Version 1
: Received: 1 July 2017 / Approved: 6 July 2017 / Online: 6 July 2017 (12:40:22 CEST)
How to cite:
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. Preprints2017, 2017070011. https://doi.org/10.20944/preprints201707.0011.v1
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. Preprints 2017, 2017070011. https://doi.org/10.20944/preprints201707.0011.v1
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. Preprints2017, 2017070011. https://doi.org/10.20944/preprints201707.0011.v1
APA Style
Fang, Y., Chen, Q., Xiong, N.N., Zhao, D., & Wang, J. (2017). RGCA: a Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization. Preprints. https://doi.org/10.20944/preprints201707.0011.v1
Chicago/Turabian Style
Fang, Y., Deyu Zhao and Jingjuan Wang. 2017 "RGCA: a Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization" Preprints. https://doi.org/10.20944/preprints201707.0011.v1
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 computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA 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.
Keywords
Internet of Things; data mining algorithms; GPU cluster; performance; energy consumption; reliability
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
