RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization
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 perf...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/8/1799 |
| _version_ | 1798040975051849728 |
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| author | Yuling Fang Qingkui Chen Neal N. Xiong Deyu Zhao Jingjuan Wang |
| author_facet | Yuling Fang Qingkui Chen Neal N. Xiong Deyu Zhao Jingjuan Wang |
| author_sort | Yuling Fang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:15:05Z |
| format | Article |
| id | doaj.art-fe0f4bd4b73846bc8bf2bfefc602d7ff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:15:05Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fe0f4bd4b73846bc8bf2bfefc602d7ff2022-12-22T04:00:27ZengMDPI AGSensors1424-82202017-08-01178179910.3390/s17081799s17081799RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy OptimizationYuling Fang0Qingkui Chen1Neal N. Xiong2Deyu Zhao3Jingjuan Wang4.University of Shanghai for Science and Technology, Shanghai 200093, China.University of Shanghai for Science and Technology, Shanghai 200093, China.University of Shanghai for Science and Technology, Shanghai 200093, China.University of Shanghai for Science and Technology, Shanghai 200093, China.University of Shanghai for Science and Technology, Shanghai 200093, ChinaThis 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.https://www.mdpi.com/1424-8220/17/8/1799Internet of Thingsdata mining algorithmsGPU clusterperformanceenergy consumptionreliability |
| spellingShingle | Yuling Fang Qingkui Chen Neal N. Xiong Deyu Zhao Jingjuan Wang RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization Sensors Internet of Things data mining algorithms GPU cluster performance energy consumption reliability |
| title | RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization |
| title_full | RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization |
| title_fullStr | RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization |
| title_full_unstemmed | RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization |
| title_short | RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization |
| title_sort | rgca a reliable gpu cluster architecture for large scale internet of things computing based on effective performance energy optimization |
| topic | Internet of Things data mining algorithms GPU cluster performance energy consumption reliability |
| url | https://www.mdpi.com/1424-8220/17/8/1799 |
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