orx-concurrent-ordered-bag

An efficient, convenient and lightweight grow-only concurrent data structure allowing high performance and ordered concurrent collection.

• convenient: ConcurrentOrderedBag can safely be shared among threads simply as a shared reference. It is a PinnedConcurrentCol with a special concurrent state implementation. Underlying PinnedVec and concurrent bag can be converted back and forth to each other. The main goal of this collection is to enable efficient parallel operations with very simple implementations.
• efficient: ConcurrentOrderedBag is a lock free structure suitable for concurrent, copy-free and high performance growth while enabling to collect the results in the desired order.

Safety Requirements

Unlike ConcurrentBag and ConcurrentVec, collection into a CollectionOrderedBag is through unsafe setter methods which are flexible in allowing to write at any position of the bag at any order. In order to use the bag safely, the caller is expected to satisfy the following two safety requirements:

• Each position is written exactly once, so that there exists no race condition.
• At the point where into_inner is called to get the underlying vector of collected elements, the bag must not contain any gaps.
  • Let m be the maximum index of the position that we write an element to.
  • The bag assumes that the length of the vector is equal to m + 1.
  • Then, it expects that exactly m + 1 elements are written to the bag.
  • If the first condition was also satisfied; then, this condition is sufficient to conclude that the bag has no gaps and can be unwrapped.

Satisfying these two conditions is easy in certain situations and harder in others. A good idea in complicated cases is to pair ConcurrentOrderedBag with a ConcurrentIter to greatly mitigate complexity and safety risks, please see the parallel map example below.

Examples

Manual Example

In the following example, we split computation among two threads: the first thread processes inputs with even indices, and the second with odd indices. This fulfills the safety requirements mentioned above.

use orx_concurrent_ordered_bag::*;

let n = 1024;

let evens_odds = ConcurrentOrderedBag::new();

// just take a reference and share among threads
let bag = &evens_odds;

std::thread::scope(|s| {
    s.spawn(move || {
        for i in (0..n).filter(|x| x % 2 == 0) {
            unsafe { bag.set_value(i, i as i32) };
        }
    });

    s.spawn(move || {
        for i in (0..n).filter(|x| x % 2 == 1) {
            unsafe { bag.set_value(i, -(i as i32)) };
        }
    });
});

let vec = unsafe { evens_odds.into_inner().unwrap_only_if_counts_match() };
assert_eq!(vec.len(), n);
for i in 0..n {
    if i % 2 == 0 {
        assert_eq!(vec[i], i as i32);
    } else {
        assert_eq!(vec[i], -(i as i32));
    }
}

Note that as long as no-gap and write-only-once guarantees are satisfied, ConcurrentOrderedBag is very flexible in the order of writes. Consider the following example. We spawn a thread just two write to the end of the collection. Then we spawn a bunch of other threads to fill the beginning of the collection. This just works without any locks or waits.

use orx_concurrent_ordered_bag::*;

let n = 1024;
let num_additional_threads = 4;

let bag = ConcurrentOrderedBag::new();
let con_bag = &bag;

std::thread::scope(|s| {
    s.spawn(move || {
        // start writing to the end
        unsafe { con_bag.set_value(n - 1, 42) };
    });

    for thread in 0..num_additional_threads {
        s.spawn(move || {
            // then fill the rest concurrently from the beginning
            for i in (0..(n - 1)).filter(|i| i % num_additional_threads == thread) {
                unsafe { con_bag.set_value(i, i as i32) };
            }
        });
    }
});

let vec = unsafe { bag.into_inner().unwrap_only_if_counts_match() };
assert_eq!(vec.len(), n);
for i in 0..(n - 1) {
    assert_eq!(vec[i], i as i32);
}
assert_eq!(vec[n - 1], 42);

These examples represent cases where the work can be trivially split among threads while providing the safety requirements. In a general case, it requires special care to fulfill the safety requirements. This complexity and safety risks can significantly be avoided by pairing the ConcurrentOrderedBag with a ConcurrentIter on the input side.

Parallel Map with ConcurrentIter

Parallel map operation is one of the cases where we care about the order of the collected elements, and hence, a ConcurrentBag would not do. On the other hand, a very simple yet efficient implementation can be achieved with ConcurrentOrderedBag and ConcurrentIter.

use orx_concurrent_ordered_bag::*;
use orx_concurrent_iter::*;

fn parallel_map<In, Out, Map, Inputs>(
    num_threads: usize,
    inputs: Inputs,
    map: &Map,
) -> ConcurrentOrderedBag<Out>
where
    Inputs: ConcurrentIter<Item = In>,
    Map: Fn(In) -> Out + Send + Sync,
    Out: Send + Sync,
{
    let outputs = ConcurrentOrderedBag::new();
    let inputs = &inputs;
    let out = &outputs;
    std::thread::scope(|s| {
        for _ in 0..num_threads {
            s.spawn(|| {
                while let Some(next) = inputs.next_id_and_value() {
                    unsafe { out.set_value(next.idx, map(next.value)) };
                }
            });
        }
    });
    outputs
}

let len = 2465;
let input: Vec<_> = (0..len).map(|x| x.to_string()).collect();

let bag = parallel_map(4, input.into_con_iter(), &|x| x.to_string().len());

let output = unsafe { bag.into_inner().unwrap_only_if_counts_match() };
assert_eq!(output.len(), len);
for (i, value) in output.iter().enumerate() {
    assert_eq!(value, &i.to_string().len());
}

As you may see, no manual work or care is required to satisfy the safety requirements. Each element of the iterator is processed and written exactly once, just as it would in a sequential implementation.

Parallel Map with ConcurrentIter

A further performance improvement to the parallel map implementation above is to distribute the tasks among the threads in chunks. The aim of this approach is to avoid false sharing, you may see further details here. This can be achieved by pairing an ConcurrentIter rather than a ConcurrentIter with the set_values method of the ConcurrentOrderedBag.

use orx_concurrent_ordered_bag::*;
use orx_concurrent_iter::*;

fn parallel_map<In, Out, Map, Inputs>(
    num_threads: usize,
    inputs: Inputs,
    map: &Map,
    chunk_size: usize,
) -> ConcurrentOrderedBag<Out>
where
    Inputs: ConcurrentIter<Item = In>,
    Map: Fn(In) -> Out + Send + Sync,
    Out: Send + Sync,
{
    let outputs = ConcurrentOrderedBag::new();
    let inputs = &inputs;
    let out = &outputs;
    std::thread::scope(|s| {
        for _ in 0..num_threads {
            s.spawn(|| {
                while let Some(next) = inputs.next_chunk(chunk_size) {
                    unsafe { out.set_values(next.begin_idx, next.values.map(map)) };
                }
            });
        }
    });
    outputs
}

let len = 2465;
let input: Vec<_> = (0..len).map(|x| x.to_string()).collect();
let bag = parallel_map(4, input.into_con_iter(), &|x| x.to_string().len(), 64);

let output = unsafe { bag.into_inner().unwrap_only_if_counts_match() };
for (i, value) in output.iter().enumerate() {
    assert_eq!(value, &i.to_string().len());
}

Concurrent State and Properties

The concurrent state is modeled simply by an atomic capacity. Combination of this state and PinnedConcurrentCol leads to the following properties:

• Writing to a position of the collection does not block other writes, multiple writes can happen concurrently.
• Caller is required to guarantee that each position is written exactly once.
• ⟹ caller is responsible to avoid write & write race conditions.
• Only one growth can happen at a given time.
• Reading is only possible after converting the bag into the underlying PinnedVec.
• ⟹ no read & write race condition exists.

Concurrent Friend Collections

License

This library is licensed under MIT license. See LICENSE for details.