基于GPU的全波形并行LM分解算法

波形分解是机载激光雷达全波形数据处理的重要基础工作,通过求解波形函数模型的参数,将波形数据利用具体的函数模型拟合出来,实现对全波形及其中各个子波形函数表达。LM(Levenberg-Marquardt)算法及其改进的算法是波形分解中对参数进行拟合求解的常用方法。针对LM算法在参数拟合计算的过程中存在大量迭代和矩阵运算,提出了基于线程块组和线程两级并行粒度的并行计算方案。将串行多次循环迭代求解参数改为单次并行计算取最佳值实现对参数的选择,将矩阵运算进行线程块的协同并行计算,实现了LM算法在通用计算图形处理器上的并行计算。实验证明,在规定阈值条件下,并行LM降低了算法的迭代次数,提高了波形分解LM算法的计算效率,为提高波形分解的处理效率提供了研究思路。     

基于移动多核GPU的并行二维DCT变换实现方法

传统的基于CPU的串行程序所实现的二维DCT变换算法时间复杂度高变换效率低,难以满足许多应用的实时要求。特别是在当代以嵌入式处理器为核心的移动端信息处理终端,有限的CPU性能更加难以实现快速的DCT变换。值得欣慰的是新一代嵌入式处理器提供了支持GPGPU技术的GPU,为解决复杂的移动计算问题提供了高效的并行化解决途径。基于最新的ARM Cortex-A15内嵌GPU Mali-T604及Open CL框架设计实现了一种针对二维DCT变换的并行化加速方案并实测了优化效果,实验结果表明文中的并行方案能够提高二维DCT变换的效率,在输入数据量足够大的条件下能够达到近20倍的加速比。     

基于GPU的遥感影像SAM分类算法并行化研究

本文简要介绍了并行图像处理的基本模式;对GPU的并行性和流式编程模型进行了分析,提出了GPU并行图像处理的基本流程,并对数据加载、计算结果反馈保存等关键技术问题进行了论述;实现了光谱角匹配(SAM)算法的GPU并行化,最后通过实验验证了该技术方法的有效性。     

基于OpenGL城市大规模管网三维建模

城市管网是城市建设的重要组成部分。本文讨论了基于OpenGL的大规模城市管网三维建模技术,从介绍世界坐标系和局部坐标系的转换关系出发,重点介绍了管网的直线部分建模和管网衔接处的建模,提出了一种基于顶点混合技术来绘制管网衔接处的新算法,该算法方便高效,能产生逼真的效果,并给出了加快三维显示速度的方法,实现了城市管网的大规模实时建模。     

CPU/GPU near real-time preprocessing for ZY-3 satellite images: Relative radiometric correction,MTF compensation,and geocorrection

ZY-3 is the first high-accuracy civil stereo-mapping optical satellite of China. It greatly improves China’s optical satellite image resolution with a boom in data volume, calling for new challenges in processing real-time applications. On the other hand, using central processing unit (CPU)/graphic processing unit (GPU) to resolve data-intensive remote sensing problems becomes a hot issue. In this paper, we present an approach for CPU/GPU near real-time preprocessing of ZY-3 satellite images, focusing on three key processors: relative radiometric correction (RRC), modulation transfer function compensation (MTFC), and geocorrection (GC). First, basic GPU implementation issues are addressed to make the processors capable of processing with GPU. Second, three effective GPU specific optimizations are applied for further improvement of the GPU performance. Furthermore, to fully exploit the CPU’s computing horsepower within the system, a CPU/GPU workload distribution scheme is proposed, in which CPU undertakes partial computation to share the workloads of GPU. The experimental result shows that our approach achieved an overall 48.84-fold speedup ratio in ZY-3 nadir image preprocessing (the corresponding run time is 11.60 s for one image), which is capable of meeting the requirement of near real-time response to the applications that follow. In addition, with the supportability of IEEE 754–2008 floating-point standard in the Fermi type GPU, preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of image preprocess as done traditionally with CPU processors.     

Retinex图像增强在GPU平台上的实现

伴随着无人机时代的到来,对海量数据处理的实时性要求越来越高。本文在GPU（Graphic Processing Unit）平台上实现了Retinex图像增强算法的并行处理,提升了Retinex图像增强算法处理高分辨率数字图像的处理速度。首先,通过数据合并访问和内存数据交互技术实现了数据的快速访问,缩短了数据在不同种类内存间的传输时间,提升了数据访问的效率;然后,采用内核指令优化和数据并行计算技术,实现了Retinex图像增强算法在GPU平台上的多核程序设计;最后,采用主机端和设备端的异步执行模式,在数据传输的同时进行内核数据的并行计算,通过任务级的并行进一步缩短了算法在GPU平台上的执行时间。研究表明,对于不同分辨率的图像,Retinex图像增强算法的处理速度相比于CPU平台均有数十倍的提高,如处理一帧分辨率为2048像元×2048像元的图像仅需要38.04 ms,算法的处理速度较CPU提高了40倍。     

基于GPU的SIFT特征点快速匹配

影像匹配是计算机视觉及数字摄影测量的核心问题,基于特征的SIFT匹配算法由于其较强的匹配能力和良好的健壮性成为研究的热点.但SIFT算子的多量性及提取特征点维度较高的特点,直接影响了匹配速度.因此,本文利用GPU强大的并行处理能力,就SIFT特征点匹配在GPU上的设计与实现进行了详细的介绍,并通过对比实验说明利用GPU对SIFT特征点进行匹配所带来的优势.     

Debris flow simulation-oriented three-dimensional scenery modeling and rendering

3D (three-dimensional) process simulation is currently one of the most challenging fields of research on debris flow.Large scale terrain rendering is the most basic task of 3D scenery construction in debris flow simulation.As the major trigger for debris flow,rainfall will substantially enhance the realistic sense.Terrain and rainfall rendering in 3D debris flow simulations poses great challenges for numerical computation and graphical processing capability.In this paper,we propose to integrate GPU technology,LoD algorithms,and particle systems to realize 3D scenery modeling and rendering.The real-time LoD-based terrain modeling and rendering algorithm is presented first,and then a particle system-based rainfall scenery rendering method is implemented.Experimental results demonstrate that the 3D scenery rendered with the proposed approach exhibits sound performance and fair visual effects,which lays a solid foundation for the whole process simulation of debris flow disasters.     

A GPU accelerated Boussinesq-type model for coastal waves

This study presents an efficient Boussinesq-type wave model accelerated by a single Graphics Processing Unit (GPU). The model uses the hybrid finite volume and finite difference method to solve weakly dispersive and nonlinear Boussinesq equations in the horizontal plane, enabling the model to have the shock-capturing ability to deal with breaking waves and moving shoreline properly. The code is written in CUDA C. To achieve better performance, the model uses cyclic reduction technique to solve massive tridiagonal linear systems and overlapped tiling/shared memory to reduce global memory access and enhance data reuse. Four numerical tests are conducted to validate the GPU implementation. The performance of the GPU model is evaluated by running a series of numerical simulations on two GPU platforms with different hardware configurations. Compared with the CPU version, the maximum speedup ratios for single-precision and double-precision calculations are 55.56 and 32.57, respectively.     

基于GPU实现汉克尔变换并行计算

地球物理勘探技术日新月异,地球物理勘探数据的处理和解释对高性能计算机的要求越来越高.相比于地震勘探,重力、磁法、电法勘探中的并行计算研究还都处于起步阶段.基于GPU的并行计算能够提供强大的计算能力和存储器带宽,同时具有良好的可编程性、较低的成本和较短的开发周期.这里实现了瞬变电磁法一维正演计算中汉克尔变换基于GPU的并行计算,比较了汉克尔变换串行算法和并行算法的计算耗时,基于GPU技术的并行计算相比串行计算,获得了很高的加速比.