The Lock-free $k$-LSM Relaxed Priority Queue

TL;DR

This paper introduces a lock-free, relaxed priority queue that allows deleting any of the  smallest keys, improving scalability and performance for concurrent applications by relaxing strict delete-min semantics.

Contribution

It presents a novel lock-free priority queue with a configurable relaxation parameter, built from log-structured merge-trees, enhancing scalability and efficiency in multiprocessor environments.

Findings

High performance compared to state-of-the-art lock-free priority queues

Good scalability demonstrated through experiments

Configurable relaxation improves throughput and scalability

Abstract

Priority queues are data structures which store keys in an ordered fashion to allow efficient access to the minimal (maximal) key. Priority queues are essential for many applications, e.g., Dijkstra's single-source shortest path algorithm, branch-and-bound algorithms, and prioritized schedulers. Efficient multiprocessor computing requires implementations of basic data structures that can be used concurrently and scale to large numbers of threads and cores. Lock-free data structures promise superior scalability by avoiding blocking synchronization primitives, but the \emph{delete-min} operation is an inherent scalability bottleneck in concurrent priority queues. Recent work has focused on alleviating this obstacle either by batching operations, or by relaxing the requirements to the \emph{delete-min} operation. We present a new, lock-free priority queue that relaxes the \emph{delete-min} operation so that it is allowed to delete \emph{any} of the $\rho+1$ smallest keys, where $\rho$ is a runtime configurable parameter. Additionally, the behavior is identical to a non-relaxed priority queue for items added and removed by the same thread. The priority queue is built from a logarithmic number of sorted arrays in a way similar to log-structured merge-trees. We experimentally compare our priority queue to recent state-of-the-art lock-free priority queues, both with relaxed and non-relaxed semantics, showing high performance and good scalability of our approach.