Many concurrent algorithms require processes to perform fetch-and-add operations on a single memory location, which can be a hot spot of contention. We present a novel algorithm called Aggregating Funnels that reduces this contention by spreading the fetch-and-add operations across multiple memory locations. It aggregates fetch-and-add operations into batches so that the batch can be performed by a single hardware fetch-and-add instruction on one location and all operations in the batch can efficiently compute their results by performing a fetch-and-add instruction on a different location. We show experimentally that this approach achieves higher throughput than previous combining techniques, such as Combining Funnels, and is substantially more scalable than applying hardware fetch-and-add instructions on a single memory location. We show that replacing the fetch-and-add instructions in the fastest state-of-the-art concurrent queue by our Aggregating Funnels eliminates a bottleneck and greatly improves the queue's overall throughput.
@article{arxiv.2411.14420,
title = {Aggregating Funnels for Faster Fetch&Add and Queues},
author = {Younghun Roh and Yuanhao Wei and Eric Ruppert and Panagiota Fatourou and Siddhartha Jayanti and Julian Shun},
journal= {arXiv preprint arXiv:2411.14420},
year = {2025}
}
Comments
This is the full version of the paper appearing in PPoPP 2025