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Sensors 2017, 17(8), 1799; https://doi.org/10.3390/s17081799

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

1
.University of Shanghai for Science and Technology, Shanghai 200093, China
2
.Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA
*
Author to whom correspondence should be addressed.
Received: 29 June 2017 / Revised: 24 July 2017 / Accepted: 2 August 2017 / Published: 4 August 2017
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Abstract

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. View Full-Text
Keywords: Internet of Things; data mining algorithms; GPU cluster; performance; energy consumption; reliability Internet of Things; data mining algorithms; GPU cluster; performance; energy consumption; reliability
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Fang, Y.; Chen, Q.; Xiong, N.N.; Zhao, D.; Wang, J. RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization. Sensors 2017, 17, 1799.

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