Practical Performance Analysis of CPU, GPU and Xeon-Phi in Atmospheric Correction Processing for the Geostationary Ocean Color Imager (GOCI)

Title
Practical Performance Analysis of CPU, GPU and Xeon-Phi in Atmospheric Correction Processing for the Geostationary Ocean Color Imager (GOCI)
Author(s)
허재무; 한희정; 양현; 박영제
KIOST Author(s)
Heo, Jae Moo(허재무)Yang, Hyun(양현)Park, Young Je(박영제)
Publication Year
2017-05-18
Abstract
In 2019, Geostationary Ocean Color Imager-II (GOCI-II) will be launched as a successor to GOCI, the worlds first geostationary ocean color sensor. The existing atmospheric correction algorithm for GOCI was designed to be processed sequentially. Also, if GOCI-II data is processed sequentially it takes more than 1 hour although GOCI-II data must be processed within 11 minutes. To meet these requirements, the parallelism was applied to the existing GOCI data processing prior to developing GOCI-II. In this paper, we have improved the processing speed of atmospheric correction algorithm using OpenMP(Open Multi-Processing) and OpenCL(Open Computing Language). The newest CPUs, Xeon-Phi and GPUs were used in experiments, and the performances of CPU- and GPU-parallelized versions were derived by comparisons with the existing sequential version. As a result, GPU version showed the performance improvements 40 times better than a sequential version and nearly twice as high as a CPU version, and its memory capacity were maintained within 3GB.ially. Also, if GOCI-II data is processed sequentially it takes more than 1 hour although GOCI-II data must be processed within 11 minutes. To meet these requirements, the parallelism was applied to the existing GOCI data processing prior to developing GOCI-II. In this paper, we have improved the processing speed of atmospheric correction algorithm using OpenMP(Open Multi-Processing) and OpenCL(Open Computing Language). The newest CPUs, Xeon-Phi and GPUs were used in experiments, and the performances of CPU- and GPU-parallelized versions were derived by comparisons with the existing sequential version. As a result, GPU version showed the performance improvements 40 times better than a sequential version and nearly twice as high as a CPU version, and its memory capacity were maintained within 3GB.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/23967
Bibliographic Citation
International Symposium on Remote Sensing 2017, pp.685 - 688, 2017
Publisher
Remote Sensing Society of Japan
Type
Conference
Language
English
Publisher
Remote Sensing Society of Japan
Related Researcher
Research Interests

Ocean Color Remote Sensing,Satellite Applications,Ocean color Algorithm,해양원격탐사,위성활용,해색 알고리즘

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