A transformation strategy for process partitioning in hierarchical concurrent process networks

Concurrent process networks are a widely used parallel programming model for designing multiprocessor embedded systems, where system functionality is decomposed into processes that communicate via signals. These processes can be mapped onto different processing elements and executed concurrently. While the initial process network is designed to effectively capture high-level parallelism, it may not fully exploit the available parallelism. To enhance concurrency and balance workload distribution, process partitioning transformations are applied, restructuring process networks to expose finer-grained parallelism. The effectiveness of these transformations, however, depends on how well they align with the underlying hardware’s parallel capabilities.

A variety of partitioning transformations have been introduced for process networks constructed using higher-order functions in the form of process constructors and data-parallel skeletons. For such networks, algebraic laws of functions provide a principled foundation for defining transformation rules, enabling a systematic and non-ad-hoc approach to process network modification. However, selecting the most suitable transformation to optimize key performance metrics remains an open challenge. To address this, we propose a transformation strategy that systematically identifies the most effective partitioning transformations. Our approach introduces evaluation metrics and analytical models to assess the impact of parametric transformations across different configurations. We validate the proposed strategy through the transformation of two image processing algorithms, demonstrating that our analytical models correctly predict the most suitable transformations for enhancing parallelism and performance.