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Using parallel embedded systems these days is increasing. They are gettingmore complex due to integrating multiple functionalities in one application orrunning numerous ones concurrently. This concerns a wide range of applications,including streaming applications, commonly used in embedded systems. Theseapplications must implement adaptable and reliable algorithms to deliver therequired performance under varying circumstances (e.g., running applications onthe platform, input data, platform variety, etc.). Given the complexity ofstreaming applications, target systems, and adaptivity requirements, designingsuch systems with traditional programming models is daunting. This is whymodel-based strategies with an appropriate Model of Computation (MoC) have longbeen studied for embedded system design. This work provides algorithmicadaptivity on top of parallelism for dynamic dataflow to express larger sets ofvariants. We present a multi-Alternative Process Network (mAPN), a high-levelabstract representation in which several variants of the same applicationcoexist in the same graph expressing different implementations. We introducemAPN properties and its formalism to describe various local implementationalternatives. Furthermore, mAPNs are enriched with metadata to Provide thealternatives with quantitative annotations in terms of a specific metric. Tohelp the user analyze the rich space of variants, we propose a methodology toextract feasible variants under user and hardware constraints. At the core ofthe methodology is an algorithm for computing global metrics of an execution ofdifferent alternatives from a compact mAPN specification. We validate ourapproach by exploring several possible variants created for the AutomaticSubtitling Application (ASA) on two hardware platforms.

mAPN: Modeling, Analysis, and Exploration of Algorithmic and Parallelism Adaptivity