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High performance multiscale image processing framework on multi-GPUs (graphics processing units) with applications to unbiased diffeomorphic atlas construction.

机译:多GPU(图形处理单元)上的高性能多尺度图像处理框架,可应用于无偏微晶图集构造。

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摘要

Stochastic methods, dense free-form mapping, atlas construction, and total variation are examples of advanced image processing techniques which are robust but computationally demanding. These algorithms often require a large amount of computational power as well as massive memory bandwidth. These requirements used to be fulfilled only by supercomputers. The development of heterogeneous parallel subsystems and computation-specialized devices such as Graphic Processing Units (GPUs) has brought the requisite power to commodity hardware, opening up opportunities for scientists to experiment and evaluate the influence of these techniques on their research and practical applications. However, harnessing the processing power from modern hardware is challenging. The differences between multicore parallel processing systems and conventional models are significant, often requiring algorithms and data structures to be redesigned significantly for efficiency. It also demands in-depth knowledge about modern hardware architectures to optimize these implementations, sometimes on a per-architecture basis.;The goal of this dissertation is to introduce a solution for this problem based on a 3D image processing framework, using high performance APIs at the core level to utilize parallel processing power of the GPUs. The design of the framework facilitates an efficient application development process, which does not require scientists to have extensive knowledge about GPU systems, and encourages them to harness this power to solve their computationally challenging problems. To present the development of this framework, four main problems are described, and the solutions are discussed and evaluated: (1) essential components of a general 3D image processing library: data structures and algorithms, as well as how to implement these building blocks on the GPU architecture for optimal performance; (2) an implementation of unbiased atlas construction algorithms---an illustration of how to solve a highly complex and computationally expensive algorithm using this framework; (3) an extension of the framework to account for geometry descriptors to solve registration challenges with large scale shape changes and high intensity-contrast differences; and (4) an out-of-core streaming model, which enables developers to implement multi-image processing techniques on commodity hardware.
机译:随机方法,密集的自由形式映射,图集构造和总变化是高级图像处理技术的示例,这些技术虽然健壮但在计算上要求很高。这些算法通常需要大量的计算能力以及庞大的内存带宽。这些要求以前只能由超级计算机来满足。异构并行子系统和图形处理单元(GPU)等计算专用设备的开发为商品硬件带来了必要的功能,为科学家提供了实验和评估这些技术对其研究和实际应用的影响的机会。但是,利用现代硬件的处理能力具有挑战性。多核并行处理系统与常规模型之间的差异非常明显,通常需要对算法和数据结构进行重新设计以提高效率。它还需要对现代硬件体系结构有深入的了解,以便有时甚至基于每个体系结构来优化这些实现。本文的目的是使用高性能的API为基于3D图像处理框架的解决方案引入解决方案。在核心级别利用GPU的并行处理能力。框架的设计促进了高效的应用程序开发过程,该过程不需要科学家对GPU系统有广泛的了解,并鼓励他们利用这种能力来解决他们的计算难题。为了说明此框架的发展,描述了四个主要问题,并对解决方案进行了讨论和评估:(1)通用3D图像处理库的基本组成部分:数据结构和算法,以及如何在这些基础上实现这些构造块GPU架构以获得最佳性能; (2)一种无偏图集构造算法的实现-说明如何使用此框架解决高度复杂且计算量大的算法; (3)扩展框架以解决几何形状描述符问题,以解决大规模形状变化和高强度对比度差异的配准挑战; (4)核心外流模型,使开发人员能够在商品硬件上实现多图像处理技术。

著录项

  • 作者

    Ha, Linh Khanh.;

  • 作者单位

    The University of Utah.;

  • 授予单位 The University of Utah.;
  • 学科 Health Sciences Radiology.;Computer Science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:45:08

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