The future(s) of shared data structures

Abstract

This paper considers how to use futures, a well-known mechanism to manage parallel computations, to improve the performance of long-lived, mutable shared data structures in large-scale multicore systems. We show that futures can enable type-specific optimizations such as combining and elimination, improve cache locality and reduce contention. To exploit these benefits in an effective way, however, it is important to define clear notions of correctness. We propose new extensions to linearizability appropriate for method calls that return futures as results. To illustrate the utility and trade-offs of these extensions, we describe implementations of three common data structures: stacks, queues, and linked lists, designed to exploit futures. Our experimental results show that optimizations enabled by futures lead to substantial performance improvements, in some cases up to two orders of magnitude, compared to well-known lock-free alternatives.