Particle-in-Cell algorithms for emerging computer architectures

We have designed Particle-in-Cell algorithms for emerging architectures. These algorithms share a common approach, using fine-grained tiles, but different implementations depending on the architecture. On the GPU, there were two different implementations, one with atomic operations and one with no data collisions, using CUDA C and Fortran. Speedups up to about 50 compared to a single core of the Intel i7 processor have been achieved. There was also an implementation for traditional multi-core processors using OpenMP which achieved high parallel efficiency. We believe that this approach should work for other emerging designs such as Intel Phi coprocessor from the Intel MIC architecture.     

面向大规模数据流处理的偏最小二乘法

用光谱分析鉴别生物特征,导致数据量大,而实际需要必须实时处理。偏最小二乘法是使用最广泛的鉴别算法,但是对于大规模数据流该算法无法达到实时性。为了解决这个应用矛盾,提出了一种基于NVIDIA CUDA架构下的并行计算策略,利用具有大规模并行计算特征的图形处理器（GPU）作为计算设备,结合GPU存储器的优势实现了偏最小二乘算法。实验的测试结果表明,在GPU上使用CUDA实现的偏最小二乘算法比在CPU上实现该算法快了47倍,性能得到了显著提高,从而使偏最小二乘算法应用于大规模数据流处理成为可能。     

基于CUDA的AES并行算法优化

为提升高级加密标准(AES)的加密性能,利用显卡的通用计算能力,在统一计算设备架构(CUDA)平台上实现AES的128位、192位和256位3个版本的GPU并行算法,并提出优化的AES并行算法。在考虑块内线程数量、共享存储器容量和总块数的基础上,根据分块最优值的经验数据指导AES算法在GPU上的最优分块。实验结果表明,与未优化的AES并行算法相比,该算法的3个版本在Nvidia Geforce G210显卡上的加密速度分别提高5.28%,14.55%和12.53%,而在Nvidia Geforce GTX460显卡上的加密速度分别提高12.48%,15.40%和15.84%,且能更好地对SSL数据进行加密。     

基于CUDA架构的FFT并行计算研究

FFT（快速傅里叶变换）是基于提高DFT（离散傅里叶变换）计算的高效算法,它在众多科学和工程领域都得到了广泛的应用。自FFT算法出现以后,从早期的以降低复杂度到近年以来的大规模并行FFT计算,各种优化算法得到广泛的研究。在并行运算领域中,随着可编程的、并行化GPU的不断推广,特别是通用并行统一计算架构CUDA的出现,极大增强了GPU的计算能力,在编程和优化等方面都有显著地提升。鉴于此,本文在分析FFT算法实现的基础上,研究了一种适合GPU运算的FFT并行计算方法,并通过CUDA架构实现了FFT算法在GPU上的运算。该方法的引入在理论不计算数据传输的情况下,使一维FFT运算时间的复杂度由O（N logN2）可以降到O（N/rlogN2）。通过验证,本文提出的CUDA的并行FFT方法得到较好的加速效果,在精度计算上也符合实际的要求,从而证明了该方法的正确性和有效性。     

Improving cache locality for GPU-based volume rendering

We present a cache-aware method for accelerating texture-based volume rendering on a graphics processing unit (GPU). Because a GPU has hierarchical architecture in terms of processing and memory units, cache optimization is important to maximize performance for memory-intensive applications. Our method localizes texture memory reference according to the location of the viewpoint and dynamically selects the width and height of thread blocks (TBs) so that each warp, which is a series of 32 threads processed simultaneously, can minimize memory access strides. We also incorporate transposed indexing of threads to perform TB-level cache optimization for specific viewpoints. Furthermore, we maximize TB size to exploit spatial locality with fewer resident TBs. For viewpoints with relatively large strides, we synchronize threads of the same TB at regular intervals to realize synchronous ray propagation. Experimental results indicate that our cache-aware method doubles the worst rendering performance compared to those provided by the CUDA and OpenCL software development kits.     

Parallel Implementation of the BiCGStab（2） Method in GPU Using CUDA and Matlab for Solution of Linear Systems

This paper presents a parallel implementation of the hybrid BiCGStab（2） （bi-conjugate gradient stabilized） iterative method in a GPU （graphics processing unit） for solution of large and sparse linear systems. This implementation uses the CUDA-Matlab integration, in which the method operations are performed in a GPU core using Matlab built-in functions. The goal is to show that the exploitation of parallelism by using this new technology can provide a significant computational performance. For the validation of the work, we compared the proposed implementation with a BiCGStab（2） sequential and parallelized implementation in the C and CUDA-C languages. The results showed that the proposed implementation is more efficient and can be viable for simulations being carried out with quality and in a timely manner. The gains in computational efficiency were 76x and 6x compared to the implementation in C and CUDA-C, respectively.     

基于GPU-CUDA的共轭斜量法实现及性能对比

偏微分方程数值解法(包括有限差分法、有限元法)以及大量的数学物理方程数值解法最终都会演变成求解大型线性方程组。因此,探讨快速、稳定、精确的大型线性方程组解法一直是数值计算领域不断深入研究的课题且具有特别重要的意义。在迭代法中,共轭斜量法(又称共轭梯度法)被公认为最好的方法之一。但是,该方法最大缺点是仅适用于线性方程组系数矩阵为对称正定矩阵的情况,而且常规的CPU算法实现非常耗时。为此,通过将线性方程组系数矩阵作转换成对称矩阵后实施基于GPU-CUDA的快速共轭斜量法来解决一般性大型线性方程组的求解问题。试验结果表明:在求解效率方面,基于GPU-CUDA的共轭斜量法运行效率高,当线性方程组阶数超过3000时,其加速比将超过14;在解的精确性与求解过程的稳定性方面,与高斯列主元消去法相当。基于GPU-CUDA的快速共轭斜量法是求解一般性大型线性方程组快速而非常有效的方法。     

基于CUDA的并行粒子群优化算法研究及实现

应用图形处理器(GPU)来加速粒子群优化(PSO)算法并行计算时,为突出其加速性能,经常有文献以恶化CPU端PSO算法性能为代价。为了科学比较GPU-PSO算法和CPU-PSO算法的性能,提出用"有效加速比"作为算法的性能指标。文中给出的评价方法不需要CPU和GPU端粒子数相同,将GPU并行算法与最优CPU串行算法的性能作比较,以加速收敛到目标精度为准则,在统一计算设备架构(CUDA)下对多个基准测试函数进行了数值仿真实验。结果表明,在GPU上大幅增加粒子数能够加速PSO算法收敛到目标精度,与CPU-PSO相比,获得了10倍以上的"有效加速比"。     

快速鲁棒特征算法的CUDA加速优化

提出一种基于统一计算设备架构(Compute Unified Device Architecture,CUDA)的快速鲁棒特征(Speed-up Robust Feature,SURF)图像匹配算法。分析了SURF算法的并行性,在图像处理单元(Graphics Processing Unit,GPU)的线程映射和内存模型方面对算法的构建尺度空间、特征点提取、特征点主方向的确定、特征描述子的生成及特征匹配5个步骤进行CUDA加速优化。实验表明,相比适用于CPU的SURF算法,文中提出的适用于GPU的SURF算法在处理30MB的图片时性能提高了33倍。适用于GPU的SURF算法拓展了SURF算法在遥感等领域的快速应用,尤其是大影像的快速配准。     

Accelerating geospatial analysis on GPUs using CUDA

Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis.IDW interpolation assigns geographical values to unknown spatial points using values from ...