Kahn Process Networks and a Reactive Extension

Kahn and MacQueen have introduced a generic class of determinate asynchronous data-flow applications, called Kahn Process Networks (KPNs) with an elegant mathematical model and semantics in terms of Scott-continuous functions on data streams together with an implementation model of independent asynchronous sequential programs communicating through FIFO buffers with blocking read and non-blocking write operations. The two are related by the Kahn Principle which states that a realization according to the implementation model behaves as predicted by the mathematical function. Additional steps are required to arrive at an actual implementation of a KPN to take care of scheduling of independent processes on a single processor and to manage communication buffers. Because of the expressiveness of the KPN model, buffer sizes and schedules cannot be determined at design time in general and require dynamic run-time system support. Constraints are discussed that need to be placed on such system support so as to maintain the Kahn Principle.We then discuss a possible extension of the KPN model to include the possibility for sporadic, reactive behavior which is not possible in the standard model. The extended model is called Reactive Process Networks. We introduce its semantics, look at analyzability and at more constrained data-flow models combined with reactive behavior

Rapport LIFL # 2003-06 Design of a Real-Time Scheduler for Kahn Process Networks on Multiprocessor Systems

High-throughput real-time systems require non-standard and costly hardware and software solutions. Modern workstation can represent a credible alternative to develop real-time intensive signal processing applications. Furthermore, the programming model of Kahn Process Networks (KPN) corresponds completely to this kind of applications and fits perfectly on multiprocessor systems.We present a new activation strategy of processes in a KPN which considerably improves the existing techniques. This new activation order takes into account both deadlock detection and resolution with time constraints. With this algorithm, we do not need to wait until the entire execution has deadlocked to remove bottleneck. Moreover, we have an optimized allocation memory mechanism and we can bound the number of processes context switches