基于Intel SIMD指令的二维FFT优化算法

在基于频域的大数据量图像处理算法中,最为耗时的步骤就是对图像数据进行二维FFT变换的过程。论文针对这一问题,提出一种基于Intel SIMD指令的二维FFT优化算法。通过将数据按照便于SIMD指令计算的方式进行组织,利用SSE3指令加速复数乘法,在二维处理中针对处理器缓存进行优化等方法,实现了很高的性能。实验结果表明:描述的算法比目前使用最广泛的公共域FFT程序包FFTW快30%左右。达到了对大数据量图像进行快速处理的要求,具有较大的工程实用价值。     

基于异构编程模型的FFT算法实现和优化

FFT（快速傅立叶变换）是一种广泛应用于科学和工程领域的算法,现实应用中数据规模较大,需要高效实现才能满足实际应用需求。为了研究使用异构编程模型高效实现FFT算法,以华为鲲鹏处理器和昇腾AI加速芯片为实验平台,以SYCL语言为异构编程语言,实现了Cooley-Tukey基-2时域抽取FFT算法的方法和优化策略,并且提出了一种数据对切重组优化算法,大幅提高了对硬件并行能力的利用率。使用异构编程模型实现快速傅立叶变换算法可以更好地发挥异构计算设备的性能优势,易于编程且具有更高的兼容性。测试表明,在一定规模下,优化后的算法性能相比于优化前快了220.39倍。     

基于OpenCL的FFT算法研究

快速福利叶变换在图像处理领域,尤其是在图像复原算法中作为常用的计算工具,将时域计算转变为频域计算,在工程应用中有着非常重要的意义。采取多线程分块以及并行的映射方法,可以使FFT算法最大程度并行。针对OpenCL的存储层次特点和算法层次的优化,在AMD GPU平台上取得了明显的加速效果。优化后的算法性能比具有相同处理能力的CPU平台提高了7倍,比具有相同处理能力的CUDA提高了4倍。     

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。     

多核计算机上的快速傅里叶变换并行算法

针对现有多核结构上快速傅里叶变换(FFT)并行算法没有利用多级缓存和线程级并行等多核特性问题,通过运用多核多级存储特性合理划分数据,采取子序列FFT计算和多线程并行逐对计算FFT相结合的方法,给出一个N点、一维、有序和基数为2的多核多线程并行计算FFT非递归算法。理论分析和实验结果表明,该算法实用、高效,能获得较好的加速比和可扩展性。     

一种偶数基Cooley-Tukey FFT高性能实现方法

快速傅里叶变换(Fast Fourier Transform,FFT)是最重要的基础算法之一,在科学计算、信号处理、图像处理等领域都有着广泛的应用。随着这些应用领域对实时性需求的进一步提高,FFT算法面临着越来越高的性能要求。在现有的FFT算法库中,FFT算法的求解速度和计算精度受到一定程度的限制,而且也少有研究者对偶数基Cooley-Tukey FFT的高性能实现提出相应的优化策略并对技术进行深入研究。基于此,文中提出了一套针对偶数基的Cooley-Tukey FFT的优化策略和方法。首先构建一个SIMD(Single Instruction Multiple Data)友好、支持混合基的蝶形网络,然后根据偶数基旋转因子特性最大限度地降低蝶形计算的复杂度,接着通过SIMD汇编优化、汇编指令重排及选择、寄存器分配策略制定、高性能矩阵转置算法等方法来优化应用,最后实现一个高性能的FFT算法库。目前,最流行、应用最广的FFT有FFTW和Intel MKL。实验结果表明,在X86计算平台上,新提出的这套针对偶数基Cooley-Tukey FFT的技术所实现的FFT算法库的性能全面优于MKL和FFTW。所提出的这套高性能算法优化和实现技术体系,可推广到除偶数基以外的其他基的实现和优化上,为进一步的研究开发工作奠定一定的基础,进而突破FFT算法在硬件平台上的性能瓶颈,实现一套针对特定平台的高性能FFT算法库。     

基于ARMv8处理器的实数FFT实现与性能优化研究

FFT(快速傅里叶变换)是离散傅里叶变换或其逆变换的一种常见快速算法,是高性能计算领域最重要的基础核心算法之一,在科学、工程和数学等领域的应用十分广泛.实数FFT算法,即输入或者输出为实数的FFT算法,其中包括R2C(Real-to-Complex)、C2R(Complex-to-Real)等变换类型.相比复数FFT算法,实数FFT算法在图形图像处理、数据压缩等领域有着不可替代的作用.传统实数FFT实现针对的是输入规模为偶数,一般转变为复数FFT进行运算.然而当前鲜有针对输入规模为奇数的实数FFT高效实现.对此,本文提出了一种实数FFT高效算法(DRFFT),并采用蝶形网络优化、蝶形计算优化、访存优化、SIMD优化以及数据转置等方法进行优化,大幅提升了实数FFT算法性能,最终构建了一种针对实数FFT的高性能算法库.实验结果表明,本文实现的DRFFT R2C变换在单双精度浮点数处理方面较FFTW库性能分别平均提升了37.6%和4.6%,较ARMPL库性能分别平均提升了67.6%和28.1%.DRFFT C2R变换在单双精度浮点数处理方面则较FFTW库性能分别平均提升了58.6%和10.8...     

异构平台上性能自适应FFT框架

快速傅里叶变换(fast Fourier transform, FFT)在科学和工程界中具有着广泛的应用,尤其是在信号处理、图像处理以及求解偏微分方程领域.基于图形处理器(graphic processing unit, GPU)和加速处理器(accelerated processing unit, APU)的异构平台,提出了自适应性能优化的大规模并行FFT(massively parallel FFT, MPFFT)框架.MPFFT框架采用了安装时和运行时2层自适应策略.安装时借助代码产生器可以生成被GPU程序内核(kernel)调用的任意长度的代码模板库(codelet);运行时根据自动调优技术使代码产生器生成高度优化的GPU计算代码.实验结果表明：MPFFT在APU平台上,一维、二维以及三维FFT相对于AMD clAmdFft 1.6取得的平均加速比分别为3.45,15.20以及4.47,在AMD HD7970 GPU上平均加速比分别为1.75,3.01和1.69.在NVIDIA Tesla C2050 GPU上取得的整体性能都达到了CUFFT 4.1的93%,最大加速比能够达到1.28.     

大规模集群上多维FFT算法的实现与优化研究

快速傅里叶变换(fast Fourier transform,FFT)是用于计算离散傅里叶变换(discrete Fourier transform,DFT)或其逆运算的快速算法,在工程、科学和数学领域的应用非常广泛,例如信号分解、数字滤波、图像处理等。因此,在实际应用中对FFT算法进行细粒度优化是非常重要的。研究了FFT算法常用的分解策略以及FFT算法在大规模集群系统上的并行实现,并提出了相关的优化策略。在此基础上,对多种FFT算法在不同平台上进行了性能评估,并分析了各算法的实现、优缺点及其在大规模计算时的可扩展性。实验结果表明,相关研究有助于对现有的FFT算法进行进一步的优化,以及指导如何在大规模CPU+GPU的异构系统上根据不同需求选择实现性能更优的FFT算法。     

改进的基于FFT pruning的窄带高分辨率频谱算法

提出一种改进的基于FFT pruning的窄带高分辨率频谱计算方法。该方法是对Sreenivas’s FFT pruning 算法和 Nagai 的利用频移变换的FFT pruning 算法的推广。同时提出输出点分级思想,可实现任意窄带上非2的整数幂次频点输出。该算法比Sreenivas’s FFT pruning 算法具有更小的计算量和更简单的信号流图。     

A CUDA implementation of the Continuous Space Language Model

The training phase of the Continuous Space Language Model (CSLM) was implemented in the NVIDIA hardware/software architecture Compute Unified Device Architecture (CUDA). A detailed explanation of the CSLM algorithm is provided. Implementation was accomplished using a combination of CUBLAS library routines, NVIDIA NPP functions, and CUDA kernel calls on three different CUDA enabled devices of varying compute capability and a time savings over the traditional CPU approach demonstrated. The efficiency of the CUDA version of the open source implementation is analyzed and compared to that using the Intel Math Kernel Libraries (MKL) on a variety of CUDA enabled and multi-core CPU platforms. It is demonstrated that substantial performance benefit can be obtained using CUDA, even with nonoptimal code. Techniques for optimizing performance are then provided. Furthermore, an analysis is performed to determine the conditions in which the performance of CUDA exceeds that of the multi-core MKL realization.     

GPU‐accelerated finite‐difference time‐domain method for dielectric media based on CUDA

The simulation of electromagnetic (EM) waves propagation in the dielectric media is presented using Compute Unified Device Architecture (CUDA) implementation of finite‐difference time‐domain (FDTD) method on graphic processing unit (GPU). The FDTD formulation in the dielectric media is derived in detail, and GPU‐accelerated FDTD method based on CUDA programming model is described in the flowchart. The accuracy and speedup of the presented CUDA‐implemented FDTD method are validated by the numerical simulation of the EM waves propagating into the lossless and lossy dielectric media from the free space on GPU, by comparison with the original FDTD method on CPU. The comparison of the numerical results of CUDA‐implemented FDTD method on GPU and original FDTD method on CPU demonstrates that the CUDA‐implemented FDTD method on GPU can obtain better application speedup performance with reasonable accuracy. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:512–518, 2016.     

CUDA并行技术与数字图像几何变换

CUDA是GPU通过并发执行多个线程以实现大规模快速并行计算能力的技术,它能使对GPU编程变得更容易。介绍了CUDA基本特性及主要编程模型,在此基础上,提出并实现了基于NVIDIA CUDA技术的图像快速几何变换。采用位置偏移增量代替原变换算法中大量乘法运算,并把CUDA技术的快速并行计算能力应用到数字图像几何变换中,解决了基于CPU的传统图像几何变换运算效率低下的问题。实验结果证明使用CUDA技术,随着处理图像尺寸的增加,对数字图像几何变换处理效率最高能够提高到近100倍。     

CUDA架构下大规模稠密线性方程组的并行求解

在Gauss-Jordan消去法的基础上,给出了一种适应于CUDA架构的改进Gauss-Jordan消去并行算法。通过分析该方法的处理过程以及CUDA架构的相应限制,在CUDA的grid-block-thread三层组织结构的基础上,从算法构造的角度提出了grid-strip-group-block-thread五层结构,给出了基础行以及全局基础行等概念,并构建了适应于CUDA架构的Gauss-Jordan消去法的并行版本,在最高维数为4 000维的大规模稠密线性方程组的算例求解上与串行Gauss-Jordan消去法进行了比较,实验结果表明,该算法能够充分利用GPU的硬件特性,有效地降低了大规模稠密线性方程组的求解时间。     

Optimizations of a GPU accelerated heat conduction equation by a programming of CUDA Fortran from an analysis of a PTX file

The Fortran language has been commonly used for many kinds of scientific computation. In this paper, we focus on the solution of an unsteady heat conduction equation, which is one of the simplest problems for thermal dynamics. Recently, a GPU (graphics processing unit) has been enhanced with a Fortran programming language capability employing CUDA (compute unified device architecture), known as CUDA Fortran. We find that the speed performance of a system using an ordinary program coding of CUDA Fortran is lower than that of systems using a program coding of CUDA C. We also find that intermediate assembly files PTX (parallel thread execution) of the two languages are not coincident. Therefore, by comparing the PTX files from the two coding programs we could detect the bottleneck that causes the speed reduction. We propose three optimization techniques that can enable the calculated speeds using CUDA Fortran and CUDA C to be coincident. The optimizations can be performed by the Fortran language when improved by an analyzed PTX file. It is thus possible to improve the performance of CUDA Fortran by adding a correction to it, which happens to be at a programming language level.     

CUDA‐quicksort: an improved GPU‐based implementation of quicksort

Sorting is a very important task in computer science and becomes a critical operation for programs making heavy use of sorting algorithms. General‐purpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize some sorting algorithms. Two GPU‐based implementations of the quicksort were presented in literature: the GPU‐quicksort, a compute‐unified device architecture (CUDA) iterative implementation, and the CUDA dynamic parallel (CDP) quicksort, a recursive implementation provided by NVIDIA Corporation. We propose CUDA‐quicksort an iterative GPU‐based implementation of the sorting algorithm. CUDA‐quicksort has been designed starting from GPU‐quicksort. Unlike GPU‐quicksort, it uses atomic primitives to perform inter‐block communications while ensuring an optimized access to the GPU memory. Experiments performed on six sorting benchmark distributions show that CUDA‐quicksort is up to four times faster than GPU‐quicksort and up to three times faster than CDP‐quicksort. An in‐depth analysis of the performance between CUDA‐quicksort and GPU‐quicksort shows that the main improvement is related to the optimized GPU memory access rather than to the use of atomic primitives. Moreover, in order to assess the advantages of using the CUDA dynamic parallelism, we implemented a recursive version of the CUDA‐quicksort. Experimental results show that CUDA‐quicksort is faster than the CDP‐quicksort provided by NVIDIA, with better performance achieved using the iterative implementation. Copyright © 2015 John Wiley & Sons, Ltd.     

CUDA并行计算技术在情报信息研判中的应用

文章在研究公安情报信息研判技术的基础上,提出了一种基于CUDA并行计算技术的方法,实现对公安情报信息中文本信息快速分类的方法,实现将CUDA技术的快速计算能力应用到公安情报研判工作中。该文从介绍CUDA技术的概况出发,阐述了基于CUDA并行计算技术的文本分类方法,以及该方法的详细实现过程,解决了高效处理海量文本信息的问题。实验结果证明,CUDA并行计算技术在公安情报信息研判工作中卓有成效。     

Swan: A tool for porting CUDA programs to OpenCL

The use of modern, high-performance graphical processing units (GPUs) for acceleration of scientific computation has been widely reported. The majority of this work has used the CUDA programming model supported exclusively by GPUs manufactured by NVIDIA. An industry standardisation effort has recently produced the OpenCL specification for GPU programming. This offers the benefits of hardware-independence and reduced dependence on proprietary tool-chains. Here we describe a source-to-source translation tool, “Swan” for facilitating the conversion of an existing CUDA code to use the OpenCL model, as a means to aid programmers experienced with CUDA in evaluating OpenCL and alternative hardware. While the performance of equivalent OpenCL and CUDA code on fixed hardware should be comparable, we find that a real-world CUDA application ported to OpenCL exhibits an overall 50% increase in runtime, a reduction in performance attributable to the immaturity of contemporary compilers. The ported application is shown to have platform independence, running on both NVIDIA and AMD GPUs without modification. We conclude that OpenCL is a viable platform for developing portable GPU applications but that the more mature CUDA tools continue to provide best performance.

Program summary

Program title: SwanCatalogue identifier: AEIH_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIH_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: GNU Public License version 2No. of lines in distributed program, including test data, etc.: 17 736No. of bytes in distributed program, including test data, etc.: 131 177Distribution format: tar.gzProgramming language: CComputer: PCOperating system: LinuxRAM: 256 MbytesClassification: 6.5External routines: NVIDIA CUDA, OpenCLNature of problem: Graphical Processing Units (GPUs) from NVIDIA are preferentially programed with the proprietary CUDA programming toolkit. An alternative programming model promoted as an industry standard, OpenCL, provides similar capabilities to CUDA and is also supported on non-NVIDIA hardware (including multicore ×86 CPUs, AMD GPUs and IBM Cell processors). The adaptation of a program from CUDA to OpenCL is relatively straightforward but laborious. The Swan tool facilitates this conversion.Solution method:Swan performs a translation of CUDA kernel source code into an OpenCL equivalent. It also generates the C source code for entry point functions, simplifying kernel invocation from the host program. A concise host-side API abstracts the CUDA and OpenCL APIs. A program adapted to use Swan has no dependency on the CUDA compiler for the host-side program. The converted program may be built for either CUDA or OpenCL, with the selection made at compile time.Restrictions: No support for CUDA C++ featuresRunning time: Nominal     

RSA算法的CUDA高效实现技术

CUDA（Compute Unified Device Architecture）作为一种支持GPU通用计算的新型计算架构,在大规模数据并行计算方面得到了广泛的应用。RSA算法是一种计算密集型的公钥密码算法,给出了基于CUDA的RSA算法并行化高效实现技术,其关键为引入大量独立并发的Montgomery模乘线程,并给出了具体的线程组织、数据存储结构以及基于共享内存的性能优化实现技术。根据RSA算法CUDA实现方法,在某款GPU上测试了RSA算法的运算性能和吞吐率。实验结果表明,与RSA算法的通用CPU实现方式相比,CUDA实现能够实现超过40倍的性能加速。     

GPGPU编程技术初探

伴随着GPGPU计算技术的不断发展,HPC高性能计算系统体系结构正在悄然发生着一场变革,这场变革为高性能计算发展提供了一个新的方向、CUDA是NIVIDIA公司提供的利用GPGPU进行并行运算应用开发的一套C语言编程平台,通过它可以利用特定显卡的高性能运算能力进行一些大规模高性能计算,有效提升计算机系统的使用效率,本文主要介绍GPU发展现状以及如何利用CUDA编程技术进行并行运算软件开发．