An Advanced Dynamic Visualization Method of Global Ocean Tide with Multi-core Processor Based on OpenGL

In terms of ocean tide visualization, to meet the requirement of both display range and operational efficiency, an advanced method is proposed, in which the tide height is rapidly computed with global tide model EOT10a, and dynamically displayed by OpenGL. Aiming at the large amounts of calculation of global tide height, the feature of multicore processor is integrated into the method. The experiment shows that, compared to a single-core processor, when using a 6-core processor, the speedup ratio is about 5.4, parallel efficiency reaches 90%, and 880 000 tide heights can be calculated per second. Eventually, the result would be output as a tide height graph by OpenGL. This method could be a useful tool for marine cartography due to the large display range and the high efficiency.

DRAC: a delta recurrent neural network-based arithmetic coding algorithm for edge computing

This paper develops an arithmetic coding algorithm based on delta recurrent neural network for edge computing devices called DRAC. Our algorithm is implemented on a Xilinx Zynq 7000 Soc board. We evaluate DRAC with four datasets and compare it with the state-of-the-art compressor DeepZip. The experimental results show that DRAC outperforms DeepZip and achieves 5X speedup ratio and 20X power consumption saving.

A massive images classification method based on MapReduce parallel fuzzy C-means clustering

Aiming at the low performance of classifying images under the computing model of single node. With GLCM (Gray Level Co-occurrence Matrix) which fuses gray level with texture of image, a parallel fuzzy C-means clustering method based on MapReduce is designed to classify massive images and improve the real-time performance of classification. The experimental results show that the speedup ratio of this method is more than 10% higher than that of the other two methods, moreover, the accuracy of image classification has not decreased. It shows that this method has high real-time processing efficiency in massive images classification.

Research on terrain aerodynamics analysis of power grid structure at different temperature

The distribution of near-surface meteorological elements will be greatly affected by topography and other factors, such as wind, which making the dynamic structure of the micro-topography area passed by the power transmission and transformation equipment more susceptible to micro-topography climate. The single hill is one of the typical topography in the micro-topography. This paper studies the variation of buoyant flow and temperate circulation in a typical long hilly terrain. Integrating factors such as buoyancy, turbulence and micro-topography, the separation vortex simulation technology is adopted to the special conditions of microtopography climate. The effect of different surface temperatures on the wind field of the power grid is described in detail. Experiments in the thesis show that the difference in surface temperature will directly affect the speedup ratio. Compared with the change of air temperature, when the surface temperature is higher, the acceleration effect is less obvious, but when the surface temperature is lower, the acceleration effect is more obvious.

GPU-accelerated hydraulic simulations of large-scale natural gas pipeline networks based on a two-level parallel process

The numerical simulation efficiency of large-scale natural gas pipeline network is usually unsatisfactory. In this paper, Graphics Processing Unit (GPU)-accelerated hydraulic simulations for large-scale natural gas pipeline networks are presented. First, based on the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method, presented in our previous study, a novel two-level parallel simulation process and the corresponding parallel numerical method for hydraulic simulations of natural gas pipeline networks are proposed. Then, the implementation of the two-level parallel simulation in GPU is introduced in detail. Finally, some numerical experiments are provided to test the performance of the proposed method. The results show that the proposed method has notable speedup. For five large-scale pipe networks, compared with the well-known commercial simulation software SPS, the speedup ratio of the proposed method is up to 57.57 with comparable calculation accuracy. It is more inspiring that the proposed method has strong adaptability to the large pipeline networks, the larger the pipeline network is, the larger speedup ratio of the proposed method is. The speedup ratio of the GPU method approximately linearly depends on the total discrete points of the network.

Numerical study of the aerodynamic characteristics around typical terrain with different temperature

The distributions of near-surface meteorological elements, such as wind, are greatly affected by the terrain underneath, which makes the power structure of micro geomorphic area more vulnerable to the influence of local climate. Single hills with length are one of typical terrains in microrelief. In this paper, the circulation caused by buoyant flows and temperate within typical single hilly terrain with length is studied. The Detached Eddy Simulation (DES) is used to integrate buoyancy, turbulence and micro-terrain into a single model and it is applied to the special situation of micro-terrain climate. How the wind field is influ-enced by different surface temperature and the model surface roughness is carefully described. The results show that, different surface temperature has a very strong effect on the speedup ratio. Compared with the air temperature, the lower the terrain surface temperature is, the more obvious the speedup ratio effect is, and vice versa. For different roughness surface terrain, the speedup ratio has almost the same characteristics.

A Novel Strategy for Retrieving Large Scale Scene Images Based on Emotional Feature Clustering

Due to complicated data structure, image can present rich information, and so images are applied widely at different fields. Although the image can offer a lot of convenience, handling such data consume much time and multi-dimensional space. Especially when users need to retrieve some images from larger-scale image datasets, the disadvantage is more obvious. So, in order to retrieve larger-scale image data effectively, a scene images retrieval strategy based on the MapReduce parallel programming model is proposed. The proposed strategy first, investigates how to effectively store large-scale scene images under a Hadoop cluster parallel processing architecture. Second, a distributed feature clustering algorithm MeanShift is introduced to implement the clustering process of emotional feature of scene images. Finally, several experiments are conducted to verify the effectiveness and efficiency of the proposed strategy in terms of different aspects such as retrieval accuracy, speedup ratio and efficiency and data scalability.

GPU-accelerated mesh-based Monte Carlo photon transport simulations

The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC) based methods. Compared to multi-layered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy, but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. Here we report a highly efficient MMC – MMCL – using the OpenCL heterogeneous computing framework, and demonstrate a speedup ratio up to 420× compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.

CUDA Based Parallel Computation for Gauss Elimination Method

The Central Processing Unit (CPU) parallel algorithm based on Computing Unified Device Architecture (CUDA) has shown great power of computing speedup ability. What performance will the new technique show in the field of structural computation? We choose the Gauss elimination method as the research object. In this study, the parallel Gauss elimination is realized in CUDA on GPU. Furthermore, we carry out two groups of numerical experiments. The first group investigates the effect of Matrix Bandwidths (MBs) and Node Numbers (NNs) on speedup ratio. The second one compares our method with the commercial software by analyzing two actual structural problems in ocean engineering.

Fast Fractal Image Compression by Kicking Out Similar Domain Images

Fractal algorithms are used to represent similar parts of images into mathematical transforms that can recreate the original image. This chapter presents a fast fractal image compression technique via domain kick-out method, based on averaging of domain images to discard redundant domain images. It accelerates the encoding process by reducing the size of the domain pool. Results of a simulation on the proposed speedup technique on three standard test images shows that performance of the proposed technique is far superior to the present kick out methods of fractal image compression. It has reported a speedup ratio of 31.07 in average while resulting into compression ratio and retrieved image quality comparable to Jacquin's full search method.