Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

Autor: Baldomero Imbernón, José M. Cecilia, Antonio Llanes, Juan Morales-García, Juan-Carlos Cano
Jazyk: angličtina
Rok vydání: 2020
Předmět:
low-power
Clustering algorithms
Computer science
Cloud computing
02 engineering and technology
lcsh:Chemical technology
Biochemistry
Article
Analytical Chemistry
edge computing
Low-power
0202 electrical engineering
electronic engineering
information engineering
Intelligent systems
lcsh:TP1-1185
Electrical and Electronic Engineering
Cluster analysis
clustering algorithms
Instrumentation
Edge computing
intelligent systems
business.industry
cloud computing
Intelligent decision support system
020206 networking & telecommunications
IoT applications
GPU computing
Atomic and Molecular Physics
and Optics
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
Computer engineering
020201 artificial intelligence & image processing
Enhanced Data Rates for GSM Evolution
General-purpose computing on graphics processing units
business
Zdroj: Sensors
Volume 20
Issue 21
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 6335, p 6335 (2020)
RIUCAM: Repositorio Institucional de la Universidad Católica San Antonio de Murcia
Universidad Católica San Antonio de Murcia (UCAM)
RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
ISSN: 1424-8220
DOI: 10.3390/s20216335
Popis: 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 &ldquo
dark data&rdquo
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&rsquo
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.
Databáze: OpenAIRE
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