@inproceedings{b572cdc7d93946c69d7f1be88acf2dbc,
title = "Comparison of distributed GPU computing frameworks for SAR raw data simulation",
abstract = "Synthetic Aperture Radar(SAR) has been widely used in airborne remote sensing and satellite ocean observation fields to reduce the affect of weather condition and sun illumination. As technology developed, swath and resolution requirements are increased in terrain, which arouse a huge increase in the number of simulated points and simulated pulses and lead to a huge increase in simulated time. With the development of graphics processing unit(GPU), it can parallel simulated points to reduce simulated time. As for increased simulated pulses, they can be paralleled on distributed computers. In the article, we focus on parallel on increased simulated pulses and put forward two frameworks based on message passing libraries (MPI) and cloud computing (Hadoop).",
keywords = "GPU, Hadoop, MPI, Parallel, SAR",
author = "Xiaojie Yao and Fan Zhang and Xiong Sun and Qiang Yin and Wei Li",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8128179",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5225--5228",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
}