Publication Date



PODC '23: Proceedings of the 2023 ACM Symposium on Principles of Distributed Computing


A multiplicity queue is a concurrently-defined data type which relaxes the conditions of a linearizable FIFO queue by allowing concurrent Dequeue instances to return the same value. It would seem that this should allow faster message-passing implementations, as processes should not need to wait as long to learn about concurrent operations and previous work has shown that multiplicity queues are computationally less complex than the unrelaxed version. Intriguingly, recent work has shown that there is, in fact, little possible speedup versus an unrelaxed queue. Seeking to understand this difference between intuition and real behavior, we increase the lower bound for uniform algorithms. Further, we outline a path toward building proofs for even higher lower bounds, hypothesizing that the worst-case time to Dequeue approaches maximum message delay, which is similar to the time required for an unrelaxed Dequeue. We also give an upper bound for a special case to show that our bounds are tight at that point. To achieve our lower bounds, we use extended shifting arguments, which have been rarely used but allow larger lower bounds than traditional shifting arguments. We use these in series of inductive indistinguishability proofs which allow us to extend our proofs beyond the usual limitations of shifting arguments. This proof structure is an interesting contribution independently of the main result, as developing new lower bound proof techniques may have many uses in future work.




Computer Science

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