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A Processor Workload Distribution Algorithm for Massively Parallel Applications

机译：大规模并行应用的处理器工作负载分配算法

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摘要

Directed Acyclic Graph (DAG) is a standard model used to describe tasks that execute according to precedence constraints and that allows intra-task parallelism. This model is well suited to camera-based applications where multiple treatments must be executed in parallel according to the camera input, such applications found for example in self-driving cars or image recognition via convolutional neural network (CNN). Such applications are used on embedded systems and therefore require low energy cost and a limited hardware space. The main contribution of this paper is to present a new partitioning algorithm based on a DAG stretching technique. This stretching algorithm frees processor cores and thus implies energy savings and leads to new hardware design using a reduced number of processors. We present an experimental evaluation of this algorithm to show its efficiency.
机译：有向无环图（DAG）是一种标准模型，用于描述根据优先级约束执行的任务，并允许任务内并行处理。该模型非常适合必须基于摄像头输入并行执行多种处理的基于摄像头的应用程序，例如在自动驾驶汽车或通过卷积神经网络（CNN）进行图像识别的应用程序。这样的应用程序用于嵌入式系统，因此需要较低的能源成本和有限的硬件空间。本文的主要贡献是提出了一种基于DAG拉伸技术的新分区算法。这种扩展算法可释放处理器内核，从而节省能源并导致使用更少数量的处理器进行新的硬件设计。我们目前对该算法进行实验评估，以显示其效率。

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