Accelerating R data analytics in IoT edge systems by memory optimization

De Yin Liou, Chien Chih Chen, Tien-Fu Chen, Tay Jyi Lin

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

With the number and variety of IoT devices increasing, a large amount of data have to be moved to the cloud, resulting into unpredictable latency from limitation of network bandwidth. R language, the most popular analytic tool, has a serious bottleneck, memory garbage collection, which will become even worse problem at the edge systems with limited memory resources. Processing a large amount of dynamic objects and high percentage of LLC misses with significantmiss penalty are the two key issues in the R execution environment. In this paper, we propose a Partially Parallel Garbage Collection to improve the waiting time during garbage collection; and a centralized memory allocation mechanism to reduce miss penalty that is caused by the high percentage of LLC misses. Our optimizations can bring benefits to those machine learning algorithms that spend most of time in R for processing large data list at the edge systems.

原文English
主出版物標題11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781538605011
DOIs
出版狀態Published - 10 4月 2019
事件11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Moscow, 俄羅斯
持續時間: 20 9月 201722 9月 2017

出版系列

名字11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings

Conference

Conference11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017
國家/地區俄羅斯
城市Moscow
期間20/09/1722/09/17

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