CAJ | 학술논문

合成孔径雷达(SAR)成像处理的运算量较大，在基于中央处理器(Central Processing Unit, CPU)的工作站或服务器上一般需要耗费较长的时间，无法满足实时性要求。借助于通用并行计算架构(CUDA)编程架构，该文提出一种基于图形处理器(GPU)的SAR成像处理算法实现方案。该方案解决了GPU显存不足以容纳一景SAR数据时数据处理环节与内存/显存间数据传输环节的并行化问题，并能够支持多GPU设备的并行处理，充分利用了GPU设备的计算资源。在NVIDIA K20C和INTEL E5645上的测试表明，与传统基于GPU的SAR成像处理算法相比，该方案能够达到数十倍的速度提升，显著降低了处理设备的功耗，提高了处理设备的便携性，能够达到每秒约36兆采样点的实时处理速度。
합성공경뢰체(SAR)성상처리적운산량교대，재기우중앙처리기(Central Processing Unit, CPU)적공작참혹복무기상일반수요모비교장적시간，무법만족실시성요구。차조우통용병행계산가구(CUDA)편정가구，해문제출일충기우도형처리기(GPU)적SAR성상처리산법실현방안。해방안해결료GPU현존불족이용납일경SAR수거시수거처리배절여내존/현존간수거전수배절적병행화문제，병능구지지다GPU설비적병행처리，충분이용료GPU설비적계산자원。재NVIDIA K20C화INTEL E5645상적측시표명，여전통기우GPU적SAR성상처리산법상비，해방안능구체도수십배적속도제승，현저강저료처리설비적공모，제고료처리설비적편휴성，능구체도매초약36조채양점적실시처리속도。
Synthetic Aperture Radar (SAR) image processing requires a considerable amount of computational resources. Traditionally, this task runs on a workstation or a server based on Central Processing Units (CPUs) and is rather time-consuming, making real-time processing of SAR data impossible. Based on Compute Unified Device Architecture (CUDA) technology, a new plan for a SAR imaging algorithm operated on an NVIDIA Graphic Processing Unit (GPU) is proposed. The new proposal makes it possible for the data processing procedure and the CPU/GPU data exchange to execute concurrently, especially when the size of SAR data exceeds the total GPU global memory size. A multi-GPU is suitably supported by the new proposal, and all computational resources are fully exploited. It has been shown by an experiment on an NVIDIA K20C and INTEL E5645 that the proposed solution accelerates SAR data processing by tens of times. Consequently, a GPU based SAR processing system that embeds the proposed solution is much more efficient and portable, thereby making it qualified to be a real-time SAR data processing system. Experiments showed that SAR data can be processed in real-time at a rate of 36 megapixels per second by a K20C when the new solution is implemented.