orx-concurrent-bag

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

• convenient: ConcurrentBag 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.
• efficient: ConcurrentBag is a lock free structure suitable for concurrent, copy-free and high performance growth. You may see benchmarks and further performance notes for details.

Examples

Underlying PinnedVec guarantees make it straightforward to safely grow with a shared reference which leads to a convenient api as demonstrated below.

use orx_concurrent_bag::*;

let bag = ConcurrentBag::new();

let (num_threads, num_items_per_thread) = (4, 1_024);

let bag_ref= &bag;
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);

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 allocation 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();

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();

Concurrent State and Properties

The concurrent state is modeled simply by an atomic length. 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.
• Each position is written exactly once.
• ⟹ no write & write race condition exists.
• Only one growth can happen at a given time. Growth is copy-free and does not change memory locations of already pushed elements.
• Underlying pinned vector is always valid and can be taken out any time by into_inner(self).
• Reading is only possible after converting the bag into the underlying PinnedVec.
• ⟹ no read & write race condition exists.

Concurrent Friend Collections

Benchmarks

Performance with push

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

In the experiment, rayons parallel iterator and ConcurrentBags push method are used to collect results from multiple threads.

// reserve and push one position at a time
for j in 0..num_items_per_thread {
    bag.push(i * 1000 + j);
}

We observe that ConcurrentBag allows for high performance concurrent collection of elements, which is achieved by simple for loops and push method which looks much like sequential code.

The performance can further be improved by using extend method instead of push. You may see results in the next subsection and details in the performance notes.

Performance with extend

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

The only difference in this follow up experiment is that we use extend rather than push with ConcurrentBag. The expectation is that this approach will solve the performance degradation due to false sharing in the small data & little work situation.

In a perfectly homogeneous scenario, we can evenly share the work to threads as follows.

// reserve num_items_per_thread positions at a time
// and then push as the iterator yields
let iter = (0..num_items_per_thread).map(|j| i * 100000 + j);
ref.extend(iter);

However, we do not need to have perfect homogeneity or perfect information on the number of items to be pushed per thread to get the benefits of extend. We can simply step_by and extend by batch_size elements. A large enough batch_size so that batch size elements exceed a cache line would be sufficient prevent the dramatic performance degradation of false sharing.

// reserve batch_size positions at each iteration
// and then push as the iterator yields
for j in (0..num_items_per_thread).step_by(batch_size) {
    let iter = (j..(j + batch_size)).map(|j| i * 100000 + j);
    bag.extend(iter);
}

Using a batch size of 64 with extend provides a significant performance improvement.

Performance Notes

How many times and how long we spin?

There is only one waiting or spinning condition of the push and extend methods: whenever the underlying PinnedVec needs to grow. Note that growth with pinned vector is copy free. Therefore, when it spins, all it waits for is the allocation. Since number of growth is deterministic, so is the number of spins.

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 will allocate exactly 12 times during the entire execution.
• If we use a SplitVec<_, Linear> with constant fragment lengths of 1_024, we will allocate 15 equal capacity fragments.
• 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.

False Sharing

We need to be aware of the potential false sharing risk which might lead to significant performance degradation when we are filling the bag with one by one with ConcurrentBag::push.

Performance degradation due to false sharing might be observed specifically when both of the following conditions hold:

• small data: data to be pushed is small, the more elements fitting in a cache line the bigger the risk,
• little work: multiple threads/cores are pushing to the concurrent bag with high frequency; i.e., very little or negligible work / time is required in between push calls.

The example above fits this situation. Each thread only performs one multiplication and addition in between pushing elements, and the elements to be pushed are small.

• ConcurrentBag assigns unique positions to each value to be pushed. There is no true sharing among threads in the position level.
• However, cache lines contain more than one position. One thread updating a particular position invalidates the entire cache line on an other thread.
• Threads end up frequently reloading cache lines instead of doing the actual work of writing elements to the bag. This might lead to a significant performance degradation.

extend to Avoid False Sharing

Assume that we are filling a ConcurrentBag from n threads. At any given point, thread A calls extend by passing in an iterator which will yield 64 elements. Concurrent bag will reserve 64 consecutive positions for this extend call. Concurrent push or extend calls from other threads will not have access to these positions. Assuming that size of 64 elements is large enough:

• Thread A writing to these 64 positions will not invalidate cache lines of other threads. Similarly, other threads writing to their reserved positions will not invalidate thread A's cache line.
• Further, this will reduce the number of atomic updates compared to pushing elements one at a time.

Required change in the code from push to extend is not significant. The example above could be revised as follows to avoid the performance degrading of false sharing.

use orx_concurrent_bag::*;

let (num_threads, num_items_per_thread) = (4, 1_024);
let bag = ConcurrentBag::new();
let batch_size = 64;

let bag_ref= &bag;
std::thread::scope(|s| {
    for i in 0..num_threads {
        s.spawn(move || {
            for j in (0..num_items_per_thread).step_by(batch_size) {
                let iter = (j..(j + batch_size)).map(|j| i * 1000 + j);
                bag_ref.extend(iter);
            }
        });
    }
});

Contributing

Contributions are welcome! If you notice an error, have a question or think something could be improved, please open an issue or create a PR.

License

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