On the Analytic Evaluation of Schedules via Max-Plus Algebra for DSE of Multi-Core Architectures

Abstract

Dataflow modeling is well suited for a wide variety of applications for multi-core architectures, e.g. signal processing and control domain. Additionally, Design Space Exploration (DSE) can be used to explore the distribution of tasks to resources and their scheduling to obtain optimized trade-off solutions between throughput and resource costs. However, the performance evaluation of an implementation candidate in particular via compilation and throughput measurement on the target hardware is prohibitively time-consuming. Thus, we propose to use a max-plus algebra-based analytic throughput calculation method in the initial DSE phase where a fast evaluation with low accuracy is sufficient to guide the search through the design space. However, this analysis neglects some real-world concerns like cache effects and scheduling overhead. Thus, a hybrid DSE is proposed where throughput measurements are taken later in the DSE to get more accurate throughput results for real-world platforms. Results show that our approach is able to find much higher throughput multi-core solutions within a given exploration time compared to a state-of-the-art DSE approach.