Finding fast action selectors for dataflow actors

The parallel structure of dataflow programs and their support for processing streams of data make dataflow programming an interesting tool for doing stream processing on parallel processing architectures. The computational kernels, the actors, of a dataflow program communicate with other actors via FIFO channels. The actors in the dataflow model used in this paper may perform different actions depending on the state of the actor and on the data that has been sent to the actor that is present on its ingoing channels. For this kind of dataflow programs, decisions on what to do in an actor in a given state has to be made at runtime in a process called action selection. Each action is associated with a set of conditions on the state and the input channels. All conditions must be fulfilled for the action to be selected, and the task of the action selector is to test different conditions to select an action. This paper builds upon previous work on the actor machine - a machine model for dataflow actors where the action selection is central. We present two heuristics that based on profiling data creates fast action selectors using the actor machine. The heuristics are implemented in the Tÿcho Dataflow Compiler and are evaluated using a video decoder written in Cal.