The following experiments focus only on the concurrent growth or collection of the elements.

In the first part, rayon's parallel iterator, and push methods of AppendOnlyVec, boxcar::Vec and ConcurrentVec are used to collect results from multiple threads. Further, different underlying pinned vectors of the ConcurrentVec are evaluated. You may find the details of the benchmarks at benches/collect_with_push.rs.

We observe that:

• The default Doubling growth strategy leads to efficient concurrent collection of results. Note that this variant does not require any input to construct.
• On the other hand, Linear growth strategy performs significantly better. Note that value of this argument means that each fragment of the underlying SplitVec will have a capacity of 2^12 (4096) elements. The underlying reason of improvement is potentially be due to less waste and could be preferred with minor knowledge of the data to be pushed.
• Finally, Fixed growth strategy is the least flexible and requires perfect knowledge about the hard-constrained capacity (will panic if we exceed). Since it does not outperform Linear, we do not necessarily prefer Fixed even if we have the perfect knowledge.

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 of ConcurrentBag which has similar characteristics.

The only difference in this follow up experiment is that we use extend rather than push with ConcurrentVec. You may find the details of the benchmarks at benches/collect_with_extend.rs.

The expectation is that this approach will solve the performance degradation due to false sharing, which turns out to be true:

• Extending rather than pushing might double the growth performance.
• There is not a significant difference between extending by batches of 64 elements or batches of 65536 elements. We do not need a well tuned number, a large enough batch size seems to be just fine.
• Not all scenarios allow to extend in batches; however, the significant performance improvement makes it preferable whenever possible.