Runtime multicore scheduling techniques for dispatching parameterized signal and vision dataflow applications on heterogeneous MPSoCs

Abstract : An important trend in embedded processing is the integration of increasingly more processing elements into Multiprocessor Systemson- Chip (MPSoC). This trend is due in part to limitations in processing power of individual elements that are caused by power consumption considerations. At the same time, signal processing applications are becoming increasingly dynamic in terms of their hardware resource requirements due to the growing sophistication of algorithms to reach higher levels of performance. In design and implementation of multicore signal processing systems, one of the main challenges is to dispatch computational tasks efficiently onto the available processing elements while taking into account dynamic changes in application functionality and resource requirements. An inefficient use can lead to longer processing times and higher energy consumption, making multicore task scheduling a very difficult problem to solve. Dataflow process network Models of Computation (MoCs) are widely used in design of signal processing systems. It decomposes application functionality into actors that communicate data exclusively through channels. The interconnection of actors and communication channels is modeled and manipulated as a directed graph, called a dataflow graph. There are different dataflow MoCs which offer different trade-off between predictability and expressiveness. These MoCs are widely used in design of signal processing systems due to their analyzability and their natural parallel expressivity. In this thesis, we propose a novel scheduling method to address multicore scheduling challenge. This scheduling method determines scheduling decisions strategically at runtime to optimize the overall execution time of applications onto heterogeneous multicore processing resources. Applications are described using the Parameterized and Interfaced Synchronous DataFlow (PiSDF) MoC. The PiSDF model allows describing parameterized application, making possible changes in application’s resource requirement at runtime. At each execution, the parameterized dataflow is then transformed into a locally static one used to efficiently schedule the application with an a priori knowledge of its behavior. The proposed scheduling method have been tested and benchmarked on multiple state-of-the-art applications from computer vision, signal processing and multimedia domains.
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Julien Heulot. Runtime multicore scheduling techniques for dispatching parameterized signal and vision dataflow applications on heterogeneous MPSoCs. Signal and Image processing. INSA de Rennes, 2015. English. ⟨NNT : 2015ISAR0023⟩. ⟨tel-01301642⟩

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