Using stream rewriting for mapping and scheduling data flow graphs onto many-core architectures

Dataflow graphs, consisting of concurrent actors connected by communication channels, are widely used to model multimedia applications. As dataflow graphs explicitly expose the parallelism contained in the application, they yield well to synthesis for many-core architectures. However, in case of varying and unpredictable workloads, a static mapping of actors to computing resources is often infeasible, but a dynamic approach becomes challenging due to the numerous amount of actors. Our concept of stream-rewriting represents a novel execution semantics for dataflow graphs on many-core architectures, which allows for a completely dynamic binding of actors instances to processing units. In addition, we present a distributed scheduling mechanism, global resource sharing and lightweight lock-free synchronization based on pattern matching. Also, an optimized architecture for stream-rewriting is prototyped and evaluated.