%0 Journal Article %1 li2012parallel %A Li, Chao %A Zhang, Liqiang %A Wu, Jiaze %A Zheng, Changwen %D 2012 %E Li, Chao %J Journal of Astronautics %K CUDA General Parallel Partition Star accelerating catalogue computing method retrieval %T Parallel Accelerating for Star Catalogue Retrieval Algorithm by means of GPUs %X A GPU-based parallel star retrieval method is proposed to improve the efficiency of searching stars from star catalogue in computer simulation, especially when the FOV (Field of View) is large. By the novel algorithm, the stars in catalogue are classified and stored in different zones using latitude and longitude zoning method firstly. Based on the easily accessible star catalogue, the star zones that FOV covers can be computed exactly by constructing a spherical triangle around the FOV. As a result, the searching scope is reduced effectively. Finally, we use CUDA computation architecture to run the process of star retrieving from those star zones parallel on GPU. Experimental results show that, in comparison with CPU-oriented implementation, the proposed algorithm achieves up to tens of times speedup, and the processing time is limited within a millisecond level in large FOV and wide star magnitude span. It meets the requirement of real-time simulation.

@article{li2012parallel, abstract = {A GPU-based parallel star retrieval method is proposed to improve the efficiency of searching stars from star catalogue in computer simulation, especially when the FOV (Field of View) is large. By the novel algorithm, the stars in catalogue are classified and stored in different zones using latitude and longitude zoning method firstly. Based on the easily accessible star catalogue, the star zones that FOV covers can be computed exactly by constructing a spherical triangle around the FOV. As a result, the searching scope is reduced effectively. Finally, we use CUDA computation architecture to run the process of star retrieving from those star zones parallel on GPU. Experimental results show that, in comparison with CPU-oriented implementation, the proposed algorithm achieves up to tens of times speedup, and the processing time is limited within a millisecond level in large FOV and wide star magnitude span. It meets the requirement of real-time simulation.}, added-at = {2011-11-16T03:46:38.000+0100}, author = {Li, Chao and Zhang, Liqiang and Wu, Jiaze and Zheng, Changwen}, biburl = {https://www.bibsonomy.org/bibtex/2a3e3f436b1c332e2cd7deb7aeae9f5ab/chaoli}, description = {A firstly CUDA version for large-scale star table retrieval algorithm on GPUS}, editor = {Li, Chao}, interhash = {be4e9db63391597c4363247650b2f1a5}, intrahash = {a3e3f436b1c332e2cd7deb7aeae9f5ab}, journal = { Journal of Astronautics}, keywords = {CUDA General Parallel Partition Star accelerating catalogue computing method retrieval}, timestamp = {2011-11-16T03:46:38.000+0100}, title = {Parallel Accelerating for Star Catalogue Retrieval Algorithm by means of GPUs}, year = 2012 }