The SprayList: A Scalable Relaxed Priority Queue

Dan Alistarh, Justin Kopinsky, Jerry Li, and Nir Shavit


High-performance concurrent priority queues are essential for applications such as task scheduling and discrete event simulation. Unfortunately even the best performing implementations do not scale past a number of threads in the single

digits. This is because of the sequential bottleneck in accessing the elements at the head of the queue in order to perform a DeleteMin operation.

In this paper, we present the SprayList, a scalable priority queue with relaxed ordering semantics. Starting from a nonblocking SkipList, the main innovation behind our design is that the DeleteMin operations avoid a sequential bottleneck by “spraying” themselves onto the head of the SkipList list in a coordinated fashion. The spraying is implemented using a

carefully designed random walk, so that DeleteMin always returns an element among the first O(p polylog p) in the list, where p is the number of threads. We prove that the expected running time of a DeleteMin operation is poly-logarithmic in p, independent of the size of the list, and also provide analytic upper bounds on the number of possible priority inversions for an element.

Our experiments show that the relaxed semantics allow the data structure to scale for very high thread counts, comparable to a classic unordered SkipList. Furthermore, we observe that, for reasonably parallel workloads, the scalability benefits of relaxation considerably outweigh the additional work due to out-of-order execution.


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