Location Related Signals with Satellite Image Fusion Method Using Visual Image Integration Method

Investigations were performed on a group utilizing (General Purpose Unit) GPU and executions were evaluated for the utilization of the created parallel usages to process satellite pictures from satellite Landsat7.The usage on a realistic group gives execution change from 2 to 18 times. The nature of the considered techniques was assessed by relative dimensionless global error in synthesis (ERGAS) and Quality Without Reference (QNR) measurements. The outcomes demonstrate execution picks ups and holding of value with the bunch of GPU contrasted with the outcomes and different analysts for a CPU and single GPU. The errand of upgrading the view of a scene by combining data caught from various picture sensors is usually known as multisensor picture combination. This paper displays a territory based picture combination calculation to consolidate SAR (Synthetic Aperture Radar) and optical pictures. The co-enlistment of the two images is first led utilizing the proposed enrollment method prior to picture combination. The paper displays a parallel execution of existing picture combination techniques on a graphical group. Parallel executions of techniques in view of discrete wavelet changes are created. Division into dynamic and motionless regions is then executed on the SAR surface picture for particular injection of the SAR picture into panchromatic (PAN) picture. An integrated image in view of these two pictures is produced by the novel region based combination plot, which forces diverse combination rules for each fragmented region. At long last, this picture is melded into a multispectral(MS) picture through the half breed skillet honing technique proposed in past research. Exploratory outcomes exhibit that the proposed strategy demonstrates preferred execution over different fusion algorithms and can possibly be connected to the multisensory combination of SAR and optical pictures.     

基于GPU加速的锥束CT重建算法研究

锥束计算机断层扫描（Cone-Beam Computed Tomography，CBCT）具有采集速度快和空间分辨率高等特点，被生物医学等领域广泛关注。然而通过CPU串行处理CBCT重建中海量投影数据非常耗时，难以满足实时性的需求。GPU的发展为CBCT重建的并行加速提供了条件。根据三角函数周期性的特点对FDK算法进行了改进，并利用GPU实现了12幅投影数据同时并行计算。实验结果表明，相比于传统基于CPU的重建算法，基于GPU的CBCT重建算法在保证图像质量的前提下，将重建速度提高了超过310倍。     

GPU加速MOC输运计算性能分析研究

特征线方法（MOC）在求解堆芯规模中子输运方程时面临计算时间长的问题，加速和并行算法是目前研究的热点。基于MOC在特征线和能群层面的并行特性，采用统一计算设备构架（CUDA）编程规范，实现了基于图形处理器（GPU）的并行二维MOC算法。测试了菱形差分和步特征线法分别在双精度、混合精度及单精度浮点运算下的计算精度、效率及GPU加速效果。采用性能分析工具对GPU程序性能进行了分析，识别了程序性能瓶颈。结果表明：菱形差分和步特征线法在不同浮点运算精度下均表现出良好的计算精度；相比于CPU单线程计算，GPU加速效果在双精度和单精度情况下分别达到35倍和100倍以上。     

Multi-product continuous plant scheduling: combination of decomposition,genetic algorithm,and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.     

A three-stage graphics processing unit-based finite element analyses matrix generation strategy for unstructured meshes

With the development of parallel computing architectures, larger and more complex finite element analyses (FEA) are being performed with higher accuracy and smaller execution times. Graphics processing units (GPUs) are one of the major contributors of this computational breakthrough. This work presents a three-stage GPU-based FEA matrix generation strategy with the key idea of decoupling the computation of global matrix indices and values by use of a novel data structure referred to as the neighbor matrix. The first stage computes the neighbor matrix on the GPU based on the unstructured mesh. Using this neighbor matrix, the indices and values of the global matrix are computed separately in the second and third stages. The neighbor matrix is computed for three different element types. Two versions for performing numerical integration and assembly in the same or separate kernels are implemented and simulations are run for different mesh sizes having up to three million degrees of freedom on a single GPU. Comparison with GPU-based parallel implementation from the literature reveals speedup ranging from 4× to 6× for the proposed workload division strategy. Furthermore, the same kernel implementation is found to outperform the separate kernel implementation by 70% to 150% for different element types.     

CUDA架构下高光谱图像光谱匹配的快速实现

针对高光谱图像数据量大,光谱匹配计算耗时长的缺点,提出一种CUDA架构下的高光谱图像光谱匹配快速实现方法。首先,在分析高光谱图像特点的基础上,介绍高光谱图像光谱匹配的一般方法;其次,介绍CUDA架构及其特点,依据CUDA架构的特点,将通常的高光谱图像存储格式进行重新组织,并将参考光谱存储在共享内存中,以达到快速访问的目...     

基于Visual C＋＋的水声目标数据库设计

针对水声目标实测数据量庞大,LOFAR和DEMON分析计算量大的问题,设计了一个基于Visual C＋＋6．0的数据库管理系统。管理系统通过ADO接口实现与SQL Server 2005数据库管理程序的数据交换,保证了数据稳定存储和高效传输。通过内存映射文件技术,实现了对大数据文件的快速读取。采用CPU与GPU并行计算进行了LO-FAR和DEMON分析,实现了LOFAR和DEMON谱图的实时显示。整个数据库管理系统操作简洁高效,可靠性高,具有广泛的实际应用价值。     

碳酸盐岩储层叠前逆时偏移及GPU/CPU异构计算方案研究

针对碳酸盐岩储层地质构造的特点,提出采用基于声波方程的叠前逆时偏移算法对其进行成像,并在偏移过程中引入优化差分系数和完全匹配层(PML)边界条件来提高波场的数值模拟精度,同时利用GPU/CPU异构并行加速技术对算法进行优化,从而显著提升偏移的计算效率。模型测试表明,与传统差分系数相比,同阶的优化差分系数具有更高精度,压制频散效果明显;PML边界条件能够有效的吸收边界反射,减少边界反射对有效波场的干扰;叠前逆时偏移方法可有效识别碳酸盐岩模型中的小尺寸孔洞和裂缝,归为准确,刻画清晰,对碳酸盐岩储层成像具有显著优势;GPU/CPU异构并行加速技术可明显提高偏移的计算效率,与传统CPU算法相比,加速比可达60倍,对于实际推广应用具有重要意义。     

GPU accelerated MCMC for modeling terrorist activity

The use of graphical processing unit (GPU) parallel processing is becoming a part of mainstream statistical practice. The reliance of Bayesian statistics on Markov Chain Monte Carlo (MCMC) methods makes the applicability of parallel processing not immediately obvious. It is illustrated that there are substantial gains in improved computational time for MCMC and other methods of evaluation by computing the likelihood using GPU parallel processing. Examples use data from the Global Terrorism Database to model terrorist activity in Colombia from 2000 through 2010 and a likelihood based on the explicit convolution of two negative-binomial processes. Results show decreases in computational time by a factor of over 200. Factors influencing these improvements and guidelines for programming parallel implementations of the likelihood are discussed.     

GPU-based simulation of the long-range Potts model via parallel tempering

We discuss the efficiency of parallelization on graphical processing units (GPUs) for the simulation of the one-dimensional Potts model with long-range interactions via parallel tempering. We investigate the behavior of some thermodynamic properties, such as equilibrium energy and magnetization, critical temperatures as well as the separation between the first- and second-order regimes. By implementing multispin coding techniques and an efficient parallelization of the interaction energy computation among threads, the GPU-accelerated approach reached speedup factors of up to 37.