Hash Compaction Research Articles

Distributed LTL Model Checking with Hash Compaction

We extend a distributed-memory explicit-state LTL model checking algorithm (OWCTY) with hash compaction. We provide a detailed description of the improved algorithm and a correctness argument in the theoretical part of the paper. Additionally, we deliver an implementation of the algorithm as part of out parallel and distributed-memory model checker DiVinE, and use this implementation for a practical evaluation of the approach, on which we report in the experimental part of the paper.

Exploiting transition locality in automatic verification of finite-state concurrent systems

In this paper we show that statistical properties of the transition graph of a system to be verified can be exploited to improve memory or time performances of verification algorithms. We show experimentally that protocols exhibit transition locality. That is, with respect to levels of a breadth-first state space exploration, state transitions tend to be between states belonging to close levels of the transition graph. We support our claim by measuring transition locality for the set of protocols included in the Mur? verifier distribution . We present a cache-based verification algorithm that exploits transition locality to decrease memory usage and a disk-based verification algorithm that exploits transition locality to decrease disk read accesses, thus reducing the time overhead due to disk usage. Both algorithms have been implemented within the Mur? verifier. Our experimental results show that our cache-based algorithm can typically save more than 40% of memory with an average time penalty of about 50% when using (Mur?) bit compression and 100% when using bit compression and hash compaction, whereas our disk-based verification algorithm is typically more than ten times faster than a previously proposed disk-based verification algorithm and, even when using 10% of the memory needed to complete verification, it is only between 40 and 530% (300% on average) slower than (RAM) Mur? with enough memory to complete the verification task at hand. Using just 300 MB of memory our disk-based Mur? was able to complete verification of a protocol with about 109 reachable states. This would require more than 5 GB of memory using standard Mur? .