orx_concurrent_iter

Trait ConcurrentIter

Source 
pub trait ConcurrentIter: ConcurrentIterX {
    type BufferedIter: BufferedChunk<Self::Item, ConIter = Self>;

    // Required methods
    fn next_id_and_value(&self) -> Option<Next<Self::Item>>;
    fn next_chunk(
        &self,
        chunk_size: usize,
    ) -> Option<NextChunk<Self::Item, impl ExactSizeIterator<Item = Self::Item>>>;

    // Provided methods
    fn into_con_iter_x(self) -> impl ConcurrentIterX<Item = Self::Item>
       where Self: Sized { ... }
    fn ids_and_values(&self) -> ConIterIdsAndValues<'_, Self> ⓘ
       where Self: Sized { ... }
    fn for_each<Fun: FnMut(Self::Item)>(&self, chunk_size: usize, fun: Fun)
       where Self: Sized { ... }
    fn enumerate_for_each<Fun: FnMut(usize, Self::Item)>(
        &self,
        chunk_size: usize,
        fun: Fun,
    )
       where Self: Sized { ... }
    fn buffered_iter(
        &self,
        chunk_size: usize,
    ) -> BufferedIter<'_, Self::Item, Self::BufferedIter> { ... }
}
Expand description

Trait defining a concurrent iterator with next and next_id_and_chunk methods which can safely be called my multiple threads concurrently.

Required Associated Types§

Source

type BufferedIter: BufferedChunk<Self::Item, ConIter = Self>

Type of the buffered iterator returned by the chunk_iter method when elements are fetched in chunks by each thread.

Required Methods§

Source

fn next_id_and_value(&self) -> Option<Next<Self::Item>>

Advances the iterator and returns the next value together with its enumeration index.

Returns None when iteration is finished.

§Examples
use orx_concurrent_iter::*;
use orx_concurrent_bag::*;

fn to_str(num: usize) -> String {
    num.to_string()
}

let (num_threads, chunk_size) = (4, 32);
let strings: Vec<_> = (0..1024).map(to_str).collect();
let bag = ConcurrentBag::new();

let iter = strings.con_iter();
std::thread::scope(|s| {
    for _ in 0..num_threads {
        s.spawn(|| {
            while let Some(next) = iter.next_id_and_value() {
                // idx is the original position in `characters`
                assert_eq!(next.value, &to_str(next.idx));
                bag.push((next.idx, next.value.len()));
            }
        });
    }
});

let mut outputs: Vec<_> = bag.into_inner().into();
outputs.sort_by_key(|x| x.0); // sort to original order
for (x, y) in outputs.iter().map(|x| x.1).zip(&strings) {
    assert_eq!(x, y.len());
}
Source

fn next_chunk( &self, chunk_size: usize, ) -> Option<NextChunk<Self::Item, impl ExactSizeIterator<Item = Self::Item>>>

Advances the iterator chunk_size times and returns an iterator of at most chunk_size consecutive next values. Further, the beginning enumeration index of the yielded values is returned.

This method:

  • returns an iterator of chunk_size elements if there exist sufficient elements left in the iteration, or
  • it might return an iterator of m < chunk_size elements if there exists only m elements left, or
  • it might return None, it will never return Some of an empty iterator.

This call would be equivalent to calling next_id_and_value method chunk_size times in a single-threaded execution. However, calling next method chunk_size times in a concurrent execution does not guarantee to return chunk_size consecutive elements. On the other hand, next_chunk guarantees that it returns consecutive elements, preventing any intermediate calls.

More importantly, this is an important performance optimization feature that enables

  • reducing the number of atomic updates,
  • avoiding performance degradation by false sharing if the fetched inputs will be processed and written to a shared memory (see ConcurrentBag performance notes).
§Examples

Note that next_chunk method returns a NextChunk. Further, NextChunk::values is a regular ExactSizeIterator which might yield at most chunk_size elements. Note that the iterator will never be empty. Whenever the concurrent iterator is used up, next_chunk method, similar to next, will return None.

use orx_concurrent_iter::*;
use orx_concurrent_bag::*;

fn to_str(num: usize) -> String {
    num.to_string()
}

let (num_threads, chunk_size) = (4, 32);
let strings: Vec<_> = (0..1024).map(to_str).collect();
let bag = ConcurrentBag::new();

let iter = strings.con_iter();
std::thread::scope(|s| {
    for _ in 0..num_threads {
        s.spawn(|| {
            while let Some(chunk) = iter.next_chunk(chunk_size) {
                for (i, value) in chunk.values.enumerate() {
                    // idx is the original position in `characters`
                    let idx = chunk.begin_idx + i;
                    assert_eq!(value, &to_str(idx));
                    bag.push((idx, value.len()));
                }
            }
        });
    }
});

let mut outputs: Vec<_> = bag.into_inner().into();
outputs.sort_by_key(|x| x.0); // sort to original order
for (x, y) in outputs.iter().map(|x| x.1).zip(&strings) {
    assert_eq!(x, y.len());
}

Provided Methods§

Source

fn into_con_iter_x(self) -> impl ConcurrentIterX<Item = Self::Item>
where Self: Sized,

Converts the ConcurrentIter into a ConcurrentIterX.

Note that type or method names with suffix ‘x’ is an indicator of the dropped promise of keeping order.

  • ConcurrentIterX allows concurrent iteration; however, it may or may not provide the initial order of an element in the providing source.
  • ConcurrentIter extends ConcurrentIterX by including the keeping track of order guarantee.

This crate provides ordered ConcurrentIter implementations of all iterators. In other words, ConcurrentIterX implementations already preserve the input order and hence the iterators also implement ConcurrentIter. A different ConcurrentIterX implementation is provided only if it makes it possible to have a performance gain. One example is concurrent iterators created for arbitrary sequential iterators, in which case keeping track of input order might have an impact.

In such cases, in order to make the choice explicit, these traits are differentiated.

In most of the practical use cases, we require only the ConcurrentIter. The differentiation is utilized by under the hood coordination of crates aiming high performance such as orx_parallel.

§Examples

A. When order matters

Once can create a concurrent iterator from a sequential iterator and use it as it is. This will guarantee to provide the correct index of the input. For instance in the following, (i, value) pair provided by ids_and_values will guarantee that 2*i == value.

use orx_concurrent_iter::*;
use orx_concurrent_bag::ConcurrentBag;

let num_threads = 8;

let seq_iter = (0..1024).map(|x| x * 2).into_iter();
let con_iter = seq_iter.into_con_iter();

let con_bag = ConcurrentBag::new();

std::thread::scope(|s| {
    for _ in 0..num_threads {
        s.spawn(|| {
            for (i, value) in con_iter.ids_and_values() {
                con_bag.push((i, value / 2));
            }
        });
    }
});

let vec = con_bag.into_inner();
for (i, value) in vec.into_iter() {
    assert_eq!(i, value as usize);
}

B. When order does not matter

Alternatively, if the order does not matter, we can create an unordered version of the concurrent iterator. We can directly create one using into_con_iter_x on the source type. Alternatively, we can convert any ordered concurrent iterator any time by calling into_con_iter_x. In the following, we use an unordered version, since we are not interested in the indices of elements while computing a parallel sum.

use orx_concurrent_iter::*;

let num_threads = 8;

let seq_iter = (0..1024).map(|x| x * 2).into_iter();
let con_iter = seq_iter.into_con_iter_x(); // directly into x
// let con_iter = seq_iter.into_con_iter().into_con_iter_x(); // or alternatively

let sum: i32 = std::thread::scope(|s| {
    let mut handles: Vec<_> = vec![];
    for _ in 0..num_threads {
        // as expected, `ids_and_values` method is not available
        // but we have `values`
        handles.push(s.spawn(|| con_iter.values().sum::<i32>()));
    }

    handles.into_iter().map(|x| x.join().expect("-")).sum()
});

assert_eq!(sum, (0..1024).sum::<i32>() * 2)
Source

fn ids_and_values(&self) -> ConIterIdsAndValues<'_, Self> ⓘ
where Self: Sized,

Returns an Iterator over the ids and values of elements of the concurrent iterator.

Note that values method can be called concurrently from multiple threads to create multiple local-to-thread regular Iterators. However, each of these iterators will be connected in the sense that:

  • all iterators will be aware of the progress by the other iterators;
  • each element will be yielded exactly once.

The iterator’s next method does nothing but call the next_id_and_value; this iterator is only to allow for using for loops directly.

§Examples
use orx_concurrent_iter::*;
use orx_concurrent_bag::*;

fn to_str(num: usize) -> String {
    num.to_string()
}

let num_threads = 4;
let strings: Vec<_> = (0..1024).map(to_str).collect();
let bag = ConcurrentBag::new();

let iter = strings.con_iter();
std::thread::scope(|s| {
    for _ in 0..num_threads {
        s.spawn(|| {
            for (idx, value) in iter.ids_and_values() {
                // idx is the original position in `characters`
                assert_eq!(value, &to_str(idx));
                bag.push((idx, value.len()));
            }
        });
    }
});

let mut outputs: Vec<_> = bag.into_inner().into();
outputs.sort_by_key(|x| x.0); // sort to original order
for (x, y) in outputs.iter().map(|x| x.1).zip(&strings) {
    assert_eq!(x, y.len());
}
Source

fn for_each<Fun: FnMut(Self::Item)>(&self, chunk_size: usize, fun: Fun)
where Self: Sized,

Applies the function fun to each element of the iterator concurrently.

Note that this method might be called on the same iterator at the same time from different threads. The iterator guarantees that the function is applied to each element exactly once.

At each iteration of the loop, this method pulls chunk_size elements from the iterator. Under the hood, this method will loop using the buffered chunks iterator, pulling items as batches of the given chunk_size.

§Panics

Panics if chunk_size is zero; chunk size is required to be strictly positive.

§Examples
use orx_concurrent_iter::*;
use orx_concurrent_bag::*;
use orx_iterable::Collection;

let (num_threads, chunk_size) = (4, 2);
let characters = vec!['0', '1', '2', '3', '4', '5', '6', '7'];

let iter = characters.con_iter();
let bag = ConcurrentBag::new();

std::thread::scope(|s| {
    for _ in 0..num_threads {
        s.spawn(|| {
            iter.for_each(chunk_size, |c| {
                bag.push(c.to_digit(10).unwrap());
            });
        });
    }
});

// parent concurrent iterator is completely consumed
assert!(iter.next().is_none());

let sum: u32 = bag.into_inner().iter().sum();
assert_eq!(sum, (0..8).sum());
Source

fn enumerate_for_each<Fun: FnMut(usize, Self::Item)>( &self, chunk_size: usize, fun: Fun, )
where Self: Sized,

Applies the function fun to each index and corresponding element of the iterator concurrently.

Note that this method might be called on the same iterator at the same time from different threads. The iterator guarantees that the function is applied to each element exactly once.

At each iteration of the loop, this method pulls chunk_size elements from the iterator. Under the hood, this method will loop using the buffered chunks iterator, pulling items as batches of the given chunk_size.

§Panics

Panics if chunk_size is zero; chunk size is required to be strictly positive.

§Examples
use orx_concurrent_iter::*;
use orx_concurrent_bag::*;
use orx_iterable::Collection;

let (num_threads, chunk_size) = (4, 2);
let characters = vec!['0', '1', '2', '3', '4', '5', '6', '7'];

let iter = characters.con_iter();
let bag = ConcurrentBag::new();

std::thread::scope(|s| {
    for _ in 0..num_threads {
        s.spawn(|| {
            iter.enumerate_for_each(chunk_size, |i, c| {
                let expected_value = char::from_digit(i as u32, 10).unwrap();
                assert_eq!(c, &expected_value);

                bag.push(c.to_digit(10).unwrap());
            });
        });
    }
});

// parent concurrent iterator is completely consumed
assert!(iter.next().is_none());

let sum: u32 = bag.into_inner().iter().sum();
assert_eq!(sum, (0..8).sum());
Source

fn buffered_iter( &self, chunk_size: usize, ) -> BufferedIter<'_, Self::Item, Self::BufferedIter>

Creates an iterator which pulls elements from this iterator as chunks of the given chunk_size.

Returned iterator is a regular Iterator, except that it is linked to the concurrent iterator and pulls its elements concurrently from the parent iterator. The next call of the buffered iterator returns None if the concurrent iterator is consumed. Otherwise, it returns a NextChunk which is composed of two values:

  • begin_idx: the index in the original source data, or concurrent iterator, of the first element of the pulled chunk,
  • values: an ExactSizeIterator containing at least 1 and at most chunk_size consecutive elements pulled from the original source data.

Iteration in chunks is allocation free whenever possible; for instance, when the source data is a vector, a slice or an array, etc. with known size. On the other hand, when the source data is an arbitrary Iterator, iteration in chunks requires a buffer to write the chunk of pulled elements.

This method is memory efficient in these situations. It allocates a buffer of chunk_size only once when created, only if the source data requires it. This buffer is used over and over until the concurrent iterator is consumed.

§Panics

Panics if chunk_size is zero; chunk size is required to be strictly positive.

§Example

Example below illustrates a parallel sum operation. Entire iteration requires an allocation (4 threads) * (16 chunk size) = 64 elements.

use orx_concurrent_iter::*;

let (num_threads, chunk_size) = (4, 64);
let iter = (0..16384).map(|x| x * 2).into_con_iter();

let sum = std::thread::scope(|s| {
    (0..num_threads)
        .map(|_| {
            s.spawn(|| {
                let mut sum = 0;
                let mut buffered = iter.buffered_iter(chunk_size);
                while let Some(chunk) = buffered.next() {
                    sum += chunk.values.sum::<usize>();
                }
                sum
            })
        })
        .map(|x| x.join().expect("-"))
        .sum::<usize>()
});

let expected = 16384 * 16383;
assert_eq!(sum, expected);
§buffered_iter and next_chunk

When iterating over single elements using next or values, the concurrent iterator just yields the element without requiring any allocation.

When iterating as chunks, concurrent iteration might or might not require an allocation.

  • For instance, no allocation is required if the source data of the iterator is a vector, a slice or an array, etc.
  • On the other hand, an allocation of chunk_size is required if the source data is an any Iterator without further type information.

Pulling elements as chunks using the next_chunk or buffered_iter methods differ in the latter case as follows:

  • next_chunk will allocate a vector of next_chunk elements every time it is called;
  • buffered_iter will allocate a vector of next_chunk only once on construction, and this buffer will be used over and over until the concurrent iterator is consumed leading to negligible allocation.

Therefore, it is safer to always use buffered_iter, unless we need to keep changing the chunk_size through iteration, which is a rare scenario.

In a typical use case where we concurrently iterate over the elements of the iterator using num_threads threads:

  • we will create num_threads buffered iterators; i.e., we will call buffered_iter once from each thread,
  • each thread will allocate a vector of chunk_size capacity.

In total, the iteration will use an additional memory of num_threads * chunk_size. Note that the amount of allocation is not a function of the length of the source data. Assuming the length will be large in a scenario requiring parallel iteration, the allocation can be considered to be very small.

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§

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impl<'a, T, C> ConcurrentIter for Cloned<'a, T, C>
where T: Send + Sync + Clone, C: ConcurrentIter<Item = &'a T>,

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type BufferedIter = ClonedBufferedChunk<'a, T, <C as ConcurrentIter>::BufferedIter>

Source§

impl<'a, T, C> ConcurrentIter for Copied<'a, T, C>
where T: Send + Sync + Copy, C: ConcurrentIter<Item = &'a T>,

Source§

type BufferedIter = CopiedBufferedChunk<'a, T, <C as ConcurrentIter>::BufferedIter>

Source§

impl<Idx> ConcurrentIter for ConIterOfRange<Idx>
where Idx: Send + Sync + Clone + Copy + From<usize> + Into<usize> + Add<Idx, Output = Idx> + Sub<Idx, Output = Idx> + Ord, Range<Idx>: Iterator<Item = Idx>,

Source§

type BufferedIter = <ConIterOfRange<Idx> as ConcurrentIterX>::BufferedIterX

Source§

impl<T: Send + Sync> ConcurrentIter for ConIterOfSlice<'_, T>

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type BufferedIter = <ConIterOfSlice<'_, T> as ConcurrentIterX>::BufferedIterX

Source§

impl<T: Send + Sync> ConcurrentIter for ConIterOfVec<T>

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type BufferedIter = <ConIterOfVec<T> as ConcurrentIterX>::BufferedIterX

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impl<T: Send + Sync, Iter> ConcurrentIter for ConIterOfIter<T, Iter>
where Iter: Iterator<Item = T>,

Source§

type BufferedIter = <ConIterOfIter<T, Iter> as ConcurrentIterX>::BufferedIterX