The design and implementation of efficient concurrent data structures has seen significant attention. However, most of this work has focused on concurrent data structures providing good worst-case guarantees, although, in real workloads, objects are often accessed at different rates. Efficient distribution-adaptive data structures, such as splay-trees, are known in the sequential case; however, they often are hard to translate efficiently to the concurrent case. We investigate distribution-adaptive concurrent data structures, and propose a new design called the splay-list. At a high level, the splay-list is similar to a standard skip-list, with the key distinction that the height of each element adapts dynamically to its access rate: popular elements “move up,” whereas rarely-accessed elements decrease in height. We show that the splay-list provides order-optimal amortized complexity bounds for a subset of operations, while being amenable to efficient concurrent implementation. Experiments show that the splay-list can leverage distribution-adaptivity for performance, and can outperform the only previously-known distribution-adaptive concurrent design in certain workloads.

高效并发数据结构的设计和实现受到了极大的关注。然而，大部分工作都集中在提供良好的最坏情况保证的并发数据结构上，尽管在实际工作负载中，对象通常以不同的速率访问。高效的分布自适应数据结构，例如 splay-trees，在顺序情况下是已知的；然而，它们通常很难有效地转化为并发案例。我们研究了分布式自适应并发数据结构，并提出了一种称为splay-list的新设计. 在高层次上，splay-list 类似于标准的 skip-list，关键区别在于每个元素的高度动态适应其访问率：流行元素“向上移动”，而很少访问的元素高度降低. 我们展示了 splay-list 为操作的子集提供了顺序最优的摊销复杂度边界，同时适合高效的并发实现。实验表明，splay-list 可以利用分布自适应性来提高性能，并且可以在某些工作负载中胜过之前唯一已知的分布自适应并发设计。