Automatic shader simplification using surface signal approximation

Wang, Rui; Yang, Xianjin; Yuan, Yazhen; Chen, Wei; Bala, Kavita; Bao, Hujun

doi:10.1145/2661229.2661276

Cited by 49 publications

(26 citation statements)

References 23 publications

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“…Sitthi‐Amorn et al [SAMWL11] described a GP approach to the problem of automatic procedural shader simplification. Other researchers have also investigated automatic shader simplification by heuristic search methods that simplify programs [OKS, Pel, HFTF15], and by jointly modifying shaders and geometry [WYY*14]. Brady and colleagues [BLPW14] showed how to use GP to discover new analytic reflectance functions.…”

Section: Related Workmentioning

confidence: 99%

Approximate Program Smoothing Using Mean‐Variance Statistics, with Application to Procedural Shader Bandlimiting

Yang

Barnes

2018

Computer Graphics Forum

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We introduce a general method to approximate the convolution of a program with a Gaussian kernel. This results in the program being smoothed. Our compiler framework models intermediate values in the program as random variables, by using mean and variance statistics. We decompose the input program into atomic parts and relate the statistics of the different parts of the smoothed program. We give several approximate smoothing rules that can be used for the parts of the program. These include an improved variant of Dorn et al. [DBLW15], a novel adaptive Gaussian approximation, Monte Carlo sampling, and compactly supported kernels. Our adaptive Gaussian approximation handles multivariate Gaussian distributed inputs, gives exact results for a larger class of programs than previous work, and is accurate to the second order in the standard deviation of the kernel for programs with certain analytic properties. Because each expression in the program can have multiple approximation choices, we use a genetic search to automatically select the best approximations. We apply this framework to the problem of automatically bandlimiting procedural shader programs. We evaluate our method on a variety of geometries and complex shaders, including shaders with parallax mapping, animation, and spatially varying statistics. The resulting smoothed shader programs outperform previous approaches both numerically and aesthetically.

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Section: Related Workmentioning

confidence: 99%

Approximate Program Smoothing Using Mean‐Variance Statistics, with Application to Procedural Shader Bandlimiting

Yang

Barnes

2018

Computer Graphics Forum

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“…Sitthi‐Amorn et al [SAMWL11] introduced the genetic programming into automatic shader simplification to search for the Pareto‐optimal simplified shaders that best represent the tradeoffs between rendering time and quality. Wang et al [WYY*14] proposed new simplification rules that treat the shader simplification as signal approximation on surfaces to simplify shaders across multiple shader stages. However, these methods perform optimization in an offline scheme and are thus unable to optimally adapt to the runtime changes of context.…”

Section: Related Workmentioning

confidence: 99%

“…where x and y are the integer pixel coordinates, v is a set of input geometry primitives with attributes, u is a set of uniform parameters (such as camera position and light position), and || · || 2 is the norm, (i.e., L 2 norm used in this paper). Based on the measured rendering time and image error, shader simplification is formulated as a multi‐objective problem in the two‐dimensional cost space (∊, t ), and seeks a Pareto‐optimal solution [SAMWL11,WYY*14,HFTF15]. To consider the variations of geometry primitives and uniform parameters, offline shader simplification usually integrates the error over the domain of each uniform parameter, and averages over different geometry models.…”

Section: Overviewmentioning

confidence: 99%

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Runtime Shader Simplification via Instant Search in Reduced Optimization Space

Yuan

Wang

et al. 2018

Computer Graphics Forum

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Traditional automatic shader simplification simplifies shaders in an offline process, which is typically carried out in a context‐oblivious manner or with the use of some example contexts, e.g., certain hardware platforms, scenes, and uniform parameters, etc. As a result, these pre‐simplified shaders may fail at adapting to runtime changes of the rendering context that were not considered in the simplification process. In this paper, we propose a new automatic shader simplification technique, which explores two key aspects of a runtime simplification framework: the optimization space and the instant search for optimal simplified shaders with runtime context. The proposed technique still requires a preprocess stage to process the original shader. However, instead of directly computing optimal simplified shaders, the proposed preprocess generates a reduced shader optimization space. In particular, two heuristic estimates of the quality and performance of simplified shaders are presented to group similar variants into representative ones, which serve as basic graph nodes of the simplification dependency graph (SDG), a new representation of the optimization space. At the runtime simplification stage, a parallel discrete optimization algorithm is employed to instantly search in the SDG for optimal simplified shaders. New data‐driven cost models are proposed to predict the runtime quality and performance of simplified shaders on the basis of data collected during runtime. Results show that the selected simplifications of complex shaders achieve 1.6 to 2.5 times speedup and still retain high rendering quality.

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“…Due to their simplicity and mathematical properties, Bézier surfaces have been employed in applications such as surface reconstruction from clouds of points [3], modeling of free-form deformations [4,5], interactive manipulation of three-dimensional meshes and rendering [6][7][8], bio-mechanical modeling [9], hybrid volumetric object representation [10], registration in medical imaging [11,12], and computer games [13] among others.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Computation of Bézier Surfaces on Parallel and Heterogeneous Platforms

Palomar

Gómez-Luna

Cheikh³

et al. 2017

Int J Parallel Prog

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Bézier surfaces are mathematical tools employed in a wide variety of applications. Some works in the literature propose parallelization strategies to improve performance for the computation of Bézier surfaces. These approaches, however, are mainly focused on graphics applications and often are not directly applicable to other domains. In this work, we propose a new method for the computation of Bézier surfaces, together with approaches to efficiently map the method onto different platforms (CPUs, discrete and integrated GPUs). Additionally, we explore CPU-GPU cooperation mechanisms for computing Bézier surfaces using two integrated heterogeneous systems with different characteristics. An exhaustive performance evaluation-including different data-types, rendering and several hardware platforms-is performed. The results show that our method achieves speedups as high as 3.12x (double-precision) and 2.47x (single-precision) on CPU, and 3.69x (double-precision) and 13.14x (single-precision) on GPU compared to other methods in the literature. In heterogeneous platforms, the CPU-GPU cooperation increases the performance up to 2.09x with respect to the GPU-only version. Our method and the 123Int J Parallel Prog associated parallelization approaches can be easily employed in domains other than computer-graphics (e.g., image registration, bio-mechanical modeling and flow simulation), and extended to other Bézier formulations and Bézier constructions of higher order than surfaces.

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Automatic shader simplification using surface signal approximation

Cited by 49 publications

References 23 publications

Approximate Program Smoothing Using Mean‐Variance Statistics, with Application to Procedural Shader Bandlimiting

Approximate Program Smoothing Using Mean‐Variance Statistics, with Application to Procedural Shader Bandlimiting

Runtime Shader Simplification via Instant Search in Reduced Optimization Space

High-Performance Computation of Bézier Surfaces on Parallel and Heterogeneous Platforms

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