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Priority queues are fundamental abstract data structures, often used tomanage limited resources in parallel programming. Several proposed parallelpriority queue implementations are based on skiplists, harnessing the potentialfor parallelism of the add() operations. In addition, methods such as FlatCombining have been proposed to reduce contention by batching together multipleoperations to be executed by a single thread. While this technique can decreaselock-switching overhead and the number of pointer changes required by theremoveMin() operations in the priority queue, it can also create a sequentialbottleneck and limit parallelism, especially for non-conflicting add()operations.  In this paper, we describe a novel priority queue design, harnessing thescalability of parallel insertions in conjunction with the efficiency ofbatched removals. Moreover, we present a new elimination algorithm suitable fora priority queue, which further increases concurrency on balanced workloadswith similar numbers of add() and removeMin() operations. We implement andevaluate our design using a variety of techniques including locking, atomicoperations, hardware transactional memory, as well as employing adaptiveheuristics given the workload.

The Adaptive Priority Queue with Elimination and Combining