A Pattern-Based API for Mapping Applications to a Hierarchy of Multi-Core Devices

Recent years have witnessed an evolution of Internet of Things (IoT) devices. This has lead to the emergence of (related) paradigms of Edge/Fog computing, where the goal is to exploit the power of interconnected heterogeneous devices together with distributed/cloud computing. In Edge/Fog computing, one of the challenges is automatically distributing the work between different devices to reduce application latency. At the same time, with increasing transistor density and the end of Denard scaling, even small edge devices have parallelism. Thus, we need a programming model that can help distribute the work between different devices and yet parallelize operations on each device. Motivated by the popularity of MapReduce(-like) frame-works, we develop a pattern-based high-level programming API targeting computer vision applications for the Edge/Fog paradigm with parallelism within devices. Based on this API, parallelization, workload distribution, and optimizations that account for resource limitations of IoT devices, are implemented. Our evaluation with three image processing applications shows that while using a single device, we achieve 17-45% speedup over OpenCV, one of the most popular frameworks for image processing. In addition, we further gain benefits from distributing the work between multiple devices.