A general approach for supporting nonblocking data structures on distributed-memory systems

Thanh-Dang, Diep; Phuong, Hoai Ha und Fuerlinger, Karl (2022): A general approach for supporting nonblocking data structures on distributed-memory systems. In: Journal of Parallel and Distributed Computing, Bd. 173: S. 48-60

Abstract

Nonblocking data structures are an essential part of many parallel applications in that they can help to improve fault tolerance and performance. Although there are scores of nonblocking data structures, such as stacks, queues, double-ended queues (deques), lists, widely used in practice, most of them are designed to be used on shared-memory machines only, and cannot be used in a distributed-memory setting. Several recent studies focus on the development of novel tailor-made nonblocking distributed data structures and omit the potential for adapting a great wealth of existing nonblocking shared -memory ones for the distributed-memory case. Hence, we propose a general approach for bridging the gap between most existing nonblocking data structures and distributed-memory machines in this work. Several challenges, such as safe memory reclamation and solving the ABA problem, must be overcome. To address these issues, we present a global memory management scheme. The scheme takes advantage of hazard pointers which are widely used to tackle the problems in shared-memory environments. To demonstrate our general approach, we take stacks as a typical example of nonblocking data structures. This work also provides a survey of well-known nonblocking stack algorithms along with our analysis and evaluation in distributed-memory environments. Moreover, this paper depicts how to improve performance of a stack algorithm by making use of node locality. Besides, a cost model based on worst cases is devised to help gain a better understanding into experimental results of nonblocking distributed data structures, along with our analysis of the influence of two popular lock-free programming patterns on performance. (c) 2022 Elsevier Inc. All rights reserved.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISSN: 0743-7315
• Sprache: Englisch