Survey on hardware acceleration schemes for ray tracing

doi:10.11772/j.issn.1001-9081.2024030399

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (5): 1632-1644.DOI: 10.11772/j.issn.1001-9081.2024030399

• Multimedia computing and computer simulation • Previous Articles

Survey on hardware acceleration schemes for ray tracing

Daquan ZHANG¹, Jiarui DONG¹, Yang LEI¹, Shikang LI¹, Xiangyu SHI¹, Zonghui LI¹(), Yangdong DENG², Weimin WU¹

^1.School of Computer Science and Technology，Beijing Jiaotong University，Beijing 100044，China
^2.School of Software，Tsinghua University，Beijing 100084，China

Received:2024-05-09 Revised:2024-10-02 Accepted:2024-10-12 Online:2024-11-22 Published:2025-05-10
Contact: Zonghui LI
About author:ZHANG Daquan， born in 2000， M. S. candidate. His research interests include computer graphics rendering， machine learning.
DONG Jiarui， born in 1998， M. S. Her research interests include ray tracing， high-performance rendering.
LEI Yang， born in 2001， M. S. candidate. His research interests include ray tracing， time-sensitive networking.
LI Shikang， born in 2002， M. S. candidate. His research interests include federated learning， ray tracing.
SHI Xiangyu， born in 2000， M. S. candidate. His research interests include natural language processing， computer graphics.
DENG Yangdong， Ph. D.， associate professor. His research interests include industrial big data， neuromorphic computing.
WU Weimin， Ph. D.， associate research fellow. His research interests include high-performance computing， artificial intelligence， network and information security.
Supported by:
Fundamental Research Funds for Central Universities(2024JBMC035)

光线追踪硬件加速方案综述

张大权¹, 董家瑞¹, 雷洋¹, 李世康¹, 石响宇¹, 李宗辉¹(), 邓仰东², 吴为民¹

^1.北京交通大学计算机科学技术学院，北京 100044
^2.清华大学软件学院，北京 100084

通讯作者: 李宗辉
作者简介:张大权（2000—），男，福建龙岩人，硕士研究生，主要研究方向：计算机图形学渲染、机器学习；
董家瑞（1998—），女，河南许昌人，硕士，主要研究方向：光线追踪、高性能渲染；
雷洋（2001—），男，河北石家庄人，硕士研究生，主要研究方向：光线追踪、时间敏感网络；
李世康（2002—），男，湖北孝感人，硕士研究生，主要研究方向：联邦学习、光线追踪；
石响宇（2000—），男，河南邓州人，硕士研究生，主要研究方向：自然语言处理、计算机图形学；
邓仰东，男，山西太原人，副教授，博士，主要研究方向：工业大数据、类脑计算；
吴为民，男，吉林辽源人，副研究员，博士，主要研究方向：高性能计算、人工智能、网络与信息安全。
基金资助:
中央高校基本科研业务费专项基金资助项目(2024JBMC035)

Abstract

Abstract:

Nowadays， real-time 3D graphics rendering is undergoing technological innovation， with a surge in applications of real-time ray tracing technology. However， from a computational perspective， ray tracing remains expensive， as traditional hardware cannot support such computational demands. New Graphics Processing Units （GPUs） must balance performance， power consumption， and higher complexity scenarios， making hardware acceleration technologies central to real-time ray tracing. Firstly， the theoretical foundations of ray tracing was introduced， and based on the two most dominant accelerated data structures — KD-Tree （K-Dimensional Tree） and Bounding Volume Hierarchies based on Tree （BVH-Tree）， primitive segmentation， construction methods， optimization methods， and traversal acceleration were investigated to reveal the potential of these two structures for hardware acceleration. Secondly， the dedicated acceleration hardware developed in each stage were summarized from three perspectives： fixed-function design， hardware architecture design， and scheduling and data management to reduce memory bandwidth. Thirdly， mainstream industry oriented ray tracing GPU solutions and future development trends for industry were researched. Finally， the current situation and limitations of hardware acceleration schemes were discussed， along with potential directions for performance optimization.

Key words: ray tracing, hardware acceleration, K-Dimensional Tree (KD-Tree), Bounding Volume Hierarchies based on Tree (BVH-Tree), Graphics Processing Unit (GPU)

摘要：

当前，实时三维图形渲染领域发生着技术变革，实时光线追踪技术的应用激增；但就计算而言，光线追踪成本依旧“昂贵”，传统硬件无法支持这样的算力。新的图形处理单元（GPU）必须在性能、功耗和高复杂度场景之间获取平衡，硬件加速技术因此成为实时光线追踪的核心。首先，介绍了光线追踪的理论基础，基于目前最主流的2种硬件加速数据结构（KD-Tree（K-Dimensional Tree）和层次包围盒树（BVH-Tree）），分别从基元分割、构造方法、优化方法和遍历加速的角度进行调研，发掘这2种结构用于硬件加速的潜力；其次，从固定函数设计、硬件架构设计、以减少内存带宽为目标的调度和数据管理这3个角度，对各个阶段所开发的专用加速硬件进行总结；再次，面向产业界调研主流的光线追踪GPU的产业界解决方案以及未来发展趋势；最后，总结并讨论光线追踪硬件加速方案的现状与不足，并展望了这些方案的性能优化方向。

关键词: 光线追踪, 硬件加速, KD-Tree, 层次包围盒树, 图形处理单元

CLC Number:

TP391.4

Daquan ZHANG, Jiarui DONG, Yang LEI, Shikang LI, Xiangyu SHI, Zonghui LI, Yangdong DENG, Weimin WU. Survey on hardware acceleration schemes for ray tracing[J]. Journal of Computer Applications, 2025, 45(5): 1632-1644.

张大权, 董家瑞, 雷洋, 李世康, 石响宇, 李宗辉, 邓仰东, 吴为民. 光线追踪硬件加速方案综述[J]. 《计算机应用》唯一官方网站, 2025, 45(5): 1632-1644.

Figures/Tables 7

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107	SPJUT J， KENSLER A， KOPTA D， et al. TRaX： a multicore hardware architecture for real-time ray tracing［J］. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems， 2009， 28（12）： 1802-1815.
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时间	光线追踪技术发展	硬件加速方案
1968年^［95］	光线追踪概念提出	无专用硬件加速，主要依赖CPU计算
1980年^［96］	Whitted 光线追踪	无专用硬件加速，主要依赖CPU计算
1984年^［97］	分布式光线追踪	专用光线追踪硬件（如RTMs）开始出现，用于加速特定光线追踪任务
2000年^［98-99］	全局光照算法	GPU加速开始应用于光线追踪，主要在非实时应用中如电影制作
2018年^［100］	实时光线追踪	GPU加速显著提升，NVIDIA和AMD的高性能GPU被广泛应用于光线追踪任务中
2018—2022年^{［101-104］}	GPU融入光线追踪专用计算单元	NVIDIA RTX架构引入RT Core，实现实时光线追踪硬件加速。AMD RDNA 2架构推出，支持硬件加速光线追踪。Intel Arc GPU系列引入硬件光线追踪支持及新的优化算法
2023年至今	光线追踪与AI结合	各大GPU厂商在新一代架构中整合AI加速器，用于优化光线追踪渲染和降低计算成本

时间	光线追踪技术发展	硬件加速方案
1968年^［95］	光线追踪概念提出	无专用硬件加速，主要依赖CPU计算
1980年^［96］	Whitted 光线追踪	无专用硬件加速，主要依赖CPU计算
1984年^［97］	分布式光线追踪	专用光线追踪硬件（如RTMs）开始出现，用于加速特定光线追踪任务
2000年^［98-99］	全局光照算法	GPU加速开始应用于光线追踪，主要在非实时应用中如电影制作
2018年^［100］	实时光线追踪	GPU加速显著提升，NVIDIA和AMD的高性能GPU被广泛应用于光线追踪任务中
2018—2022年^{［101-104］}	GPU融入光线追踪专用计算单元	NVIDIA RTX架构引入RT Core，实现实时光线追踪硬件加速。AMD RDNA 2架构推出，支持硬件加速光线追踪。Intel Arc GPU系列引入硬件光线追踪支持及新的优化算法
2023年至今	光线追踪与AI结合	各大GPU厂商在新一代架构中整合AI加速器，用于优化光线追踪渲染和降低计算成本

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