GPU-OSDDA: a bit-vector GPU-based deadlock detection algorithm for single-unit resource systems

This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm matrices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents.     

Accelerating computation of Euclidean distance map using the GPU with efficient memory access

Recent graphics processing units (GPUs), which have many processing units, can be used for general purpose parallel computation. To utilise the powerful computing ability, GPUs are widely used for general purpose processing. Since GPUs have very high memory bandwidth, the performance of GPUs greatly depends on memory access. The main contribution of this paper is to present a GPU implementation of computing Euclidean distance map (EDM) with efficient memory access. Given a two-dimensional (2D) binary image, EDM is a 2D array of the same size such that each element stores the Euclidean distance to the nearest black pixel. In the proposed GPU implementation, we have considered many programming issues of the GPU system such as coalesced access of global memory and shared memory bank conflicts, and so on. To be concrete, by transposing 2D arrays, which are temporal data stored in the global memory, with the shared memory, the main access from/to the global memory enables to be performed by coalesced access. In practice, we have implemented our parallel algorithm in the following three modern GPU systems: Tesla C1060, GTX 480 and GTX 580. The experimental results have shown that, for an input binary image with size of 9216 × 9216, our implementation can achieve a speedup factor of 54 over the sequential algorithm implementation.     

A Parallel Implementation of Multiple Secret Image Sharing Based on Cellular Automata

Recently, a multisecret sharing scheme for secret color images among a set of users was proposed, which allows that each participant to share secret color images with the rest of participants in such way that all of them can recover all secret color images only if all participants pool their shares. In this work a parallel implementation of the cellular automata-based multisecret sharing scheme is proposed, in which the technology of CUDA （Compute Unified Device Architecture） is used in parallelization, taking advantage that each cell of cellular automata can be processed independently. The processing time of the proposed scheme is analyzed and it is proved that the proposed parallel algorithm using the CUDA structure is more than 12 times faster than the conventional sequential algorithm. This reduction of temporal complexity allows the practical use of the secret sharing scheme in many information security fields.     

基于CUDA的并行全搜索运动估计算法

为了提高H.264视频编码效率,基于计算统一设备架构(CUDA)的并行全搜索运动估计算法,并利用GPU强大的计算能力和CUDA优化的存储层次结构,以加速H.264编码中的运动估计.与传统的以牺牲视频质量来提升运动估计性能的方法不同,该算法在保证视频质量的同时,结合运动估计计算密集、计算量大等特点,充分利用CUDA架构的并行性加快运动估计的速度,从而达到提高实时编码速度的目的.在GTX280实验平台上的实验结果显示,采用文中算法比优化的CPU实现可获得高达70倍的加速比.     

基于GPU的社会化网络关系计算研究

随着网络不断地社会化、普及化,网络社区的规模也越来越大,这给社会化网络关系的计算带来了巨大的计算量。这些计算包括个人关系及计算与生成、全局关系计算与生成以及关系的挖掘等。虽然这些工作的计算量很大,但却适合并行处理。基于此,本文通过详细分析GPU的高性能运算及其在CUDA编程模型上的具体实现,讨论利用基于CUDA硬件架构的GPU来进行社区用户关系的并行计算。     

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．     

使用GPU加速BLAST算法初探

应用GPU通用高性能编程技术实现了一种加速BLAST算法的新方法。BLAST是目前最常用的用于生物序列查询比对的算法和软件包,其处理速度受到串行化执行和磁盘I/O等因素的影响。本文通过实验分析了BLAST软件包中的典型程序BLASTN的运行热点,并选定关键热点模块,应用CUDA编程技术对其进行并行化改造。对比实验结果表明,对于平均序列长度较大的序列库,应用GPGPU并行化可明显缩短该模块的运行时间,获得超过35倍的加速比。这说明,我们可以利用GPGPU对BLAST进行并行化加速,以满足高性能生物序列查询的需求。     

CUDA并行程序的内存访问优化技术研究

对统一计算设备架构CUDA技术进行了研究,分析了CUDA体系结构及其内存访问机制的显著特点,总结了CUDA并行程序常见的内存访问问题,针对全局内存的非对齐访问和共享内存的访问冲突,提出了相应的内存访问优化策略;最后,利用直方图均衡算法对此优化技术进行了测试,对比了优化前后的程序执行时间;实验结果表明,利用此优化技术可以大大缩短CUDA程序的执行时间,并且图像像素越大,优化效果越好。