Parallelism Reduction Based on Pattern Substitution in Dataflow Oriented Programming Languages

In this paper, we present a compiler extension for applications targeting high performance embedded systems. It analyzes the graph of a dataflow application in order to adapt its parallelism degree. Our approach consists in the detection and the substitution of built-in patterns in the dataflow. Modifications applied on the graph do not alter the semantic of the application. A parallelism reduction engine is also described to perform an exhaustive search of the best reduction. Our proposition has been implemented within an industry-grade compiler for the Sigma-C dataflow language. It shows that for dataflow applications, the parallelism reduction extension helps the user focus on the algorithm by hiding all parallelism tuning considerations. Experimentations demonstrate the accuracy and the performance of the reduction engine for both synthetic and real applications.