基于GPU的并行区域场强计算

区域场强计算是电磁频谱管理领域的重要问题,提高其计算性能以适应快速变化的战场环境具有重要的意义.提出了一种基于图形处理器（Graphic Processing Units,GPU）的并行区域场强计算方法.通过合理地划分计算步骤,并且为各个计算步骤定制相应的并行方案,使得设计的并行算法适合GPU的体系架构,并行度高.实验结果表明,该文提出的并行算法可获得高达100倍的加速比,并且具有良好的可扩展性.     

对流层散射双向时间比对中对流层斜延迟实时估计

对流层斜延迟是对流层散射双向时间比对中一个重要误差源,该文提出一种对流层散射双向时间比对中对流层斜延迟实时估计方法。通过GPT2w模型计算测站气象数据,克服对流层斜延迟估计中对实时气象数据的依赖。针对Hopfield模型中固定的对流层散射顶层高,利用几何方法计算动态对流层散射顶层高,以解决对流层散射双向比对的实际应用问题。选取日本地区3个测站,两两进行比对,在验证Hopfield模型精度后,计算3组比对站在不同入射角和不同时间的对流层斜延迟。计算结果表明,对流层散射双向时间比对中对流层斜延迟呈现出随比对距离增大而增大,随入射角增大而减小的特性,并且四季变化特性也比较明显。3个比对站的对流层散射斜延迟10~35 m之间,经比对抵消90%后的时间延迟为3.5~11.8 ns。     

复杂网格模型仿射变换的GPU加速计算

为提高计算速度,复杂网格模型操作处理中的仿射变换计算被移植到具有可编程能力的GPU上实现.在并行计算中,每个线程计算一个顶点的k邻域权重值和坐标变换,多个线程同时执行.经过对线程结构安排、设备存储器分配等的优化,充分发挥GPU并行运算性能.实验结果表明,GPU加速计算对大规模网格顶点仿射变换的处理得到了较好的加速效果.     

数字对流层散射通信链路传播可靠度工程计算

本文针对具体工程应用,根据对流层散射通信特点,梳理了数字对流层散射通信有关链路估算方法,给出了系统传播可靠度直观简便的计算公式,可用于数字对流层散射通信总体论证和工程设计.     

Efficient automatic parallelization of a single GPU program for a multiple GPU system

Single GPU scaling is unable to keep pace with the soaring demand for high throughput computing. As such executing an application on multiple GPUs connected through an off-chip interconnect will become an attractive option to explore. However, much of the current code is written for a single GPU system. Porting such a code for execution on multiple GPUs is difficulty task. In particular, it requires programmer effort to determine how data is partitioned across multiple GPU cards and then launch the appropriate thread blocks that mostly accesses the data that is local to that card. Otherwise, cross-card data movement is an expensive operation. In this work we explore hardware support to efficiently parallelize a single GPU code for execution on multiple GPUs. In particular, our approach focuses on minimizing the number of remote memory accesses across the off-chip network without burdening the programmer to perform data partitioning and workload assignment. We propose a data-location aware thread block scheduler to schedule the thread blocks on the GPU that has most of its input data. The scheduler exploits well known observation that GPU workloads tend to launch a kernel multiple times iteratively to process large volumes of data. The memory accesses of the thread block across different iterations of a kernel launch exhibit correlated behavior. Our data location aware scheduler exploits this predictability to track memory access affinity of each thread block to a specific GPU card and stores this information to make scheduling decisions for future iterations. To further reduce the number of remote accesses we propose a hybrid mechanism that enables migrating or copying the pages between the memory of multiple GPUs based on their access behavior. Hence, most of the memory accesses are to the local GPU memory. Over an architecture consisting of two GPUs, our proposed schemes are able to improve the performance by 1.55× when compared to single GPU execution across widely used Rodinia [17], Parboil [18], and Graph [23] benchmarks.     

对流层对地球同步轨道SAR成像的影响研究

地球同步轨道SAR（GEO SAR）具有超长孔径时间和大覆盖范围的特性,使得传统低轨SAR成像中采用的对流层冻结模型假设失效。因此,需要考虑时变条件下的对流层效应对GEO SAR成像的影响。本文根据对流层中电磁波的传播规律,利用射线描迹法,分析了GEO SAR中相位误差的特点;并通过建立时变对流层影响下的GEO SAR回波信号模型,研究了对流层引起的GEO SAR图像偏移及图像散焦现象,并分析总结了相关影响的边界条件。最后,基于仿真数据,分析了对流层效应对GEOSAR成像的影响。      

Implementation of a High Throughput Soft MIMO Detector on GPU

Multiple-input multiple-output (MIMO) significantly increases the throughput of a communication system by employing multiple antennas at the transmitter and the receiver. To extract maximum performance from a MIMO system, a computationally intensive search based detector is needed. To meet the challenge of MIMO detection, typical suboptimal MIMO detectors are ASIC or FPGA designs. We aim to show that a MIMO detector on Graphic processor unit (GPU), a low-cost parallel programmable co-processor, can achieve high throughput and can serve as an alternative to ASIC/FPGA designs. However, careful architecture aware software design is needed to leverage the performance offered by GPU. We propose a novel soft MIMO detection algorithm, multi-pass trellis traversal (MTT), and show that we can achieve ASIC/FPGA-like performance and handle different configurations in software on GPU. The proposed design can be used to accelerate wireless physical layer simulations and to offload MIMO detection processing in wireless testbed platforms.     

基于GPU的图形电磁计算加速算法

本文利用现代图形加速卡中GPU(Graphics Process Unit)的可编程管线,实现了图形电磁计算(GRECO)方法.与原有的方法相比,在利用物理光学和物理绕射理论的基础上,计算速度提高了20倍左右.并且利用GPU实现了射线追踪算法,用于目标上多次散射的计算,使得GRECO方法可以快速计算具有凹腔结构目标的电磁散射.本方法对于目标识别和逆合成孔径成像等方面的研究具有重要的应用价值.     

Implementation of a High Throughput 3GPP Turbo Decoder on GPU

Turbo code is a computationally intensive channel code that is widely used in current and upcoming wireless standards. General-purpose graphics processor unit (GPGPU) is a programmable commodity processor that achieves high performance computation power by using many simple cores. In this paper, we present a 3GPP LTE compliant Turbo decoder accelerator that takes advantage of the processing power of GPU to offer fast Turbo decoding throughput. Several techniques are used to improve the performance of the decoder. To fully utilize the computational resources on GPU, our decoder can decode multiple codewords simultaneously, divide the workload for a single codeword across multiple cores, and pack multiple codewords to fit the single instruction multiple data (SIMD) instruction width. In addition, we use shared memory judiciously to enable hundreds of concurrent multiple threads while keeping frequently used data local to keep memory access fast. To improve efficiency of the decoder in the high SNR regime, we also present a low complexity early termination scheme based on average extrinsic LLR statistics. Finally, we examine how different workload partitioning choices affect the error correction performance and the decoder throughput.     

基于GPU的并行APSP问题的研究

Floyd-Warshall算法是图论中APSP(All-Pair Shortest Paths)问题的经典算法,为了加快计算速度,提出使用GPU通用计算来实现。文章先从算法的原理入手,层层深入,提出了可以在GPU上运行的并行F-W算法。之后,又根据矩阵分块的原理和GPU共享存储器的使用,实现了改进的GPU并行F-W算法。通过大量测试实验,得到了该GPU并行程序相对于传统CPU并行程序产生超过百倍的加速比的结论。     

微波斜径传播对流层大气湍流结构常数模型

在微波遥感和卫星通信领域内，对流层湍流大气引起的信号闪烁现象的研究是至关重要的一个问题。引起接收信号闪烁的大气折射率起伏一般由大气结构常数(Cn^2来度量。根据ITU—R光波波段的Cn^2模型及ITU—R幅度闪烁标准偏差σχ的经验模型讨论了在微波波段影响Cn^2的主要因素；建立了微波波段的Cn^2模型。其优点是考虑了大气温度和湿度随高度的变化。根据湍流的幅度闪烁理论，在频率分别为12GHz，20GHz和30GHz时，用该Cn^2模型计算了σχ，并与ITU—R和Ortgies模型的预测结果进行了比较，结果表明一致性较好，说明该Cn^2模型是实用的。     

跳频脉冲雷达目标的运动速度参数估计

文中分析了目标运动速度对跳频脉冲雷达相参合成目标一维距离像的影响,并在此基础上提出了一种以最小脉组织误差为准则的最优速度参数估计方法。仿真结果表明,该方法测速精度高,对噪声的影响具有一定的稳健性,且计算量较小,便于实时处理。     

基于GPU的Word文档密码快速破解

随着桌面系统的广泛应用,微软的OFFICE系列软件已经成为主流的文本编辑软件。Word文档的安全问题逐渐进入了人们的视野。找到一种高效的破解或恢复被加密Word文档的密码的方案具有重要的意义。密码的暴力破解是最简单也是最常用的破解方法,但是传统的基于CPU的破解方法破解速度慢,大大限制了暴力破解的应用。文中在分析Word文档加密漏洞的基础上,提出了基于GPU的CUDA并行计算模型,实现了对Word文档加密密码的快速暴力破解,实验证明文中的密码破解方案大大提高了破解速度。     

基于GPU的数字图像并行处理方法

针对像素级图像处理算法并行化程度高的特点,利用GPU的并行流处理特性和可编程性,提出了基于GPU的数字图像并行化处理方法,并对其基本执行流程和其中的关键技术问题:数据加载,结果反馈、保存等进行了详细论述.最后通过图像的卷积运算验证了GPU的并行处理能力.     

基于GPU的高性能并行计算应用

采用基于CUDA（compute unified device architecture,统一计算设备架构）的GPU（graphic pro-cessing unit,图形处理器）与CPU协作处理方法,实现了基于时差最小测量误差的任意站定位算法的实时处理。本方法的处理速度相较于单CPU平台可以提高一至两个数量级,相较于同等处理速度的多CPU平台则体现了开发周期短、费用低、工作量小和可靠性高等众多优势。     

基于GPU的MTD性能优化

为了解决传统雷达信号处理机在研发阶段面临的调试困难,计算能力受硬件限制及程序复用性差等问题,本文提出了使用GPU作为雷达计算核心的方案.在使用GPU实现雷达信号处理算法的过程中,动目标检测(MTD)部分的优化效果远低于脉冲压缩和恒虚警检测.经过分析,MTD过程中的矩阵转置与向量点乘占据了算法的大量时间.本文从GPU的数...     

基于GPU通用计算CUDA架构的人体检测技术

随着计算机硬件技术的高速发展,图形处理器（Graphic processing unit,GPU）通用计算已经发展到颇为成熟阶段,其并行运算速度已远远超过多核CPU。文章简介CUDA架构并验证其在图形处理中的加速能力,对比线性代数运算在CPU与GPU架构下的效率,将CUDA技术应用于智能视频监控人体检测系统中,实验验证其高效性及可行性。最后对CUDA的发展方向进行了展望。     

基于GPU的AES算法实现

近几年图形处理器GPU的通用计算能力发展迅速,现在已经发展成为具有巨大并行运算能力的多核处理器,而CUDA架构的推出突破了传统GPU开发方式的束缚,把GPU巨大的通用计算能力解放了出来.本文利用GPU来加速AES算法,即利用GPU作为CPU的协处理器,将AES算法在GPU上实现,以提高计算的吞吐量.最后在GPU和CPU...     

Slant Dielectric Interface Discontinuity in a Waveguide (Short Papers)

The reflection and transmission of electromagnetic waves by a slant interface between two dielectric media is investigated. By using suitable Green's functions and a geometrical optics approximation for the field on the dielectric interface, expressions for the transmitted and reflected fields are derived. The approximate results obtained in this manner are compared with the available numerical data and are shown to be fairly accurate for a number of cases of interest.     

对流层散射实现雷达信号超视距传输的研究

非均匀对流层对投射的电磁波会产生二次辐射,从而进行前向散射,形成超视距传播。文中介绍了对流层散射的传播原理、对流层的几种数学模型,对流层散射的各种损耗,以及基于对流层散射传播原理的微波超视距无源探测定位系统的探测性能分析,给出了探测距离R与工作频率f的关系曲线和探测距离R与辐射源功率P的关系曲线。