Deterministic memory sharing in Kahn process networks: Ultrasound imaging as a case study

Kahn process networks are a popular programming model for programming multi-core systems. They ensure determinacy of applications by restricting processes to separate memory regions, only allowing communication over FIFO channels. However, many modern multi-core platforms concentrate on shared memory as a means of communication and data exchange. In this work, we present a concept for deterministic memory sharing in Kahn process networks. It allows to take advantage of shared memory data exchange mechanisms on such platforms while still preserving determinacy. We show how any Kahn process network can be transformed to use deterministic memory sharing by giving a set of transformations that can be applied selectively, only looking at one process at a time. We demonstrate how these techniques can be applied to an ultrasound image reconstruction algorithm. For an implementation on a test system, our technique yields significantly better performance combined with a drastically smaller memory footprint.