CAJ | 학술논문

星图配准是星图处理应用中的一个重要步骤，因此星图配准的速度直接影响了星图处理的整体速度。近几年来，图形处理器(GPU)在通用计算领域得到快速的发展。结合GPU在通用计算领域的优势与星图配准面临的处理速度的问题，研究了基于GPU加速处理星图配准的算法。在已有配准算法的基础上，根据算法特点提出了相应的GPU并行设计模型，利用CUDA编程语言进行仿真实验。实验结果表明：相较于传统基于CPU的配准算法，基于GPU的并行设计模型同样达到了配准要求，且配准速度的加速比达到29.043倍。
성도배준시성도처리응용중적일개중요보취，인차성도배준적속도직접영향료성도처리적정체속도。근궤년래，도형처리기(GPU)재통용계산영역득도쾌속적발전。결합GPU재통용계산영역적우세여성도배준면림적처리속도적문제，연구료기우GPU가속처리성도배준적산법。재이유배준산법적기출상，근거산법특점제출료상응적GPU병행설계모형，이용CUDA편정어언진행방진실험。실험결과표명：상교우전통기우CPU적배준산법，기우GPU적병행설계모형동양체도료배준요구，차배준속도적가속비체도29.043배。
The speed of star image registration affects the whole speed of the processing of the star image as star image registration is one of the most important steps of star image processing. In recent years, the general purpose computing of graphic process unit(GPU)has a rapid development. In this paper, the computing power of GPU for the general purpose computing and the problem of the speeding up of processing of star image registration were combined to study the accelerated processing algorithm based on GPU. A parallel model of GPU for the registration algorithm was proposed and CUDA programming language was uesd to realize it. Experiment result shows that the parallel model also fulfills the purpose of the image registration and has a 29.043X speedup compared with the serial CPU program.