Cecilia José M, Cano Juan-Carlos, Morales-García Juan, Llanes Antonio, Imbernón Baldomero
Computer Engineering Department (DISCA), Universitat Politécnica de Valencia (UPV), 46022 Valencia, Spain.
Computer Science Department, Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain.
Sensors (Basel). 2020 Nov 6;20(21):6335. doi: 10.3390/s20216335.
Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11× for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.
物联网（IoT）正在成为一场新的社会经济革命，数据和即时性是其主要要素。物联网每天都会生成大量数据集，但目前这些数据被视为“暗数据”，即虽已生成但从未被分析过的数据。要为造福社会的下一代物联网应用创建智能应用程序，对这些数据进行有效分析是必不可少的。人工智能（AI）技术非常适合在这海量数据中识别隐藏的模式和相关性。特别是，聚类算法对于执行探索性数据分析以识别一组相似对象（也称为簇）至关重要。聚类算法的计算工作量很大，需要在高性能计算集群上执行，尤其是处理大型数据集时。在高性能计算基础设施上执行此操作是一个耗能过程，还存在其他问题，如高延迟通信或隐私问题。边缘计算是一种在网络边缘实现轻量级计算的范式，最近被提出来解决这些问题。在本文中，我们对新兴的边缘计算架构进行了深入分析，这些架构包括低功耗图形处理单元（GPU）以加速这些工作负载。我们的分析包括最新的英伟达AGX Xavier的性能和功耗数据，以将这些低成本平台与基于云的高性能对应版本的能源性能比进行比较。三种不同的聚类算法（即k均值、模糊极小值（FM）和模糊C均值（FCM））被设计为可在边缘和云平台上最佳执行，与边缘平台中的顺序对应版本相比，GPU代码的加速因子高达11倍，并且边缘计算和高性能计算平台之间的节能高达150%。
