Accelerating wildfire susceptibility mapping through GPGPU

In the field of wildfire risk management the so-called burn probability maps (BPMs) are increasingly used with the aim of estimating the probability of each point of a landscape to be burned under certain environmental conditions. Such BPMs are usually computed through the explicit simulation of thousands of fires using fast and accurate models. However, even adopting the most optimized algorithms, the building of simulation-based BPMs for large areas results in a highly intensive computational process that makes mandatory the use of high performance computing. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with a wildfire simulation model based on the Cellular Automata approach, to the process of BPM building. Using three different GPGPU devices, the paper illustrates several implementation strategies to speedup the overall mapping process and discusses some numerical results obtained on a real landscape.     

Mali T604 GPU 的二维浮点矩阵运算并行优化方法

基于以三星的 Exynos5250处理器为核心的 Arndale Board 嵌入式开发平台,对集成于处理器上的 Mali T604嵌入式 GPU 的 GPGPU(General-Purpose computation on GPU)技术进行研究,并对不同运算规模的浮点矩阵乘法进行并行加速优化,提供实际测试结果。Linux 操作系统上的实验结果显示,基于 Mali GPU 的并行浮点矩阵乘法方案相对原始串行算法而言,效率显著提高,并且运算规模的增大可以显著提高并行性。     

GPGPU技术研究与发展

半导体工艺的发展使得芯片上集成的晶体管数目不断增加,图形处理器的存储和计算能力也越来越强大。目前,GPU的峰值运算能力已经远远超出主流的CPU,它在非图形计算领域,特别是高性能计算领域的潜力已经引起越来越多研究者的关注。本文介绍了GPU用于通用计算的原理以及目前学术界和产业界关于GPGPU体系结构和编程模型方面的最新研究成果。     

基于GPU的四维医学图像动态快速体绘制

传统的三维医学图像重建技术无法满足四维医学图像动态重建的需求,而四维医学图像庞大的数据量使传统重建技术很难实现高性能实时绘制.基于以上需求,提出了一种四维医学图像动态快速体绘制方法.首先采用GPU强大的并行计算能力,提出一种基于GPU、利用CUDA技术实现的光线投射算法;然后分析了算法框架、体数据及计算结果的存储策略、...     

基于GPGPU的数字图像并行化预处理

首先简要介绍了统一设备架构CUDA(Compute Unified Device Architecture)技术的背景、特点、内存模型,利用通用计算图形处理单元GPGPU(General Purpose GPU)及CUDA技术,实现了图像直方图均衡化和薄云去除的并行化处理,与传统的基于CPU的方法相比,两个基于GPGPU的图像预处理操作的执行效率分别提高了40倍与80倍左右,在大规模实时性图像处理操作中,有很大的实用价值。     

GPGPU性能模型及应用实例分析

现代图形处理器(GPU)的高性能吸引了大量非图形应用,为了有效地进行性能预测和优化,提出一种GPU处理通用计算问题的性能模型.通过分析现代GPU并行架构和工作原理,将GPU的通用计算过程划分为数据获取、计算、输出和传输4个并列的阶段,结合程序特点和硬件规格对各阶段进行量化分析,完成性能预测.通过实验分析得出两大性能影响要素:计算强度和访问密度,并将其作为性能优化的基本准则.该模型被用于分析几种常见的图像和视频处理算法在GPU上的实现,包括高斯卷积、离散余弦变换和运动估计.实验结果表明,通过增大计算强度和访问密度,文中优化方案显著地降低了GPU上的执行时间,使得计算效率提升了4～10倍,充分说明了该模型在性能预测和优化方面的有效性.     

Rigid body constraints realized in massively-parallel molecular dynamics on graphics processing units

Molecular dynamics (MD) methods compute the trajectory of a system of point particles in response to a potential function by numerically integrating Newton?s equations of motion. Extending these basic methods with rigid body constraints enables composite particles with complex shapes such as anisotropic nanoparticles, grains, molecules, and rigid proteins to be modeled. Rigid body constraints are added to the GPU-accelerated MD package, HOOMD-blue, version 0.10.0. The software can now simulate systems of particles, rigid bodies, or mixed systems in microcanonical (NVE), canonical (NVT), and isothermal-isobaric (NPT) ensembles. It can also apply the FIRE energy minimization technique to these systems. In this paper, we detail the massively parallel scheme that implements these algorithms and discuss how our design is tuned for the maximum possible performance. Two different case studies are included to demonstrate the performance attained, patchy spheres and tethered nanorods. In typical cases, HOOMD-blue on a single GTX 480 executes 2.5–3.6 times faster than LAMMPS executing the same simulation on any number of CPU cores in parallel. Simulations with rigid bodies may now be run with larger systems and for longer time scales on a single workstation than was previously even possible on large clusters.     

一种适合于GPU计算的并行后缀数组构造算法

后缀数组广泛应用于序列分析、字符串匹配和文本压缩,近年来,有关后缀数组构造和应用算法的不断探索构成了计算机科学中一个非常活跃的研究领域.在对现有串行算法进行了分析和对比之后,提出了一种新的、简洁的适合于GPU计算的并行后缀数组倍增构造算法,以排序方法替代传统的分组策略,不但能独立完成后缀数组的并行构造,还可与现存的串行倍增算法结合使用,以达到最高的执行效率.实验结果表明该算法在解决实际应用问题时,具有易于实现、执行速度快和可扩展性强等优点,尤其在处理小字符集的数据时效率更高.     

使用CUDA平台关于并行高斯-约当消去法的研究与比较

使用CUDA平台,提出在通用图形处理器(GPGPU)上实现并行的全选主元、归一和消去等操作,加速实现并行全选主元高斯-约当消去法求解线性方程组的一种基本方法。该方法在CPU上完成解向量的恢复。根据NVIDIA公司最新Fermi架构图形处理器的特点,通过一系列的优化设计,使通用GPGPU相对Intel最新架构CPU的加速比超过了6.5倍,比Intel上一代CPU的加速比超过了10倍。     

CUDA-NP：Realizing Nested Thread-Level Parallelism in GPGPU Applications

Parallel programs consist of series of code sections with different thread-level parallelism (TLP). As a result, it is rather common that a thread in a parallel program, such as a GPU kernel in CUDA pr...