Paper Info
Title
Massive parallelism for non-linear and non-stationary data analysis with GPGPU
Abstract
In recent years, a large volume of natural signal data has become available for scientists because of the maturity of sensor techniques. However, the sensor data can form huge data streams that are non-linear and non-stationary. Existing methods cannot process such a large volume of data efficiently with a single CPU because of the high complexity of the algorithms. In this paper, we present Massive Parallelism GPU-Optimized Adaptive Data Analysis (MG-ADA), a new parallel signal data analysis algorithm that utilizes General-Purpose Graphics Programming Unit (GPGPU) to improve data scalability and reduce computation time for large non-linear and non-stationary datasets. We propose effective strategies to significantly improve the efficiency and scalability of MG-ADA. Our experimental results show that MG-ADA provides high scalability and significantly reduces the processing time in large datasets compared to other baseline algorithms.
Year
DOI
Venue
2016
10.1109/BigData.2016.7840619
2016 IEEE International Conference on Big Data (Big Data)
Keywords
Field
DocType
massive parallelism,nonlinear data analysis,nonstationary data analysis,GPGPU,natural signal data,sensor techniques,data streams,massive parallelism GPU-optimized adaptive data analysis,MG-ADA,parallel signal data analysis,general-purpose graphics programming unit,data scalability,nonstationary datasets,nonlinear datasets
Graphics,Data stream mining,Algorithm design,Computer science,Instruction set,Massively parallel,Parallel computing,Stationary process,General-purpose computing on graphics processing units,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-4673-9006-4
0
0.34
References 
Authors
4
3
Name
Order
Citations
PageRank
Chun-chich Chen100.34
Chih-Ya Shen210317.13
Ming Chen365071277.71