We propose a new and practical framework for integrating the behavioral reasoning about distributed systems with model-checking methods.Our proof methods are based on trace abstractions, which relate the behaviors of the program and the specification. We show that for finitestate systems such symbolic abstractions can be specified conveniently in Monadic Second-Order Logic (M2L). Model-checking is then made possible by the reduction of non-determinism implied by the trace abstraction.Our method has been applied to a recent verification problem by Broy and Lamport. We have transcribed their behavioral description of a distributed program into temporal logic and verified it against another distributed system without constructing the global program state space. The reasoning is expressed entirely within M2L and is carried out by a decision procedure. Thus M2L is a practical vehicle for handling complex temporal logic specifications, where formulas decided by a push of a button are as long as 10-15 pages.
The Cutting edge Reconfigurable ICs for Stream Processing (CRISP) project aims to create a highly scalable and dependable reconfigurable system concept for a wide range of tomorrow's streaming DSP applications. Within CRISP, a network-on-chip based many-core stream processor with dependability infrastructure and run-time resource management is devised, implemented, and manufactured to demonstrate a coarse-grained core-level reconfigurable system with scalable computing power, flexibility, and dependability. This chapter introduces CRISP, presents the concepts, and outlines the preliminary results of a running project.
Parallel architectures are nowadays not only confined to the domain of high performance computing, they are also increasingly used in embedded time-critical systems. The ARGO H2020 project 1 provides a programming paradigm and associated tool flow to exploit the full potential of architectures in terms of development productivity, time-to-market, exploitation of the platform computing power and guaranteed real-time performance. In this paper we give an overview of the objectives of ARGO and explore the challenges introduced by our approach.
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