Scalable Dynamic Partial Order Reduction

Abstract

Systematic testing, first demonstrated in small, specialized cases 15 years ago, has matured sufficiently for large-scale systems developers to begin to put it into practice. With actual deployment come new, pragmatic challenges to the usefulness of the techniques. In this paper we are concerned with scaling dynamic partial order reduction, a key technique for mitigating the state space explosion problem, to very large clusters. In particular, we present a new approach for distributed dynamic partial order reduction. Unlike previous work, our approach is based on a novel exploration algorithm that 1) enables trading space complexity for parallelism, 2) achieves efficient load-balancing through time-slicing, 3) provides for fault tolerance, which we consider a mandatory aspect of scalability, 4) scales to more than a thousand parallel workers, and 5) is guaranteed to avoid redundant exploration of overlapping portions of the state space.

This research was sponsored by the U.S. Army Research Office under grant number W911NF0910273. The authors are also thankful to Google for providing its hardware and software infrastructure for the evaluation presented in this paper. Further, we also thank the members and companies of the PDL Consortium. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity.