Content area

Abstract

Determinism is very useful to multithreaded programs in debugging, testing, etc. Many deterministic approaches have been proposed, such as deterministic multithreading (DMT) and deterministic replay. However, these systems either are inefficient or target a single purpose, which is not flexible. In this paper, we propose an efficient and flexible deterministic framework for multithreaded programs. Our framework implements determinism in two steps: relaxed determinism and strong determinism. Relaxed determinism solves data races efficiently by using a proper weak memory consistency model. After that, we implement strong determinism by solving lock contentions deterministically. Since we can apply different approaches for these two steps independently, our framework provides a spectrum of deterministic choices, including nondeterministic system (fast), weak deterministic system (fast and conditionally deterministic), DMT system, and deterministic replay system. Our evaluation shows that the DMT configuration of this framework could even outperform a state-of-the-art DMT system.

Details

Title
An Efficient and Flexible Deterministic Framework for Multithreaded Programs
Author
Lu, Kai 1 ; Zhou, Xu 2 ; Wang, Xiao-Ping 1 ; Bergan, Tom 3 ; Chen, Chen 1 

 National University of Defense Technology, Science and Technology on Parallel and Distributed Processing Laboratory, Changsha, China (GRID:grid.412110.7) (ISNI:0000000095482110); National University of Defense Technology, College of Computer, Changsha, China (GRID:grid.412110.7) (ISNI:0000000095482110) 
 National University of Defense Technology, College of Computer, Changsha, China (GRID:grid.412110.7) (ISNI:0000000095482110) 
 University of Washington, Department of Computer Science and Engineering, Seattle, U.S.A (GRID:grid.34477.33) (ISNI:0000000122986657) 
Pages
42-56
Publication year
2015
Publication date
Jan 2015
Publisher
Springer Nature B.V.
ISSN
10009000
e-ISSN
18604749
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1646984602
Copyright
© Springer Science+Business Media New York 2015.