Elastic Relaxation of Concurrent Data Structures

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, stacks, a counter, and a deque, capable of reconfiguring relaxation during run-time. We establish linearizability and define worst-case bounds for relaxation errors in our designs. Experimental evaluations show that our elastic designs match the performance of state-of-the-art statically relaxed structures when no elastic changes are utilized. We develop a lightweight, contention-aware controller for adjusting relaxation in real time, and demonstrate its benefits both in a dynamic producer-consumer micro-benchmark and in a parallel BFS traversal, where it improves throughput and work-efficiency compared to static designs.