Abstract: Model checking of safety properties can be scaled up by pooling the CPU and memory resources of multiple computers. As compute clusters containing 100s of nodes, with each node realized using multi-core (e.g., 2) CPUs will be widespread, a model checker based on the parallel (shared memory) and distributed (message passing) paradigms will more efficiently use the hardware resources. Such a model checker can be designed by having each node employ two shared memory threads that run on the (typically) two CPUs of a node, with one thread responsible for state generation, and the other for efficient communication, including (i) performing overlapped asynchronous message passing, and (ii) aggregating the states to be sent into larger chunks in order to improve communication network utilization. We present the design details of such a novel model checking architecture called Eddy. We describe the design rationale, details of how the threads interact and yield control, exchange messages, as well as detect termination. We have realized an instance of this architecture for the Murphi modeling language. Called Eddy_Murphi, we report its performance over the number of nodes as well as communication parameters such as those controlling state aggregation. Nearly linear reduction of compute time with increasing number of nodes is observed. Our thread task partition is done in such a way that it is modular, easy to port across different modeling languages, and easy to tune across a variety of platforms.

Abstract: We present a methodology for the synthesis of controllers, which exploits (explicit) model checking techniques. That is, we can cope with the systematic exploration of a very large state space. This methodology can be applied to systems where other approaches fail. In particular, we can consider systems with an highly non-linear dynamics and lacking a uniform mathematical description (model). We can also consider situations where the required control action cannot be specified as a local action, and rather a kind of planning is required. Our methodology individuates first a raw optimal controller, then extends it to obtain a more robust one. A case study is presented which considers the well known truck-trailer obstacle avoidance parking problem, in a parking lot with obstacles on it. The complex non-linear dynamics of the truck-trailer system, within the presence of obstacles, makes the parking problem extremely hard. We show how, by our methodology, we can obtain optimal controllers with different degrees of robustness.

Abstract: One of the most successful approach to automatic software verification is SAT based bounded model checking (BMC). One of the main factors limiting the size of programs that can be automatically verified via BMC is the huge number of clauses that the backend SAT solver has to process. In fact, because of this, the SAT solver may easily run out of RAM. We present two disk based algorithms that can considerably decrease the number of clauses that a BMC backend SAT solver has to process in RAM. Our experimental results show that using our disk based algorithms we can automatically verify programs that are out of reach for RAM based BMC.