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The Splay-List: A Distribution-Adaptive Concurrent Skip-List

Authors Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami

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Author Details

Vitaly Aksenov
  • ITMO, St. Petersburg, Russia
Dan Alistarh
  • IST Austria, Klosterneuburg, Austria
Alexandra Drozdova
  • ITMO University, St. Petersburg, Russia
Amirkeivan Mohtashami
  • Sharif University of Technology, Tehran, Iran

Funding

  • Aksenov, Vitaly: Government of Russian Federation (Grant 08-08).
  • Alistarh, Dan: ERC Starting Grant 805223 ScaleML.

Cite AsGet BibTex

Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. The Splay-List: A Distribution-Adaptive Concurrent Skip-List. In 34th International Symposium on Distributed Computing (DISC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 179, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.DISC.2020.3

Abstract

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. In real workloads, objects are often accessed at different rates, since access distributions may be non-uniform. Efficient distribution-adaptive data structures are known in the sequential case, e.g. the splay-trees; however, they often are hard to translate efficiently in the concurrent case. In this paper, 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. Experimental results show that the splay-list can leverage distribution-adaptivity to improve on the performance of classic concurrent designs, and can outperform the only previously-known distribution-adaptive design in certain settings.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
  • Theory of computation → Concurrent algorithms
Keywords
  • Data structures
  • self-adjusting
  • concurrency

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