GPU的通用计算应用研究

由于图形处理器（GPU）最近几年迅速发展,国内外学者已经将基于GPU的通用计算作为一个新的研究领域。本文在研究国外最新文献的基础上,分析了GPU本身的特性,阐明了基于GPU的应用程序的结构,研究了GPU在编程方法上与普通CPU的差别,并以高斯滤波为实例详细描述了GPU编程的方法和过程。     

Surfing the optimization space of a multiple-GPU parallel implementation of a X-ray tomography reconstruction algorithm

The increasing popularity of massively parallel architectures based on accelerators have opened up the possibility of significantly improving the performance of X-ray computed tomography (CT) applications towards achieving real-time imaging. However, achieving this goal is a challenging process, as most CT applications have not been designed for exploiting the amount of parallelism existing in these architectures. In this paper we present the massively parallel implementation and optimization of Mangoose++, a CT application for reconstructing 3D volumes from 2D images collected by scanners based on cone-beam geometry. The main contribution of this paper are the following. First, we develop a modular application design that allows to exploit the functional parallelism inside the application and to facilitate the parallelization of individual application phases. Second, we identify a set of optimizations that can be applied individually and in combination for optimally deploying the application on a massively parallel multi-GPU system. Third, we present a study of surfing the optimization space of the modularized application and demonstrate that a significant benefit can be obtained from employing the adequate combination of application optimizations.     

Kernelized correlation tracker on smartphones

This paper shows the implementation of a KC tracker (high-speed kernelized correlation tracker) on an Android smartphone. The image processing part is implemented with the Android-NDK in C/C++. Some parts of the tracking algorithm, which can be parallelized very well, are partitioned and calculated on the GPU with OpenGL ES and OpenCL. Other parts, such as the Discrete Fourier Transform (DFT), are calculated on the CPU (partly with the ARM-NEON features). With these hardware acceleration steps we could reach real-time performance (at least 20–30 FPS) on up-to-date smartphones, such as Samsung Galaxy S4, S5 or Google Nexus 5.Beyond that, we present some new color features and compare their tracking quality to the HOG features using the KC tracker and show that their tracking quality is mostly superior compared to the HOG features.If an object gets lost by the tracker which is the case e.g. if the object is totally hidden or outside the viewing range, there should be a possibility to perform a re-detection. In this paper, we show a basic approach to determine the tracking quality and search for the tracking object in the entire images of the subsequent video-frames.     

基于集群和GPGPU技术的三维地形数据生成方法研究

针对三维地形数据生成时间过长的问题,提出了一种基于集群和GPGPU技术的HPC(High Performance Computing)高性能计算方法,能够有效整合集群中多台计算机的CPU和GPU的处理能力.在三维地形数据生成过程中,该方法将地形数据进行二次细化,分别分配给GPU的每个任务管线,大幅度提高运算吞吐量,缩短三维地形数据的生成时间.     

SURF算法在通用GPU和OpenCL的优化与实现

Speeded Up Robust Feature（SURF）算法是在计算机视觉领域得到广泛应用的一种图像兴趣点检测和匹配方法。开放计算语言（OpenCL）提供了一个在异构体系结构上,包括GPU,CPU及其他类型处理器,编写并行程序的框架。本文介绍了如何在通用GPU和OpenCL平台上,对SURF算法进行优化与实现。本文对其中一些优化方法,例如kernel线程的配置,局部内存的使用方法等,进行了详细的对比和讨论。最终实现的OpenCL版本的算法在NVidiaGTX260平台上获得了比原始的CPU版本在IntelDual—CoreE54002．7G处理器上至少21倍的加速。     

Real-time flow-based video abstraction using OpenCL

A non-photorealistic rendering technique is a method to show various effects different from those of realistic image generation.Of the various techniques,flow-based image abstraction displays the shape...     

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

The introduction of NVidia’s powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.     

Real-time stereo on GPGPU using progressive multi-resolution adaptive windows

We introduce a new GPGPU-based real-time dense stereo matching algorithm. The algorithm is based on a progressive multi-resolution pipeline which includes background modeling and dense matching with adaptive windows. For applications in which only moving objects are of interest, this approach effectively reduces the overall computation cost quite significantly, and preserves the high definition details. Running on an off-the-shelf commodity graphics card, our implementation achieves a 36 fps stereo matching on 1024 × 768 stereo video with a fine 256 pixel disparity range. This is effectively same as 7200 M disparity evaluations per second. For scenes where the static background assumption holds, our approach outperforms all published alternative algorithms in terms of the speed performance, by a large margin. We envision a number of potential applications such as real-time motion capture, as well as tracking, recognition and identification of moving objects in multi-camera networks.     

一个SPH流体实时模拟的全GPU实现框架

怎样实时地进行高度逼真的大规模流体模拟是图形学要研究的一个重要内容。流体的模拟由物理计算、碰撞检测、表面重构和渲染几个部分组成,因此有大量工作针对流体模拟中的各个部分算法进行GPU加速。提出一整套基于GPU的SPH流体模拟加速框架。在利用平滑粒子动力学(SPH)求解Navier-Stokes方程的基础上,借助基于GPU的空间划分PSS(Parallel Spatial Subdivision)来大幅度提升粒子碰撞的检测速度。同时,设计一种基于几何着色器(Geometry Shader)的流体表面信息的重建算法,并进一步地实现基于索引的优化,使得在流体表面重建过程无须遍历不包含表面的区域。实验结果表明,该方法能实时模拟出具有较好真实感的流体场景。     

基于OpenCL的并行方腔流加速性能分析*

本文提出了一种使用OpenCL技术对方腔流问题进行加速计算的方法。在计算方腔流问题时,本文将其转换为Ｎ-Ｓ方程通过空间有限差分和龙格库塔时间差分求解,并使用局部缓存等技术进行GPU优化。实验在Nvidia和ATI平台对所给算法进行评测。结果显示,OpenCL相对其串行版本加速约30倍左右。