implementing high-performance intensity model with blur effect on gpus for large-scale star image simulation

ISCAS OpenIR

	implementing high-performance intensity model with blur effect on gpus for large-scale star image simulation
	Li Chao; Zhang Yunquan; Zheng Changwen; Hu Xiaohui
	2012
会议名称	2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
会议录名称	Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
页码	1879-1888
会议日期	May 21, 2012 - May 25, 2012
会议地点	Shanghai, China
收录类别	EI ; ISTP
ISSN	2164-7062
ISBN	9780769546766
部门归属	(1) National Key Laboratory of Integrated Information System Technology Institute of Software Chinese Academy of Sciences Beijing China; (2) Lab. of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing China; (3) Graduate University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China
摘要	Intensity model with blur effects are widely employed to accurately simulate the imaging process of a star simulator used for attitude determination and guiding feedback. The model is computationally intensive and the time requirements are proportional to the number of stars in the simulation, imposing great demands of computing power for realistic uses. This paper presents two star simulators using Graphic Processing Units (GPUs). We analyze the parallelism inherent in the intensity model and leverage a massive number of computing cores on GPU to efficiently exploit the fine-grain data parallelism. We first give a parallel simulator and discuss the performance trade-offs related to small amount of shared memory and the atomic operations on GPU. We then give the second simulator by adapting the first based on the characteristics of spatial locality with on-chip memory redesign. We analyze the balance between the kernel time and the non-kernel overhead in the two simulators and observe the inflection points in terms of two crucial model parameters. A selection table is given to choose between the two simulators. Benchmarks corresponding to the data parallelism are developed to fully evaluate the performance. The parallel simulator reports one to two orders of magnitude speedups with a maximum of 270 X compared to the widely-used sequential simulators and the average speedup is around 97 times. The adaptive simulator achieved up to 1.8 X compared with the parallel one over the inflection point. The developed code is currently used for simulating complex star images in a realistic large-scale star simulator. © 2012 IEEE.; Intensity model with blur effects are widely employed to accurately simulate the imaging process of a star simulator used for attitude determination and guiding feedback. The model is computationally intensive and the time requirements are proportional to the number of stars in the simulation, imposing great demands of computing power for realistic uses. This paper presents two star simulators using Graphic Processing Units (GPUs). We analyze the parallelism inherent in the intensity model and leverage a massive number of computing cores on GPU to efficiently exploit the fine-grain data parallelism. We first give a parallel simulator and discuss the performance trade-offs related to small amount of shared memory and the atomic operations on GPU. We then give the second simulator by adapting the first based on the characteristics of spatial locality with on-chip memory redesign. We analyze the balance between the kernel time and the non-kernel overhead in the two simulators and observe the inflection points in terms of two crucial model parameters. A selection table is given to choose between the two simulators. Benchmarks corresponding to the data parallelism are developed to fully evaluate the performance. The parallel simulator reports one to two orders of magnitude speedups with a maximum of 270 X compared to the widely-used sequential simulators and the average speedup is around 97 times. The adaptive simulator achieved up to 1.8 X compared with the parallel one over the inflection point. The developed code is currently used for simulating complex star images in a realistic large-scale star simulator. © 2012 IEEE.
关键词	Benchmarking Distributed Parameter Networks Program Processors Stars
主办者	IEEE Computer Society Technical Committee on Parallel Processing
学科领域	Computer Science
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15776
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Li Chao,Zhang Yunquan,Zheng Changwen,et al. implementing high-performance intensity model with blur effect on gpus for large-scale star image simulation[C],2012:1879-1888.