
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}
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#[bench]
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fn bench_skip_while(b: &mut Bencher) {
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b.iter(|| {
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let it = 0..100;
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let mut sum = 0;
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it.skip_while(|&x| {
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sum += x;
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sum < 4000
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})
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.all(|_| true);
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});
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}
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#[bench]
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fn bench_multiple_take(b: &mut Bencher) {
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let mut it = (0..42).cycle();
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b.iter(|| {
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let n = it.next().unwrap();
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for _ in 0..n {
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it.clone().take(it.next().unwrap()).all(|_| true);
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}
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});
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}
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fn scatter(x: i32) -> i32 {
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(x * 31) % 127
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}
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#[bench]
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fn bench_max_by_key(b: &mut Bencher) {
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b.iter(|| {
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let it = 0..100;
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it.map(black_box).max_by_key(|&x| scatter(x))
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})
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}
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// https://www.reddit.com/r/rust/comments/31syce/using_iterators_to_find_the_index_of_the_min_or/
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#[bench]
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fn bench_max_by_key2(b: &mut Bencher) {
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fn max_index_iter(array: &[i32]) -> usize {
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array.iter().enumerate().max_by_key(|&(_, item)| item).unwrap().0
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}
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let mut data = vec![0; 1638];
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data[514] = 9999;
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b.iter(|| max_index_iter(&data));
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}
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#[bench]
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fn bench_max(b: &mut Bencher) {
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b.iter(|| {
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let it = 0..100;
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it.map(black_box).map(scatter).max()
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})
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}
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pub fn copy_zip(xs: &[u8], ys: &mut [u8]) {
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for (a, b) in ys.iter_mut().zip(xs) {
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*a = *b;
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}
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}
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pub fn add_zip(xs: &[f32], ys: &mut [f32]) {
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for (a, b) in ys.iter_mut().zip(xs) {
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*a += *b;
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}
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}
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#[bench]
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fn bench_zip_copy(b: &mut Bencher) {
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let source = vec![0u8; 16 * 1024];
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let mut dst = black_box(vec![0u8; 16 * 1024]);
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b.iter(|| copy_zip(&source, &mut dst))
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}
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#[bench]
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fn bench_zip_add(b: &mut Bencher) {
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let source = vec![1.; 16 * 1024];
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let mut dst = vec![0.; 16 * 1024];
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b.iter(|| add_zip(&source, &mut dst));
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}
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/// `Iterator::for_each` implemented as a plain loop.
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fn for_each_loop<I, F>(iter: I, mut f: F)
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where
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I: Iterator,
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F: FnMut(I::Item),
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{
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for item in iter {
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f(item);
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}
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}
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/// `Iterator::for_each` implemented with `fold` for internal iteration.
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/// (except when `by_ref()` effectively disables that optimization.)
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fn for_each_fold<I, F>(iter: I, mut f: F)
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where
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I: Iterator,
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F: FnMut(I::Item),
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{
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iter.fold((), move |(), item| f(item));
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}
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#[bench]
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fn bench_for_each_chain_loop(b: &mut Bencher) {
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b.iter(|| {
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let mut acc = 0;
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let iter = (0i64..1000000).chain(0..1000000).map(black_box);
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for_each_loop(iter, |x| acc += x);
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acc
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});
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}
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#[bench]
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fn bench_for_each_chain_fold(b: &mut Bencher) {
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b.iter(|| {
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let mut acc = 0;
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let iter = (0i64..1000000).chain(0..1000000).map(black_box);
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for_each_fold(iter, |x| acc += x);
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acc
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});
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}
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#[bench]
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fn bench_for_each_chain_ref_fold(b: &mut Bencher) {
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b.iter(|| {
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let mut acc = 0;
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let mut iter = (0i64..1000000).chain(0..1000000).map(black_box);
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for_each_fold(iter.by_ref(), |x| acc += x);
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acc
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});
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}
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/// Helper to benchmark `sum` for iterators taken by value which
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/// can optimize `fold`, and by reference which cannot.
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macro_rules! bench_sums {
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($bench_sum:ident, $bench_ref_sum:ident, $iter:expr) => {
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#[bench]
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fn $bench_sum(b: &mut Bencher) {
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b.iter(|| -> i64 { $iter.map(black_box).sum() });
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}
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#[bench]
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fn $bench_ref_sum(b: &mut Bencher) {
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b.iter(|| -> i64 { $iter.map(black_box).by_ref().sum() });
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}
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};
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}
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bench_sums! {
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bench_flat_map_sum,
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bench_flat_map_ref_sum,
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(0i64..1000).flat_map(|x| x..x+1000)
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}
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bench_sums! {
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bench_flat_map_chain_sum,
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bench_flat_map_chain_ref_sum,
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(0i64..1000000).flat_map(|x| once(x).chain(once(x)))
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}
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bench_sums! {
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bench_enumerate_sum,
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bench_enumerate_ref_sum,
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(0i64..1000000).enumerate().map(|(i, x)| x * i as i64)
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}
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bench_sums! {
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bench_enumerate_chain_sum,
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bench_enumerate_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).enumerate().map(|(i, x)| x * i as i64)
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}
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bench_sums! {
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bench_filter_sum,
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bench_filter_ref_sum,
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(0i64..1000000).filter(|x| x % 3 == 0)
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}
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bench_sums! {
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bench_filter_chain_sum,
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bench_filter_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).filter(|x| x % 3 == 0)
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}
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bench_sums! {
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bench_filter_map_sum,
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bench_filter_map_ref_sum,
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(0i64..1000000).filter_map(|x| x.checked_mul(x))
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}
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bench_sums! {
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bench_filter_map_chain_sum,
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bench_filter_map_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).filter_map(|x| x.checked_mul(x))
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}
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bench_sums! {
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bench_fuse_sum,
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bench_fuse_ref_sum,
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(0i64..1000000).fuse()
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}
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bench_sums! {
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bench_fuse_chain_sum,
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bench_fuse_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).fuse()
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}
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bench_sums! {
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bench_inspect_sum,
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bench_inspect_ref_sum,
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(0i64..1000000).inspect(|_| {})
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}
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bench_sums! {
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bench_inspect_chain_sum,
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bench_inspect_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).inspect(|_| {})
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}
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bench_sums! {
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bench_peekable_sum,
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bench_peekable_ref_sum,
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(0i64..1000000).peekable()
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}
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bench_sums! {
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bench_peekable_chain_sum,
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bench_peekable_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).peekable()
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}
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bench_sums! {
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bench_skip_sum,
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bench_skip_ref_sum,
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(0i64..1000000).skip(1000)
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}
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bench_sums! {
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bench_skip_chain_sum,
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bench_skip_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).skip(1000)
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}
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bench_sums! {
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bench_skip_while_sum,
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bench_skip_while_ref_sum,
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(0i64..1000000).skip_while(|&x| x < 1000)
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}
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bench_sums! {
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bench_skip_while_chain_sum,
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bench_skip_while_chain_ref_sum,
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(0i64..1000000).chain(0..1000000).skip_while(|&x| x < 1000)
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}
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bench_sums! {
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bench_take_while_chain_sum,
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bench_take_while_chain_ref_sum,
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(0i64..1000000).chain(1000000..).take_while(|&x| x < 1111111)
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}
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bench_sums! {
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bench_cycle_take_sum,
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bench_cycle_take_ref_sum,
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(0..10000).cycle().take(1000000)
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}
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bench_sums! {
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bench_cycle_skip_take_sum,
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bench_cycle_skip_take_ref_sum,
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(0..100000).cycle().skip(1000000).take(1000000)
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}
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bench_sums! {
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bench_cycle_take_skip_sum,
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bench_cycle_take_skip_ref_sum,
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(0..100000).cycle().take(1000000).skip(100000)
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}
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bench_sums! {
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bench_skip_cycle_skip_zip_add_sum,
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bench_skip_cycle_skip_zip_add_ref_sum,
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(0..100000).skip(100).cycle().skip(100)
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.zip((0..100000).cycle().skip(10))
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.map(|(a,b)| a+b)
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.skip(100000)
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.take(1000000)
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}
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// Checks whether Skip<Zip<A,B>> is as fast as Zip<Skip<A>, Skip<B>>, from
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// https://users.rust-lang.org/t/performance-difference-between-iterator-zip-and-skip-order/15743
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#[bench]
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fn bench_zip_then_skip(b: &mut Bencher) {
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let v: Vec<_> = (0..100_000).collect();
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let t: Vec<_> = (0..100_000).collect();
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b.iter(|| {
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let s = v
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.iter()
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.zip(t.iter())
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.skip(10000)
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.take_while(|t| *t.0 < 10100)
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.map(|(a, b)| *a + *b)
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.sum::<u64>();
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assert_eq!(s, 2009900);
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});
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}
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#[bench]
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fn bench_skip_then_zip(b: &mut Bencher) {
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let v: Vec<_> = (0..100_000).collect();
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let t: Vec<_> = (0..100_000).collect();
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b.iter(|| {
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let s = v
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.iter()
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.skip(10000)
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.zip(t.iter().skip(10000))
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.take_while(|t| *t.0 < 10100)
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.map(|(a, b)| *a + *b)
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.sum::<u64>();
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assert_eq!(s, 2009900);
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});
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}
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#[bench]
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fn bench_filter_count(b: &mut Bencher) {
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b.iter(|| (0i64..1000000).map(black_box).filter(|x| x % 3 == 0).count())
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}
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#[bench]
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fn bench_filter_ref_count(b: &mut Bencher) {
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b.iter(|| (0i64..1000000).map(black_box).by_ref().filter(|x| x % 3 == 0).count())
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}
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#[bench]
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fn bench_filter_chain_count(b: &mut Bencher) {
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b.iter(|| (0i64..1000000).chain(0..1000000).map(black_box).filter(|x| x % 3 == 0).count())
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}
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#[bench]
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fn bench_filter_chain_ref_count(b: &mut Bencher) {
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b.iter(|| {
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(0i64..1000000).chain(0..1000000).map(black_box).by_ref().filter(|x| x % 3 == 0).count()
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})
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}
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#[bench]
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fn bench_partial_cmp(b: &mut Bencher) {
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b.iter(|| (0..100000).map(black_box).partial_cmp((0..100000).map(black_box)))
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}
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#[bench]
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fn bench_chain_partial_cmp(b: &mut Bencher) {
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b.iter(|| {
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(0..50000).chain(50000..100000).map(black_box).partial_cmp((0..100000).map(black_box))
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})
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}
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#[bench]
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fn bench_lt(b: &mut Bencher) {
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b.iter(|| (0..100000).map(black_box).lt((0..100000).map(black_box)))
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}
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#[bench]
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fn bench_trusted_random_access_adapters(b: &mut Bencher) {
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let vec1: Vec<_> = (0usize..100000).collect();
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let vec2 = black_box(vec1.clone());
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b.iter(|| {
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let mut iter = vec1
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.iter()
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.copied()
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.enumerate()
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.map(|(idx, e)| idx.wrapping_add(e))
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.zip(vec2.iter().copied())
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.map(|(a, b)| a.wrapping_add(b))
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.fuse();
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let mut acc: usize = 0;
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let size = iter.size();
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for i in 0..size {
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// SAFETY: TRA requirements are satisfied by 0..size iteration and then dropping the
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// iterator.
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acc = acc.wrapping_add(unsafe { iter.__iterator_get_unchecked(i) });
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}
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acc
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})
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}
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/// Exercises the iter::Copied specialization for slice::Iter
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#[bench]
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fn bench_copied_chunks(b: &mut Bencher) {
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let v = vec![1u8; 1024];
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b.iter(|| {
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let mut iter = black_box(&v).iter().copied();
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let mut acc = Wrapping(0);
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// This uses a while-let loop to side-step the TRA specialization in ArrayChunks
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while let Ok(chunk) = iter.next_chunk::<{ mem::size_of::<u64>() }>() {
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let d = u64::from_ne_bytes(chunk);
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acc += Wrapping(d.rotate_left(7).wrapping_add(1));
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}
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acc
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})
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}
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/// Exercises the TrustedRandomAccess specialization in ArrayChunks
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#[bench]
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fn bench_trusted_random_access_chunks(b: &mut Bencher) {
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let v = vec![1u8; 1024];
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b.iter(|| {
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black_box(&v)
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.iter()
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// this shows that we're not relying on the slice::Iter specialization in Copied
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.map(|b| *b.borrow())
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.array_chunks::<{ mem::size_of::<u64>() }>()
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.map(|ary| {
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let d = u64::from_ne_bytes(ary);
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Wrapping(d.rotate_left(7).wrapping_add(1))
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})
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.sum::<Wrapping<u64>>()
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})
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}
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