A Wait-Free Universal Construct for Large Objects

TL;DR

This paper introduces CX, a multi-instance wait-free universal construct that reduces copying overhead, enabling efficient concurrent access to large objects and outperforming existing methods in various data structure implementations.

Contribution

The paper presents CX, a novel multi-instance wait-free universal construct that significantly decreases copy operations for large objects, improving performance and scalability.

Findings

CX outperforms existing wait-free constructs in most experiments.

CX rivals hand-written lock-free and wait-free data structures.

It provides wait-free progress, safe memory reclamation, and high reader scalability.

Abstract

Concurrency has been a subject of study for more than 50 years. Still, many developers struggle to adapt their sequential code to be accessed concurrently. This need has pushed for generic solutions and specific concurrent data structures. Wait-free universal constructs are attractive as they can turn a sequential implementation of any object into an equivalent, yet concurrent and wait-free, implementation. While highly relevant from a research perspective, these techniques are of limited practical use when the underlying object or data structure is sizable. The copy operation can consume much of the CPU's resources and significantly degrade performance. To overcome this limitation, we have designed CX, a multi-instance-based wait-free universal construct that substantially reduces the amount of copy operations. The construct maintains a bounded number of instances of the object that can potentially be brought up to date. We applied CX to several sequential implementations of data structures, including STL implementations, and compared them with existing wait-free constructs. Our evaluation shows that CX performs significantly better in most experiments, and can even rival with hand-written lock-free and wait-free data structures, simultaneously providing wait-free progress, safe memory reclamation and high reader scalability.