Aggregating Funnels for Faster Fetch&Add and Queues

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Abstract
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.
Funding Information
  • Google Research Scholar Award
  • MIT UROP Funding - Ralph L. Evans (1948) Endowment Fund
  • Greek Ministry of Education Religious Affairs and Sports under the call SUB 1.1 -- Research Excellence Partnerships (HARSH \Ypsilon\Pi 3TA-0560901)
  • Natural Sciences and Engineering Research Council of Canada
  • National Science Foundation Grants (CCF-1845763, CCF-2316235, CCF-2403237)
  • Google Faculty Research Award
  • Hellenic Foundation for Research and Innovation (HFRI) under the ``Second Call for HFRI Research Projects to support Faculty Members and Researchers' (PERSIST3684)

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