TY - GEN
T1 - Multi-GPUs Gaussian filtering for real-time big data processing
AU - Zhang, Chaolong
AU - Xu, Yuanping
AU - He, Jia
AU - Lu, Jun
AU - Lu, Li
AU - Xu, Zhijie
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - Gaussian filtering has been extensively used in the field of surface metrology. However, the computing performance becomes a core bottleneck for Gaussian filtering algorithm based applications when facing large-scale and/or real-time data processing. Although researchers tried to accelerate Gaussian filtering algorithm by using GPU (Graphics Processing Unit), single GPU still fail to meet the large-scale and real-time requirements of surface texture micro- and nano-measurements. Therefore, to solve this bottleneck problem, this paper proposes a single node multi-GPUs based computing framework to accelerate the 2D Gaussian filtering algorithm. This paper presents that the devised framework seamlessly integrated the multi-level spatial domain decomposition method and the CUDA stream mechanism to overlap the two main time consuming steps, i.e., the data transfer and GPU kernel execution, such that it can increase concurrency and reduce the overall running time. This paper also tests and evaluates the proposed computing framework with other three conventional solutions by using large-scale measured data extracted from real mechanical surfaces, and the final results show that the proposed framework achieved higher efficiency. It also proved that this framework satisfies the real-time and big data requirements in micro- and nano-surface texture measurement.
AB - Gaussian filtering has been extensively used in the field of surface metrology. However, the computing performance becomes a core bottleneck for Gaussian filtering algorithm based applications when facing large-scale and/or real-time data processing. Although researchers tried to accelerate Gaussian filtering algorithm by using GPU (Graphics Processing Unit), single GPU still fail to meet the large-scale and real-time requirements of surface texture micro- and nano-measurements. Therefore, to solve this bottleneck problem, this paper proposes a single node multi-GPUs based computing framework to accelerate the 2D Gaussian filtering algorithm. This paper presents that the devised framework seamlessly integrated the multi-level spatial domain decomposition method and the CUDA stream mechanism to overlap the two main time consuming steps, i.e., the data transfer and GPU kernel execution, such that it can increase concurrency and reduce the overall running time. This paper also tests and evaluates the proposed computing framework with other three conventional solutions by using large-scale measured data extracted from real mechanical surfaces, and the final results show that the proposed framework achieved higher efficiency. It also proved that this framework satisfies the real-time and big data requirements in micro- and nano-surface texture measurement.
KW - CUDA
KW - Data processing
KW - Gaussian filtering algorithm
KW - Multi-GPUs
KW - Surface metrology
UR - http://www.scopus.com/inward/record.url?scp=85020003541&partnerID=8YFLogxK
U2 - 10.1109/SKIMA.2016.7916225
DO - 10.1109/SKIMA.2016.7916225
M3 - Conference Proceeding
AN - SCOPUS:85020003541
T3 - SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications
SP - 231
EP - 236
BT - SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2016
Y2 - 15 December 2016 through 17 December 2016
ER -