How to Relax Instantly: Elastic Relaxation of Concurrent Data Structures

TL;DR

This paper introduces elastic relaxation for concurrent data structures, enabling dynamic reconfiguration of relaxation levels during runtime to improve scalability and performance.

Contribution

It presents the Lateral structure for designing elastically relaxed, lock-free queues and stacks with adjustable relaxation, supporting better scalability and latency trade-offs.

Findings

Elastic relaxed queues and stacks outperform static relaxations in scalability.

The designs maintain linearizability and bounded relaxation errors.

Experimental results show improved performance over state-of-the-art relaxed structures.

Abstract

The sequential semantics of many concurrent data structures, such as stacks and queues, inevitably lead to memory contention in parallel environments, thus limiting scalability. Semantic relaxation has the potential to address this issue, increasing the parallelism at the expense of weakened semantics. Although prior research has shown that improved performance can be attained by relaxing concurrent data structure semantics, there is no one-size-fits-all relaxation that adequately addresses the varying needs of dynamic executions. In this paper, we first introduce the concept of elastic relaxation and consequently present the Lateral structure, which is an algorithmic component capable of supporting the design of elastically relaxed concurrent data structures. Using the Lateral , we design novel elastically relaxed, lock-free queues and stacks capable of reconfiguring relaxation during run time. We establish linearizability and define upper bounds for relaxation errors in our designs. Experimental evaluations show that our elastic designs hold up against state-of-the-art statically relaxed designs, while also swiftly managing trade-offs between relaxation and operational latency. We also outline how to use the Lateral to design elastically relaxed lock-free counters and deques.