异构计算平台激光雷达算法优化研究

doi:10.19678/j.issn.1000-3428.0051237

计算机工程 ›› 2018, Vol. 44 ›› Issue (7): 1-7. doi: 10.19678/j.issn.1000-3428.0051237

所属专题：智能交通专题；

异构计算平台激光雷达算法优化研究

许武,梁军,李威,徐鹏飞,徐圣瑞,张福贵

北京联合大学北京市信息服务工程重点实验室,北京 100101

收稿日期:2018-01-17 出版日期:2018-07-15 发布日期:2018-07-15
作者简介:许武(1994—),男,硕士研究生,主研方向为智能驾驶算法优化;梁军(通信作者),教授;李威、徐鹏飞、徐圣瑞,硕士研究生;张福贵,硕士。
基金资助:
国家自然科学基金“视听觉信息的认知计算”重大研究计划重点支持项目“智能车驾驶脑认知技术、平台与转化研究”(91420202),由北京联合大学和原总参61所合作承担,鲍泓教授为项目负责人、李德毅院士为首席专家,横向建立了十三个课题组群,纵向建立了“猛狮”“京龙”和宇通等六个车队群,全过程实行有效的矩阵式管理。该项目的核心是研究“如何在开放道路条件下,自主完成各类驾驶行为、可用于深度学习和自学习的机器驾驶脑、不确定性驾驶环境的认知”这一系列科学问题;北京市教委科研计划项目(KM201811417006)。

Research on Optimization of Lidar Algorithm for Heterogeneous Computing Platform

XU Wu,LIANG Jun,LI Wei,XU Pengfei,XU Shengrui,ZHANG Fugui

Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China

Received:2018-01-17 Online:2018-07-15 Published:2018-07-15

摘要/Abstract

摘要： 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。

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

Abstract: 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,zero 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.

Key words: coarse-grained parallelism, load imbalance, zero copy, data locality, GPU optimization, heterogeneous computing platform

中图分类号:

TP301.6

许武,梁军,李威,徐鹏飞,徐圣瑞,张福贵. 异构计算平台激光雷达算法优化研究[J]. 计算机工程, 2018, 44(7): 1-7.

XU Wu,LIANG Jun,LI Wei,XU Pengfei,XU Shengrui,ZHANG Fugui. Research on Optimization of Lidar Algorithm for Heterogeneous Computing Platform[J]. Computer Engineering, 2018, 44(7): 1-7.

http://www.ecice06.com/CN/Y2018/V44/I7/1

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