Tarmo: A Framework for Parallelized Bounded Model Checking

TL;DR

Tarmo is a flexible parallel framework for Bounded Model Checking that efficiently utilizes shared memory and clusters, supporting full PLTL properties and improving performance over traditional single-threaded methods.

Contribution

We introduce Tarmo, a generic parallel BMC framework compatible with incremental SAT encoding, enabling efficient distributed and shared memory verification for safety and liveness properties.

Findings

Shared memory variants outperform single-threaded approaches.

Framework effectively scales in cluster environments.

Supports full PLTL property checking.

Abstract

This paper investigates approaches to parallelizing Bounded Model Checking (BMC) for shared memory environments as well as for clusters of workstations. We present a generic framework for parallelized BMC named Tarmo. Our framework can be used with any incremental SAT encoding for BMC but for the results in this paper we use only the current state-of-the-art encoding for full PLTL. Using this encoding allows us to check both safety and liveness properties, contrary to an earlier work on distributing BMC that is limited to safety properties only. Despite our focus on BMC after it has been translated to SAT, existing distributed SAT solvers are not well suited for our application. This is because solving a BMC problem is not solving a set of independent SAT instances but rather involves solving multiple related SAT instances, encoded incrementally, where the satisfiability of each instance corresponds to the existence of a counterexample of a specific length. Our framework includes a generic architecture for a shared clause database that allows easy clause sharing between SAT solver threads solving various such instances. We present extensive experimental results obtained with multiple variants of our Tarmo implementation. Our shared memory variants have a significantly better performance than conventional single threaded approaches, which is a result that many users can benefit from as multi-core and multi-processor technology is widely available. Furthermore we demonstrate that our framework can be deployed in a typical cluster of workstations, where several multi-core machines are connected by a network.