A New Rendering Technology of GPU-Accelerated Radiosity

Hu Wei and Qin Kaihuai

Journal of Computer Research and Development > 2005 > 42(6): 945-950.

Hu Wei and Qin Kaihuai. A New Rendering Technology of GPU-Accelerated Radiosity[J]. Journal of Computer Research and Development, 2005, 42(6): 945-950.

Citation:

Hu Wei and Qin Kaihuai. A New Rendering Technology of GPU-Accelerated Radiosity[J]. Journal of Computer Research and Development, 2005, 42(6): 945-950.

Citation:

Hu Wei and Qin Kaihuai. A New Rendering Technology of GPU-Accelerated Radiosity[J]. Journal of Computer Research and Development, 2005, 42(6): 945-950.

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A New Rendering Technology of GPU-Accelerated Radiosity

Hu Wei and Qin Kaihuai

(Department of Computer Science and Technology, Tsinghua University, Beijing 100084)

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Published Date: June 14, 2005

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Abstract

Abstract

A new rendering technology of GPU-accelerated radiosity is presented in this paper. Exploiting parallel computation power of current graphics hardware, the method implements entire classical radiosity solution on GPU without participation of CPU. Using new OpenGL extensions to realize texture traverse, classification and accumulation, the rendering results of hemi-cube method can be used directly on GPU. New Jacobi iteration solution is also proposed based on the matrix and vector representations on GPUs.
- radiosity,
- programmable graphics hardware,
- GPU

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[1]	Sun Qingxiao, Yang Hailong. Generalized Stencil Auto-Tuning Framework on GPU Platform[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440612
[2]	Xie Minhui, Lu Youyou, Feng Yangyang, Shu Jiwu. A Recommendation Model Inference System Based on GPU Direct Storage Access Architecture[J]. Journal of Computer Research and Development, 2024, 61(3): 589-599. DOI: 10.7544/issn1000-1239.202330402
[3]	Fu Maozhong, Hu Haiyang, Li Zhongjin. Dynamic Resource Scheduling Method for GPU Cluster[J]. Journal of Computer Research and Development, 2023, 60(6): 1308-1321. DOI: 10.7544/issn1000-1239.202220149
[4]	Cao Kun, Long Saiqin, Li Zhetao. Lifetime-Driven OpenCL Application Scheduling on CPU-GPU MPSoC[J]. Journal of Computer Research and Development, 2023, 60(5): 976-991. DOI: 10.7544/issn1000-1239.202220700
[5]	Li Maowen, Qu Guoyuan, Wei Dazhou, Jia Haipeng. Performance Optimization of Neural Network Convolution Based on GPU Platform[J]. Journal of Computer Research and Development, 2022, 59(6): 1181-1191. DOI: 10.7544/issn1000-1239.20200985
[6]	Zhang Heng, Zhang Libo, WuYanjun. Large-Scale Graph Processing on Multi-GPU Platforms[J]. Journal of Computer Research and Development, 2018, 55(2): 273-288. DOI: 10.7544/issn1000-1239.2018.20170697
[7]	Jia Jia, Yang Xuejun, Li Zhiling. A Redundancy-Multithread-Based Multiple GPU Copies Fault-Tolerance Technique[J]. Journal of Computer Research and Development, 2013, 50(7): 1551-1562.
[8]	Wang Zhuowei, Xu Xianbin, Zhao Wuqing, He Shuibing, Zhang Yuping. Parallel Acceleration and Performance Optimization for GRAPES Model Based on GPU[J]. Journal of Computer Research and Development, 2013, 50(2): 401-411.
[9]	Wu Xiaoxiao, Liang Xiaohui, Xu Qidi, and Zhao Qinping. An Algorithm of Physically-based Scalar-fields Guided Deformation on GPU[J]. Journal of Computer Research and Development, 2010, 47(11): 1857-1864.
[10]	Wang Jing, Wang Lili, and Li Shuai. Pre-Computed Radiance Transport All-Frequency Shadows Algorithm on GPU[J]. Journal of Computer Research and Development, 2006, 43(9): 1505-1510.