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异构计算平台激光雷达算法优化研究
Research on Optimization of Lidar Algorithm for Heterogeneous Computing Platform

查看参考文献15篇

文摘 单纯采用CPU处理激光雷达点云数据已无法满足其实时性需求。为此,选用NVIDIA Tegra X1作为异构计算平台,对激光雷达数据处理算法进行加速。结合硬件架构特征和激光雷达数据处理算法的特性,通过粗粒度并行解决GPU优化过程中出现的负载不均衡问题。同时采用零复制和数据本地化的方法进行数据的精细优化。实验结果表明,相较于目前智能车上使用的工控机,优化后的激光雷达数据处理算法能够加速5倍~ 6倍,提高了智能车对雷达数据处理的实时性。
其他语种文摘 Only using CPU to process lidar point cloud data is unable to meet the real-time demand. NVIDIA Tegra X1 is chosen as a heterogeneous computing platform to accelerate the data processing algorithm for lidar. Combined the hardware architecture features and the characteristics of data processing algorithm for lidar,the load imbalance problem in the GPU optimization process is solved by coarse-grained parallelism. Meanwhile,z ero copy and data localization methods are used to fine-tune the optimization. Experimental results show that compared with the current industrial computer used in the smart car,the performance of the improved lidar data processing algorithm is 5 to 6 times faster,which improves the real-time performance of the radar data processing for the smart car.
来源 计算机工程 ,2018,44(7):1-7 【扩展库】
DOI 10.19678/j.issn.1000-3428.0051237
关键词 粗粒度并行 ; 负载不均衡 ; 零复制 ; 数据本地化 ; GPU优化 ; 异构计算平台
地址

北京联合大学, 北京市信息服务工程重点实验室, 北京, 100101

语种 中文
文献类型 研究性论文
ISSN 1000-3428
学科 自动化技术、计算机技术
基金 国家自然科学基金 ;  北京市教委科研基金
文献收藏号 CSCD:6285659

参考文献 共 15 共1页

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引证文献 1

1 邓淇天 基于多特征融合的车辆检测算法 传感器与微系统,2020,39(6):131-134
被引 5

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