rust/library/core/benches/iter.rs

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use core::borrow::Borrow;
use core::iter::*;
use core::mem;
use core::num::Wrapping;
use core::ops::Range;
use test::{black_box, Bencher};
#[bench]
fn bench_rposition(b: &mut Bencher) {
let it: Vec<usize> = (0..300).collect();
b.iter(|| {
it.iter().rposition(|&x| x <= 150);
});
}
#[bench]
fn bench_skip_while(b: &mut Bencher) {
b.iter(|| {
let it = 0..100;
let mut sum = 0;
it.skip_while(|&x| {
sum += x;
sum < 4000
})
.all(|_| true);
});
}
#[bench]
fn bench_multiple_take(b: &mut Bencher) {
let mut it = (0..42).cycle();
b.iter(|| {
let n = it.next().unwrap();
for _ in 0..n {
it.clone().take(it.next().unwrap()).all(|_| true);
}
});
}
fn scatter(x: i32) -> i32 {
(x * 31) % 127
}
#[bench]
fn bench_max_by_key(b: &mut Bencher) {
b.iter(|| {
let it = 0..100;
it.map(black_box).max_by_key(|&x| scatter(x))
})
}
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// https://www.reddit.com/r/rust/comments/31syce/using_iterators_to_find_the_index_of_the_min_or/
#[bench]
fn bench_max_by_key2(b: &mut Bencher) {
fn max_index_iter(array: &[i32]) -> usize {
array.iter().enumerate().max_by_key(|&(_, item)| item).unwrap().0
}
let mut data = vec![0; 1638];
data[514] = 9999;
b.iter(|| max_index_iter(&data));
}
#[bench]
fn bench_max(b: &mut Bencher) {
b.iter(|| {
let it = 0..100;
it.map(black_box).map(scatter).max()
})
}
#[bench]
fn bench_range_step_by_sum_reducible(b: &mut Bencher) {
let r = 0u32..1024;
b.iter(|| {
let r = black_box(r.clone()).step_by(8);
let mut sum: u32 = 0;
for i in r {
sum += i;
}
sum
})
}
#[bench]
fn bench_range_step_by_loop_u32(b: &mut Bencher) {
let r = 0..(u16::MAX as u32);
b.iter(|| {
let r = black_box(r.clone()).step_by(64);
let mut sum: u32 = 0;
for i in r {
let i = i ^ i.wrapping_sub(1);
sum = sum.wrapping_add(i);
}
sum
})
}
#[bench]
fn bench_range_step_by_fold_usize(b: &mut Bencher) {
let r: Range<usize> = 0..(u16::MAX as usize);
b.iter(|| {
let r = black_box(r.clone());
r.step_by(64)
.map(|x: usize| x ^ (x.wrapping_sub(1)))
.fold(0usize, |acc, i| acc.wrapping_add(i))
})
}
#[bench]
fn bench_range_step_by_fold_u16(b: &mut Bencher) {
let r: Range<u16> = 0..u16::MAX;
b.iter(|| {
let r = black_box(r.clone());
r.step_by(64).map(|x: u16| x ^ (x.wrapping_sub(1))).fold(0u16, |acc, i| acc.wrapping_add(i))
})
}
pub fn copy_zip(xs: &[u8], ys: &mut [u8]) {
for (a, b) in ys.iter_mut().zip(xs) {
*a = *b;
}
}
pub fn add_zip(xs: &[f32], ys: &mut [f32]) {
for (a, b) in ys.iter_mut().zip(xs) {
*a += *b;
}
}
#[bench]
fn bench_zip_copy(b: &mut Bencher) {
let source = vec![0u8; 16 * 1024];
let mut dst = black_box(vec![0u8; 16 * 1024]);
b.iter(|| copy_zip(&source, &mut dst))
}
#[bench]
fn bench_zip_add(b: &mut Bencher) {
let source = vec![1.; 16 * 1024];
let mut dst = vec![0.; 16 * 1024];
b.iter(|| add_zip(&source, &mut dst));
}
/// `Iterator::for_each` implemented as a plain loop.
fn for_each_loop<I, F>(iter: I, mut f: F)
where
I: Iterator,
F: FnMut(I::Item),
{
for item in iter {
f(item);
}
}
/// `Iterator::for_each` implemented with `fold` for internal iteration.
/// (except when `by_ref()` effectively disables that optimization.)
fn for_each_fold<I, F>(iter: I, mut f: F)
where
I: Iterator,
F: FnMut(I::Item),
{
iter.fold((), move |(), item| f(item));
}
#[bench]
fn bench_for_each_chain_loop(b: &mut Bencher) {
b.iter(|| {
let mut acc = 0;
let iter = (0i64..1000000).chain(0..1000000).map(black_box);
for_each_loop(iter, |x| acc += x);
acc
});
}
#[bench]
fn bench_for_each_chain_fold(b: &mut Bencher) {
b.iter(|| {
let mut acc = 0;
let iter = (0i64..1000000).chain(0..1000000).map(black_box);
for_each_fold(iter, |x| acc += x);
acc
});
}
#[bench]
fn bench_for_each_chain_ref_fold(b: &mut Bencher) {
b.iter(|| {
let mut acc = 0;
let mut iter = (0i64..1000000).chain(0..1000000).map(black_box);
for_each_fold(iter.by_ref(), |x| acc += x);
acc
});
}
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
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/// Helper to benchmark `sum` for iterators taken by value which
/// can optimize `fold`, and by reference which cannot.
macro_rules! bench_sums {
($bench_sum:ident, $bench_ref_sum:ident, $iter:expr) => {
#[bench]
fn $bench_sum(b: &mut Bencher) {
b.iter(|| -> i64 { $iter.map(black_box).sum() });
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
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}
#[bench]
fn $bench_ref_sum(b: &mut Bencher) {
b.iter(|| -> i64 { $iter.map(black_box).by_ref().sum() });
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
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}
};
}
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
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bench_sums! {
bench_flat_map_sum,
bench_flat_map_ref_sum,
(0i64..1000).flat_map(|x| x..x+1000)
}
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
2017-09-25 22:53:08 -05:00
bench_sums! {
bench_flat_map_chain_sum,
bench_flat_map_chain_ref_sum,
(0i64..1000000).flat_map(|x| once(x).chain(once(x)))
}
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
2017-09-25 22:53:08 -05:00
bench_sums! {
bench_enumerate_sum,
bench_enumerate_ref_sum,
(0i64..1000000).enumerate().map(|(i, x)| x * i as i64)
}
bench_sums! {
bench_enumerate_chain_sum,
bench_enumerate_chain_ref_sum,
(0i64..1000000).chain(0..1000000).enumerate().map(|(i, x)| x * i as i64)
}
bench_sums! {
bench_filter_sum,
bench_filter_ref_sum,
(0i64..1000000).filter(|x| x % 3 == 0)
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
2017-09-25 22:53:08 -05:00
}
bench_sums! {
bench_filter_chain_sum,
bench_filter_chain_ref_sum,
(0i64..1000000).chain(0..1000000).filter(|x| x % 3 == 0)
Add more custom folding to `core::iter` adaptors Many of the iterator adaptors will perform faster folds if they forward to their inner iterator's folds, especially for inner types like `Chain` which are optimized too. The following types are newly specialized: | Type | `fold` | `rfold` | | ----------- | ------ | ------- | | `Enumerate` | ✓ | ✓ | | `Filter` | ✓ | ✓ | | `FilterMap` | ✓ | ✓ | | `FlatMap` | exists | ✓ | | `Fuse` | ✓ | ✓ | | `Inspect` | ✓ | ✓ | | `Peekable` | ✓ | N/A¹ | | `Skip` | ✓ | N/A² | | `SkipWhile` | ✓ | N/A¹ | ¹ not a `DoubleEndedIterator` ² `Skip::next_back` doesn't pull skipped items at all, but this couldn't be avoided if `Skip::rfold` were to call its inner iterator's `rfold`. Benchmarks ---------- In the following results, plain `_sum` computes the sum of a million integers -- note that `sum()` is implemented with `fold()`. The `_ref_sum` variants do the same on a `by_ref()` iterator, which is limited to calling `next()` one by one, without specialized `fold`. The `chain` variants perform the same tests on two iterators chained together, to show a greater benefit of forwarding `fold` internally. test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228) test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676) test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110) test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334) test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051) test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013) test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188) test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894) test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440) test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972) test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507) test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472) test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395) test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341) test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748) test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616) test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371) test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928) test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702) test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320) test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186) test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712) test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480) test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309) test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198) test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645) test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252) test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054) test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845) test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892) test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804) test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
2017-09-25 22:53:08 -05:00
}
bench_sums! {
bench_filter_map_sum,
bench_filter_map_ref_sum,
(0i64..1000000).filter_map(|x| x.checked_mul(x))
}
bench_sums! {
bench_filter_map_chain_sum,
bench_filter_map_chain_ref_sum,
(0i64..1000000).chain(0..1000000).filter_map(|x| x.checked_mul(x))
}
bench_sums! {
bench_fuse_sum,
bench_fuse_ref_sum,
(0i64..1000000).fuse()
}
bench_sums! {
bench_fuse_chain_sum,
bench_fuse_chain_ref_sum,
(0i64..1000000).chain(0..1000000).fuse()
}
bench_sums! {
bench_inspect_sum,
bench_inspect_ref_sum,
(0i64..1000000).inspect(|_| {})
}
bench_sums! {
bench_inspect_chain_sum,
bench_inspect_chain_ref_sum,
(0i64..1000000).chain(0..1000000).inspect(|_| {})
}
bench_sums! {
bench_peekable_sum,
bench_peekable_ref_sum,
(0i64..1000000).peekable()
}
bench_sums! {
bench_peekable_chain_sum,
bench_peekable_chain_ref_sum,
(0i64..1000000).chain(0..1000000).peekable()
}
bench_sums! {
bench_skip_sum,
bench_skip_ref_sum,
(0i64..1000000).skip(1000)
}
bench_sums! {
bench_skip_chain_sum,
bench_skip_chain_ref_sum,
(0i64..1000000).chain(0..1000000).skip(1000)
}
bench_sums! {
bench_skip_while_sum,
bench_skip_while_ref_sum,
(0i64..1000000).skip_while(|&x| x < 1000)
}
bench_sums! {
bench_skip_while_chain_sum,
bench_skip_while_chain_ref_sum,
(0i64..1000000).chain(0..1000000).skip_while(|&x| x < 1000)
}
bench_sums! {
bench_take_while_chain_sum,
bench_take_while_chain_ref_sum,
(0i64..1000000).chain(1000000..).take_while(|&x| x < 1111111)
}
bench_sums! {
bench_cycle_take_sum,
bench_cycle_take_ref_sum,
2021-01-08 03:50:35 -06:00
(0..10000).cycle().take(1000000)
}
bench_sums! {
bench_cycle_skip_take_sum,
bench_cycle_skip_take_ref_sum,
(0..100000).cycle().skip(1000000).take(1000000)
}
bench_sums! {
bench_cycle_take_skip_sum,
bench_cycle_take_skip_ref_sum,
(0..100000).cycle().take(1000000).skip(100000)
}
bench_sums! {
bench_skip_cycle_skip_zip_add_sum,
bench_skip_cycle_skip_zip_add_ref_sum,
(0..100000).skip(100).cycle().skip(100)
.zip((0..100000).cycle().skip(10))
.map(|(a,b)| a+b)
.skip(100000)
.take(1000000)
}
// Checks whether Skip<Zip<A,B>> is as fast as Zip<Skip<A>, Skip<B>>, from
// https://users.rust-lang.org/t/performance-difference-between-iterator-zip-and-skip-order/15743
#[bench]
fn bench_zip_then_skip(b: &mut Bencher) {
let v: Vec<_> = (0..100_000).collect();
let t: Vec<_> = (0..100_000).collect();
b.iter(|| {
let s = v
.iter()
.zip(t.iter())
.skip(10000)
.take_while(|t| *t.0 < 10100)
.map(|(a, b)| *a + *b)
.sum::<u64>();
assert_eq!(s, 2009900);
});
}
#[bench]
fn bench_skip_then_zip(b: &mut Bencher) {
let v: Vec<_> = (0..100_000).collect();
let t: Vec<_> = (0..100_000).collect();
b.iter(|| {
let s = v
.iter()
.skip(10000)
.zip(t.iter().skip(10000))
.take_while(|t| *t.0 < 10100)
.map(|(a, b)| *a + *b)
.sum::<u64>();
assert_eq!(s, 2009900);
});
}
2019-02-27 04:44:30 -06:00
#[bench]
fn bench_filter_count(b: &mut Bencher) {
b.iter(|| (0i64..1000000).map(black_box).filter(|x| x % 3 == 0).count())
2019-02-27 04:44:30 -06:00
}
#[bench]
fn bench_filter_ref_count(b: &mut Bencher) {
b.iter(|| (0i64..1000000).map(black_box).by_ref().filter(|x| x % 3 == 0).count())
2019-02-27 04:44:30 -06:00
}
#[bench]
fn bench_filter_chain_count(b: &mut Bencher) {
b.iter(|| (0i64..1000000).chain(0..1000000).map(black_box).filter(|x| x % 3 == 0).count())
2019-02-27 04:44:30 -06:00
}
#[bench]
fn bench_filter_chain_ref_count(b: &mut Bencher) {
b.iter(|| {
(0i64..1000000).chain(0..1000000).map(black_box).by_ref().filter(|x| x % 3 == 0).count()
})
2019-02-27 06:22:18 -06:00
}
#[bench]
fn bench_partial_cmp(b: &mut Bencher) {
b.iter(|| (0..100000).map(black_box).partial_cmp((0..100000).map(black_box)))
}
#[bench]
fn bench_chain_partial_cmp(b: &mut Bencher) {
b.iter(|| {
(0..50000).chain(50000..100000).map(black_box).partial_cmp((0..100000).map(black_box))
})
}
#[bench]
fn bench_lt(b: &mut Bencher) {
b.iter(|| (0..100000).map(black_box).lt((0..100000).map(black_box)))
}
2022-01-04 19:28:30 -06:00
#[bench]
fn bench_trusted_random_access_adapters(b: &mut Bencher) {
let vec1: Vec<_> = (0usize..100000).collect();
let vec2 = black_box(vec1.clone());
b.iter(|| {
let mut iter = vec1
.iter()
.copied()
.enumerate()
.map(|(idx, e)| idx.wrapping_add(e))
.zip(vec2.iter().copied())
.map(|(a, b)| a.wrapping_add(b))
.fuse();
let mut acc: usize = 0;
let size = iter.size();
for i in 0..size {
// SAFETY: TRA requirements are satisfied by 0..size iteration and then dropping the
// iterator.
acc = acc.wrapping_add(unsafe { iter.__iterator_get_unchecked(i) });
}
acc
})
}
/// Exercises the iter::Copied specialization for slice::Iter
#[bench]
fn bench_next_chunk_copied(b: &mut Bencher) {
let v = vec![1u8; 1024];
b.iter(|| {
let mut iter = black_box(&v).iter().copied();
let mut acc = Wrapping(0);
// This uses a while-let loop to side-step the TRA specialization in ArrayChunks
while let Ok(chunk) = iter.next_chunk::<{ mem::size_of::<u64>() }>() {
let d = u64::from_ne_bytes(chunk);
acc += Wrapping(d.rotate_left(7).wrapping_add(1));
}
acc
})
}
/// Exercises the TrustedRandomAccess specialization in ArrayChunks
#[bench]
2023-05-25 00:21:44 -05:00
#[allow(noop_method_call)]
fn bench_next_chunk_trusted_random_access(b: &mut Bencher) {
let v = vec![1u8; 1024];
b.iter(|| {
black_box(&v)
.iter()
// this shows that we're not relying on the slice::Iter specialization in Copied
.map(|b| *b.borrow())
.array_chunks::<{ mem::size_of::<u64>() }>()
.map(|ary| {
let d = u64::from_ne_bytes(ary);
Wrapping(d.rotate_left(7).wrapping_add(1))
})
.sum::<Wrapping<u64>>()
})
}
#[bench]
fn bench_next_chunk_filter_even(b: &mut Bencher) {
let a = (0..1024).next_chunk::<1024>().unwrap();
b.iter(|| black_box(&a).iter().filter(|&&i| i % 2 == 0).next_chunk::<32>())
}
#[bench]
fn bench_next_chunk_filter_predictably_true(b: &mut Bencher) {
let a = (0..1024).next_chunk::<1024>().unwrap();
b.iter(|| black_box(&a).iter().filter(|&&i| i < 100).next_chunk::<32>())
}
#[bench]
fn bench_next_chunk_filter_mostly_false(b: &mut Bencher) {
let a = (0..1024).next_chunk::<1024>().unwrap();
b.iter(|| black_box(&a).iter().filter(|&&i| i > 900).next_chunk::<32>())
}
#[bench]
fn bench_next_chunk_filter_map_even(b: &mut Bencher) {
let a = (0..1024).next_chunk::<1024>().unwrap();
b.iter(|| black_box(&a).iter().filter_map(|&i| (i % 2 == 0).then(|| i)).next_chunk::<32>())
}
#[bench]
fn bench_next_chunk_filter_map_predictably_true(b: &mut Bencher) {
let a = (0..1024).next_chunk::<1024>().unwrap();
b.iter(|| black_box(&a).iter().filter_map(|&i| (i < 100).then(|| i)).next_chunk::<32>())
}
#[bench]
fn bench_next_chunk_filter_map_mostly_false(b: &mut Bencher) {
let a = (0..1024).next_chunk::<1024>().unwrap();
b.iter(|| black_box(&a).iter().filter_map(|&i| (i > 900).then(|| i)).next_chunk::<32>())
}