Parameterized Verification of Coverability in Infinite State Broadcast Networks

Parameterized verification of coverability in broadcast networks with finite state processes has been studied for different types of models and topologies. In this paper, we attempt to develop a theory of broadcast networks in which the processes can be well-structured transition systems. The resulting formalism is called well-structured broadcast networks. For various types of communication topologies, we prove the decidability of coverability in the static case, i.e, when the network topology is not allowed to change. We do this by showing that for these types of static communication topologies, the broadcast network itself is a well-structured transition system, hence proving the decidability of coverability in the broadcast network. We also give an algorithm to decide coverability of well-structured broadcast networks when reconfiguration of links between nodes is allowed. Finally, with minor modifications of this algorithm we prove decidability of coverability when the underlying process is a pushdown automaton.Comment: Full journal version of arXiv:1809.0309

A unified view of parameterized verification of abstract models of broadcast communication

We give a unified view of different parameterized models of concurrent and distributed systems with broadcast communication based on transition systems. Based on the resulting formal models, we discuss related verification methods and tools based on abstractions and symbolic state exploration

Parameterized verification

The goal of parameterized verification is to prove the correctness of a system specification regardless of the number of its components. The problem is of interest in several different areas: verification of hardware design, multithreaded programs, distributed systems, and communication protocols. The problem is undecidable in general. Solutions for restricted classes of systems and properties have been studied in areas like theorem proving, model checking, automata and logic, process algebra, and constraint solving. In this introduction to the special issue, dedicated to a selection of works from the Parameterized Verification workshop PV \u201914 and PV \u201915, we survey some of the works developed in this research area

Parameterized Broadcast Networks with Registers: from NP to the Frontiers of Decidability

We consider the parameterized verification of arbitrarily large networks of agents which communicate by broadcasting and receiving messages. In our model, the broadcast topology is reconfigurable so that a sent message can be received by any set of agents. In addition, agents have local registers which are initially distinct and may therefore be thought of as identifiers. When an agent broadcasts a message, it appends to the message the value stored in one of its registers. Upon reception, an agent can store the received value or test this value for equality with one of its own registers. We consider the coverability problem, where one asks whether a given state of the system may be reached by at least one agent. We establish that this problem is decidable; however, it is as hard as coverability in lossy channel systems, which is non-primitive recursive. This model lies at the frontier of decidability as other classical problems on this model are undecidable; this is in particular true for the target problem where all processes must synchronize on a given state. By contrast, we show that the coverability problem is NP-complete when each agent has only one register

A Dataflow Framework For Developing Flexible Embedded Accelerators A Computer Vision Case Study.

The focus of this dissertation is the design and the implementation of a computing platform which can accelerate data processing in the embedded computation domain. We focus on a heterogeneous computing platform, whose hardware implementation can approach the power and area efficiency of specialized designs, while remaining flexible across the application domain. The multi-core architectures require parallel programming, which is widely-regarded as more challenging than sequential programming. Although shared memory parallel programs may be fairly easy to write (using OpenMP, for example), they are quite hard to optimize; providing embedded application developers with optimizing tools and programming frameworks is a challenge. The heterogeneous specialized elements make the problem even more difficult. Dataflow is a parallel computation model that relies exclusively on message passing, and that has some advantages over parallel programming tools in wide use today: simplicity, graphical representation, and determinism. Dataflow model is also a good match to streaming applications, such as audio, video and image processing, which operate on large sequences of data and are characterized by abundant parallelism and regular memory access patterns. Dataflow model of computation has gained acceptance in simulation and signal-processing communities. This thesis evaluates the applicability of the dataflow model for implementing domain-specific embedded accelerators for streaming applications