orx-concurrent-bag

An efficient, convenient and lightweight grow-only concurrent collection, ideal for collecting results concurrently.

convenient: ConcurrentBag can safely be shared among threads simply as a shared reference. Further, it is just a wrapper around any PinnedVec implementation adding concurrent safety guarantees. Therefore, underlying pinned vector and concurrent bag can be converted to each other back and forth without any cost.
lightweight: This crate takes a simplistic approach built on pinned vector guarantees which leads to concurrent programs with few dependencies and small binaries (see approach and safety for details).
efficient: ConcurrentBag is a lock free structure making use of a few atomic primitives. rayon is significantly faster when collecting small results under an extreme load (negligible work to compute results); however, ConcurrentBag starts to perform faster as result types get larger (see benchmarks for the experiments).

Examples

Safety guarantees to push to the bag with an immutable reference makes it easy to share the bag among threads. std::sync::Arc can be used; however, it is not required as demonstrated below.

use orx_concurrent_bag::*;

let (num_threads, num_items_per_thread) = (4, 8);

let bag = ConcurrentBag::new();
let bag_ref = &bag; // just take a reference and share among threads

std::thread::scope(|s| {
    for i in 0..num_threads {
        s.spawn(move || {
            for j in 0..num_items_per_thread {
                // concurrently collect results simply by calling `push`
                bag_ref.push(i * 1000 + j);
            }
        });
    }
});

let mut vec_from_bag: Vec<_> = bag.into_inner().iter().copied().collect();
vec_from_bag.sort();
let mut expected: Vec<_> = (0..num_threads).flat_map(|i| (0..num_items_per_thread).map(move |j| i * 1000 + j)).collect();
expected.sort();
assert_eq!(vec_from_bag, expected);

Approach and Safety

ConcurrentBag aims to enable concurrent growth with a minimalistic approach. It requires two major components for this:

The underlying storage, which is any PinnedVec implementation. This means that memory locations of elements that are already pushed to the vector will never change, unless explicitly changed. This guarantee eliminates a certain set of safety concerns and corresponding complexity.
An atomic counter that is responsible for uniquely assigning one vector position to one and only one thread. std::sync::atomic::AtomicUsize and its fetch_add method are sufficient for this.

Simplicity and safety of the approach can be observed in the implementation of the push method.

pub fn push(&self, value: T) -> usize {
    let idx = self.len.fetch_add(1, Ordering::AcqRel);
    self.assert_has_capacity_for(idx);

    loop {
        let capacity = self.capacity.load(Ordering::Relaxed);

        match idx.cmp(&capacity) {
            // no need to grow, just push
            std::cmp::Ordering::Less => {
                self.write(idx, value);
                break;
            }

            // we are responsible for growth
            std::cmp::Ordering::Equal => {
                let new_capacity = self.grow_to(capacity + 1);
                self.write(idx, value);
                self.capacity.store(new_capacity, Ordering::Relaxed);
                break;
            }

            // spin to wait for responsible thread to handle growth
            std::cmp::Ordering::Greater => {}
        }
    }

    idx
}

Below are some details about this implementation:

fetch_add guarantees that each pushed value receives a unique idx.
assert_has_capacity_for method is an additional safety guarantee added to pinned vectors to prevent any possible UB. It is not constraining for practical usage, see [ConcurrentBag::maximum_capacity] for details.
Inside the loop, we read the current capacity and compare it with idx:
- idx < capacity:
  - The idx-th position is already allocated and belongs to the bag. We can simply write. Note that concurrent bag is write-only. Therefore, there is no other thread writing to or reading from this position; and hence, no race condition is present.
- idx > capacity:
  - The idx-th position is not yet allocated. Underlying pinned vector needs to grow.
  - But another thread is responsible for the growth, we simply wait.
- idx == capacity:
  - The idx-th position is not yet allocated. Underlying pinned vector needs to grow.
  - Further, we are responsible for the growth. Note that this guarantees that:
    - Only one thread will make the growth calls.
    - Only one growth call can take place at a given time.
    - There exists no race condition for the growth.
  - We first grow the pinned vector, then write to the idx-th position, and finally update the capacity to the new capacity.

How many times will we spin?

This is deterministic. It is exactly equal to the number of growth calls of the underlying pinned vector, and pinned vector implementations give a detailed control on this. For instance, assume that we will push a total of 15_000 elements concurrently to an empty bag.

Further assume we use the default SplitVec<_, Doubling> as the underlying pinned vector. Throughout the execution, we will allocate fragments of capacities [4, 8, 16, ..., 4096, 8192] which will lead to a total capacity of 16_380. In other words, we might possibly visit the std::cmp::Ordering::Greater => {} block in 12 points in time during the entire execution.
If we use a SplitVec<_, Linear> with constant fragment lengths of 1_024, we will allocate 15 equal capacity fragments, which will lead to a total capacity of 15_360. So looping might only happen 15 times. We can drop this number to 8 if we set constant fragment capacity to 2_048; i.e., we can control the frequency of allocations.
If we use the strict FixedVec<_>, we have to pre-allocate a safe amount and can never grow beyond this number. Therefore, there will never be any spinning.

When we spin, how long do we spin?

Not long because:

Pinned vectors do not change memory locations of already pushed elements. In other words, growths are copy-free.
We are only waiting for allocation of memory required for the growth with respect to the chosen growth strategy.

Construction

ConcurrentBag can be constructed by wrapping any pinned vector; i.e., ConcurrentBag<T> implements From<P: PinnedVec<T>>. Likewise, a concurrent vector can be unwrapped without any cost to the underlying pinned vector with into_inner method.

Further, there exist with_ methods to directly construct the concurrent bag with common pinned vector implementations.

use orx_concurrent_bag::*;

// default pinned vector -> SplitVec<T, Doubling>
let bag: ConcurrentBag<char> = ConcurrentBag::new();
let bag: ConcurrentBag<char> = Default::default();
let bag: ConcurrentBag<char> = ConcurrentBag::with_doubling_growth();
let bag: ConcurrentBag<char, SplitVec<char, Doubling>> = ConcurrentBag::with_doubling_growth();

let bag: ConcurrentBag<char> = SplitVec::new().into();
let bag: ConcurrentBag<char, SplitVec<char, Doubling>> = SplitVec::new().into();

// SplitVec with [Linear](https://docs.rs/orx-split-vec/latest/orx_split_vec/struct.Linear.html) growth
// each fragment will have capacity 2^10 = 1024
// and the split vector can grow up to 32 fragments
let bag: ConcurrentBag<char, SplitVec<char, Linear>> = ConcurrentBag::with_linear_growth(10, 32);
let bag: ConcurrentBag<char, SplitVec<char, Linear>> = SplitVec::with_linear_growth_and_fragments_capacity(10, 32).into();

// [FixedVec](https://docs.rs/orx-fixed-vec/latest/orx_fixed_vec/) with fixed capacity.
// Fixed vector cannot grow; hence, pushing the 1025-th element to this bag will cause a panic!
let bag: ConcurrentBag<char, FixedVec<char>> = ConcurrentBag::with_fixed_capacity(1024);
let bag: ConcurrentBag<char, FixedVec<char>> = FixedVec::new(1024).into();

Of course, the pinned vector to be wrapped does not need to be empty.

use orx_concurrent_bag::*;

let split_vec: SplitVec<i32> = (0..1024).collect();
let bag: ConcurrentBag<_> = split_vec.into();

Write-Only vs Read-Write

The concurrent bag is a write-only bag which is convenient and efficient for collecting elements.

See ConcurrentVec for a read-and-write variant which

guarantees that reading and writing never happen concurrently, and hence,
allows safe iteration or access to already written elements of the concurrent vector.

However, ConcurrentVec<T> requires to use a PinnedVec<Option<T>> as the underlying storage rather than PinnedVec<T>.

Benchmarks

You may find the details of the benchmarks at benches/grow.rs.

In the experiment, ConcurrentBag variants and rayon is used to collect results from multiple threads. You may see in the table below that rayon is extremely fast with very small output data (i32 in this case). As the output size gets larger and copies become costlier, ConcurrentBag starts to perform faster.

License

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

orx-concurrent-bag 1.6.0