GPU-based out-of-core many-lights rendering

Wang, Rui; Huo, Yuchi; Yuan, Yazhen; Zhou, Kun; Wei, Hua; Bao, Hujun

doi:10.1145/2508363.2508413

Cited by 55 publications

(13 citation statements)

References 35 publications

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“…[1] which performs a tiled Cholesky factorisation -in this case there is enough computational work per byte uploaded. Similar streaming techniques are used for computationally intensive algorithms in [11], and there are applications in visualisation as well [20,27].…”

Section: Related Workmentioning

confidence: 99%

Beyond 16GB

Reguly

Mudalige

Giles

2017

Proceedings of the Workshop on Memory Centric Programming for HPC

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Stencil computations are a key class of applications, widely used in the scienti c computing community, and a class that has particularly bene ted from performance improvements on architectures with high memory bandwidth. Unfortunately, such architectures come with a limited amount of fast memory, which is limiting the size of the problems that can be e ciently solved. In this paper, we address this challenge by applying the well-known cache-blocking tiling technique to large scale stencil codes implemented using the OPS domain speci c language, such as CloverLeaf 2D, CloverLeaf 3D, and OpenSBLI. We introduce a number of techniques and optimisations to help manage data resident in fast memory, and minimise data movement. Evaluating our work on Intel's Knights Landing Platform as well as NVIDIA P100 GPUs, we demonstrate that it is possible to solve 3 times larger problems than the on-chip memory size with at most 15% loss in e ciency. ACM Reference format:

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Section: Related Workmentioning

confidence: 99%

Beyond 16GB

Reguly

Mudalige

Giles

2017

Proceedings of the Workshop on Memory Centric Programming for HPC

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“…Ou and Pellacini [OP11] cluster the shading points into groups called slices and perform row‐column sampling for each slice. Wang et al [WHY*13] proposed an out‐of‐core many‐light rendering method. Huo et al [HWJ*15] proposed a matrix sampling‐and‐recovery approach for many‐light rendering.…”

Section: Previous Workmentioning

confidence: 99%

An Error Estimation Framework for Many‐Light Rendering

Nabata

Iwasaki

Dobashi

et al. 2016

Computer Graphics Forum

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Figure 1: Rendering results of (a) our method, relative error images of (b) our method and (c) Lightcuts [WFA * 05] in Sponza scene. Relative error threshold ε = 2% and confidence level α = 95% are specified. 92.96% pixels satisfy that the relative error is within 2% in our method, while only 43.67% pixels satisfy the condition in Lightcuts. In (d) Kitchen scene and (e) San Miguel scene, Cook-Torrance BRDFs and Ashikhmin-Shirely BRDFs are used, which cannot be used in Lightcuts. (f) and (g) show relative error images of (d) and (e), respectively. AbstractThe popularity of many-light rendering, which converts complex global illumination computations into a simple sum of the illumination from virtual point lights (VPLs), for predictive rendering has increased in recent years. A huge number of VPLs are usually required for predictive rendering at the cost of extensive computational time. While previous methods can achieve significant speedup by clustering VPLs, none of these previous methods can estimate the total errors due to clustering. This drawback imposes on users tedious trial and error processes to obtain rendered images with reliable accuracy. In this paper, we propose an error estimation framework for many-light rendering. Our method transforms VPL clustering into stratified sampling combined with confidence intervals, which enables the user to estimate the error due to clustering without the costly computing required to sum the illumination from all the VPLs. Our estimation framework is capable of handling arbitrary BRDFs and is accelerated by using visibility caching, both of which make our method more practical. The experimental results demonstrate that our method can estimate the error much more accurately than the previous clustering method.

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“…The later LightSlice approach [9] combines the best of lightcuts and matrix sampling by locally refining light clusters for a cluster of shading points. Wang et al [39] presented a scheduling system for rendering out-ofcore geometry and lights on GPUs. Unfortunately, none of these methods discussed how to handle translucent materials.…”

Section: Scalable Many-light Renderingmentioning

confidence: 99%

Dual-Matrix Sampling for Scalable Translucent Material Rendering

Lin

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-This paper introduces a scalable algorithm for rendering translucent materials with complex lighting. We represent the light transport with a diffusion approximation by a dual-matrix representation with the Light-to-Surface and Surface-to-Camera matrices. By exploiting the structures within the matrices, the proposed method can locate surface samples with little contribution by using only subsampled matrices and avoid wasting computation on these samples. The decoupled estimation of irradiance and diffuse BSSRDFs also allows us to have a tight error bound, making the adaptive diffusion approximation more efficient and accurate. Experiments show that our method outperforms previous methods for translucent material rendering, especially in large scenes with massive translucent surfaces shaded by complex illumination.

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GPU-based out-of-core many-lights rendering

Cited by 55 publications

References 35 publications

Beyond 16GB

Beyond 16GB

An Error Estimation Framework for Many‐Light Rendering

Dual-Matrix Sampling for Scalable Translucent Material Rendering

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