Generating Efficient Parallel Code from the RVC-CAL Dataflow Language

Abstract

The RVC-CAL language is used for implementing dataflow process networks (DPNs), i.e., distributed systems of actors. The behavior of an actor is defined by a set of actions which can consume input tokens and produce output tokens. RVC-CAL DPNs can offer parallelism both at the level of actors and at the level of actions. To efficiently execute these models on a target hardware, it is important to generate parallel code based on the entire parallelism provided by these two levels. In this paper, we discuss criteria for the generation of parallel software from RVC-CAL models based on the potential parallelism of modeled behaviors. The approach considers both the coarse-grained (task-parallel) execution of actors using multithreading and the fine-grained (data-parallel) execution of their actions using the open computing language (OpenCL) or even a higher-level layer of OpenCL, namely SYCL. The methodology is validated by benchmarks on OpenCL abstracted hardware platforms. Based on the experimental results, the methodology is evaluated for efficiency (performance) in comparison with a pure multithreaded C++ approach and a well-known reference framework.