Tracking Issue for `Iterator::next_chunk`

[rossmacarthur]

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Feature gate: #![feature(iter_next_chunk)]

This is a tracking issue for the .next_chunk() method on iterators which allows you to advance the iterator N elements and return an array [T; N]

Public API

use core::array;

trait Iterator {
    type Item;
    
    fn next_chunk<const N: usize>(&mut self) -> Result<[Self::Item; N], array::IntoIter<Self::Item, N>>
    where
        Self: Sized;
}

Steps / History

• Implementation:
  • Add Iterator::next_chunk #93700
  • Optimized vec::IntoIter::next_chunk impl #98553
  • Optimize slice_iter.copied().next_chunk() #103166
  • optimize next_chunk impls for Filter and FilterMap #111781
  • fix Drop items getting leaked in Filter::next_chunk #126879
• Final comment period (FCP)
• Stabilization PR

Unresolved Questions

• Naming: other options include next_array() or next_array_chunk().
• Should we also add next_chunk_back to DoubleEndedIterator?
• How should we handle N = 0?

[ChayimFriedman2]

Should this method support N=0? itertools does not, and it sounds plausible; however, if we want to do the same, we will have the same concern as <[T]>::array_chunks() that cannot be stabilized until generic_const_exprs or some subset will be stabilized.

[the8472]

I'm wondering about the intended uses of this API.

If it is for performance (autovectorization) then it seems fairly brittle. I just tried doing this benchmark:

#[bench]
fn bench_next_chunk(b: &mut Bencher) {
    let v = vec![13u8; 2048];

    b.iter(|| {
        const CHUNK: usize = 8;

        let mut sum = [0u32; CHUNK];
        let mut iter = black_box(v.clone()).into_iter();

        while let Ok(chunk) = iter.next_chunk::<CHUNK>() {
            for i in 0..CHUNK {
                sum[i] += chunk[i] as u32;
            }
        }

        sum
    })
}

and it took 17,272 ns/iter when compiled with target-cpu=znver2. With a handrolled implementation I got 211 ns/iter. I encountered some 10x slowdowns in a few other combinations. Sometimes codegen-units=1 produced even worse assembly because vectorizer did silly things such as loading one byte at a time into a simd register with vpinsrb.

And that's with direct, linear memory access on a Vec::IntoIter which should be relatively straight-forward to turn into masked simd loads. Many iterators yield references. A [&T; N] would need a gather-load which is expensive and only available on newer CPUs, this is unlike slice::array_chunks which yields &[T; N]. We could try specializing slice.iter().next_chunk() but then you might as well use array_chunks. And of course it'll get more complex with additional adapters.

Another limitation is that it can't be used in a for-in loop or chained further with other iterator adapters, an issue #92393 didn't have.

[Lokathor]

This is probably an example of a case where you should first use copied(), and then chunk it, and then you turn that array into a simd type for the work, before possibly turning it back at the end of your loop.

And the change into and out of simd form will have a cost, so you'll want to ensure you're doing enough work inside each loop pass or it might exceed the cost of the scalar only code.

[the8472]

yeah that was just an example to illustrate that the default implementation is suboptimal and we'll need optimized implementations on most linear-memory sources and length-preserving adapters.

[Lokathor]

Mm, yeah.

Myself I would imagine using this most on iterators that aren't linear memory sources, and "the point" would be to have a clean and unified way to turn some crazy data source into simple SIMD chunks.

[rossmacarthur]

Might be good to align the naming of this function with Iterator::array_chunks, i.e. either

• Iterator::chunks
• Iterator::next_chunk

Or

• Iterator::array_chunks
• Iterator::next_array_chunk

[mark-i-m]

So I'm finding myself caring less and less about the actual naming. I've wanted this feature at least 3 times in the last year and had to write my own version to get around. Can we just pick something and get the feature stabilized?

[ZhennanWu]

I believe the current implementation also suffers from this compiler optimization issue: #110734

Godbolt: https://godbolt.org/z/Te5Wo9eh7

[the8472]

I'm not sure what that issue has to do with next_chunk specifically. It seems to be mostly about the codegen for it.collect::<Vec<_>>(), which I think is easily disturbed by anything mutating the iterator.