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Parallel implementation of wavelet-based image denoising on programmable PC-grade graphics hardware

SIGNAL processing 2010年第8期90卷 2396-2411页

作者： Su, Yang Xu, Zhijie Univ Huddersfield Sch Comp & Engn Huddersfield HD1 3DH W Yorkshire England Xian Univ Sci & Technol Sch Commun & Informat Engn Xian 710054 Peoples R China

The discrete wavelet transform (DWT) has been extensively used for image compression and denoising in the areas of image processing and computer vision. However, the intensive computation of DWT due to its inherent multilevel data decomposition and reconstruction operations brings a bottleneck that drastically reduces its performance and implementations for real-time applications when facing large size digital images and/or high-definition videos. Although various software-based acceleration solutions, such as the lifting scheme, have been devised and achieved a higher performance in general, the pure software accelerated DWT still struggle to cope with the demands from real-time and interactive applications. With the growing capacity and popularity of graphics hardware, personal computers (PCs) nowadays are often equipped with programmable graphics processing units (GPUs) for graphics acceleration. The GPU offers a cost-effective parallel data processing mechanism for operations on large amount of data, even for applications beyond graphics. This practice is commonly referred as general-purpose computing on GPU (GPGPU). This paper presented a GPGPU framework with the corresponding parallel computing solution for wavelet-based image denoising by using off-the-shelf consumer-grade programmable GPUs. This framework can be readily incorporated with different forms of DWT by customizing the parameter of the wavelet kernel. Experiment results show that the framework gains applicability in data parallelism and satisfaction performance in accelerating computations for wavelet-based denoising. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Discrete wavelet transform Image denoising graphics accelerator general-purpose computing on graphics processing unit

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Enhancing productivity and performance portability of OpenCL applications on heterogeneous systems using runtime optimizations

Enhancing productivity and performance portability of OpenCL...

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作者： Lutz, Thibaut University of Edinburgh

学位级别：doctor

Initially driven by a strong need for increased computational performance in science and engineering, heterogeneous systems have become ubiquitous and they are getting increasingly complex. The single processor era has been replaced with multi-core processors, which have quickly been surrounded by satellite devices aiming to increase the throughput of the entire system. These auxiliary devices, such as graphics processing units, Field Programmable Gate Arrays or other specialized processors have very different architectures. This puts an enormous strain on programming models and software developers to take full advantage of the computing power at hand. Because of this diversity and the unachievable flexibility and portability necessary to optimize for each target individually, heterogeneous systems remain typically vastly under-utilized. In this thesis, we explore two distinct ways to tackle this problem. Providing automated, non intrusive methods in the form of compiler tools and implementing efficient abstractions to automatically tune parameters for a restricted domain are two complementary approaches investigated to better utilize compute resources in heterogeneous systems. First, we explore a fully automated compiler based approach, where a runtime system analyzes the computation flow of an OpenCL application and optimizes it across multiple compute kernels. This method can be deployed on any existing application transparently and replaces significant software engineering effort spent to tune application for a particular system. We show that this technique achieves speedups of up to 3x over unoptimized code and an average of 1.4x over manually optimized code for highly dynamic applications. Second, a library based approach is designed to provide a high level abstraction for complex problems in a specific domain, stencil computation. Using domain specific techniques, the underlying framework optimizes the code aggressively. We show that even in a restricted doma

关键词： GPGPU general-purpose computing on graphics processing unit Open Compute Language OpenCL complier staging

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