基于嵌入式CPU+GPU异构平台的遥感图像滤波加速OA北大核心CSTPCD
Acceleration of Remote Sensing Image Filtering Based on Embedded CPU+GPU Heterogeneous Platform
针对遥感图像在轨实时处理提出一种基于嵌入式CPU + GPU异构平台的遥感图像滤波加速设计方法.以加速拉普拉斯滤波为例,利用GPU的并行计算特点,通过数据划分及数据映射的方法对算法进行并行设计;利用GPU的向量单元和缓存等硬件资源,通过采取向量化和向量重组以及工作组调优方法进一步提高了算法的运行速度.在嵌入式开发板上验证了加速设计的可行性和高效性.实验结果表明,相比于单CPU的串行实现,在增加GPU并行处理后的拉普拉斯滤波获得了 4.08～16.92倍的加速比.进一步利用GPU硬件资源优化性能后,加速比可达15.38～56.41倍.
A method is proposed for accelerating remote sensing image filtering in real-time using an embedded CPU + GPU heterogeneous platform for satellite-based image processing.the algorithm was initially parallelized through data division and mapping,leveraging the parallel computing capabilities of the GPU.Subsequently,hardware resources like the vector unit and cache of the GPU were employed to enhance algorithm speed through vectorization,vector permutation,and workgroup tuning.The feasibili-ty and efficiency of this accelerated design were validated on an embedded development board.The ex-periments demonstrate a speedup ranging from 4.08 to 16.92 times when incorporating GPU parallel pro-cessing,compared to the serial implementation on a single CPU.Further optimization using GPU hard-ware resources can push the speedup to 15.38 to 56.41 times.
谭鹏源;薛长斌;周莉
中国科学院国家空间科学中心 北京 100190||中国科学院大学 北京 100049中国科学院国家空间科学中心 北京 100190
嵌入式GPU遥感图像滤波OpenCL向量化向量重组
Embedded GPURemote sensing image filteringOpenCLVectorizationVector permutation
《空间科学学报》 2024 (001)
95-102 / 8
中国科学院国防科技重点实验室基金项目资助(CXJJ-20S017)
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