The Implementation of Ray Tracing Algorithm with OpenMP Parallelization

Ray tracing is a computer graphics method that renders images realistically. As the name suggests, this technique primarily traces the path of light rays interacting with objects in a scene [1], permitting the calculation of lighting and reflecting impact [2]. As ray tracing is a time-consuming process, the need for parallelization to solve this problem arises. One downside of this solution is the existence of race conditions. In this work, we explore and experiment with a different, well-known solution for this race condition. Starting with the introduction and the background section, a brief overview of the topic is followed by a detailed part of how the race conditions may occur in the case of the ray tracing algorithm. Continuing with the methods and results section, we have used OpenMP to parallelize the Ray tracing algorithm with the different compiler directives critical, atomic, and first-private. Hence, it concluded that both critical and atomic are not efficient solutions to produce a good-quality picture, but first-private succeeded in producing a high-quality picture.

电动拖拉机双电源并联控制研究

电动拖拉机具有结构简单和节能减排等优点,但现有电动拖拉机作业时采用单一动力电池能源,存在续航时间短的问题。为了提高电动拖拉机长续航供电能力,提出了一种基于下垂控制的直流双电源并联均流联合供电方案,利用MatLab/Simulink搭建了系统模型。仿真试验表明:单电源可以实现升压稳压控制;基于下垂控制的双电源并联均流能够实现双电源的电流均流;当切换不同功率负载时,系统电压随功率增加按比例下调,验证了该方案的可行性。

Parallelization of intra prediction algorithm based on array processor

For the characteristics of intra prediction algorithms, the data dependence and parallelism between intra prediction models are first analyzed. This paper proposes a parallelization method based on dynamic reconfigurable array processors provided by the project team, and uses data level parallel(DLP) algorithms in multi-core units. The experimental results show that Y-component of peak signal to noise ratio(Y-PSNR) is improved about 10 dB and the time is saved 63% compared with high-efficiency video coding(HEVC) test model HM10.0. This method can effectively reduce codec time of the video and reduce computational complexity.

Parallelization and performance tuning of molecular dynamics code with OpenMP

An OpenMP approach was proposed to parallelize the sequential molecular dynamics(MD) code on shared memory machines. When a code is converted from the sequential form to the parallel form, data dependence is a main problem. A traditional sequential molecular dynamics code is anatomized to find the data dependence segments in it, and the two different methods, i.e., recover method and backward mapping method were used to eliminate those data dependencies in order to realize the parallelization of this sequential MD code. The performance of the parallelized MD code was analyzed by using some performance analysis tools. The results of the test show that the computing size of this code increases sharply form 1 million atoms before parallelization to 20 million atoms after parallelization, and the wall clock during computing is reduced largely. Some hot-spots in this code are found and optimized by improved algorithm. The efficiency of parallel computing is 30% higher than that of before, and the calculation time is saved and larger scale calculation problems are solved.

Space decomposition based parallelization solutions for the combined finiteediscrete element method in 2D

The combined finiteediscrete element method （FDEM） belongs to a family of methods of computationalmechanics of discontinua. The method is suitable for problems of discontinua, where particles aredeformable and can fracture or fragment. The applications of FDEM have spread over a number of disciplinesincluding rock mechanics, where problems like mining, mineral processing or rock blasting canbe solved by employing FDEM. In this work, a novel approach for the parallelization of two-dimensional（2D） FDEM aiming at clusters and desktop computers is developed. Dynamic domain decompositionbased parallelization solvers covering all aspects of FDEM have been developed. These have beenimplemented into the open source Y2D software package and have been tested on a PC cluster. Theoverall performance and scalability of the parallel code have been studied using numerical examples. Theresults obtained confirm the suitability of the parallel implementation for solving large scale problems. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

An Approach to Parallelization of SIFT Algorithm on GPUs for Real-Time Applications

Scale Invariant Feature Transform (SIFT) algorithm is a widely used computer vision algorithm that detects and extracts local feature descriptors from images. SIFT is computationally intensive, making it infeasible for single threaded im-plementation to extract local feature descriptors for high-resolution images in real time. In this paper, an approach to parallelization of the SIFT algorithm is demonstrated using NVIDIA’s Graphics Processing Unit (GPU). The parallel-ization design for SIFT on GPUs is divided into two stages, a) Algorithm de-sign-generic design strategies which focuses on data and b) Implementation de-sign-architecture specific design strategies which focuses on optimally using GPU resources for maximum occupancy. Increasing memory latency hiding, eliminating branches and data blocking achieve a significant decrease in aver-age computational time. Furthermore, it is observed via Paraver tools that our approach to parallelization while optimizing for maximum occupancy allows GPU to execute memory bound SIFT algorithm at optimal levels.

A simplified hardware-friendly contour prediction algorithm in 3D-HEVC and parallelization design

After the extension of depth modeling mode 4(DMM-4)in 3D high efficiency video coding(3D-HEVC),the computational complexity increases sharply,which causes the real-time performance of video coding to be impacted.To reduce the computational complexity of DMM-4,a simplified hardware-friendly contour prediction algorithm is proposed in this paper.Based on the similarity between texture and depth map,the proposed algorithm directly codes depth blocks to calculate edge regions to reduce the number of reference blocks.Through the verification of the test sequence on HTM16.1,the proposed algorithm coding time is reduced by 9.42%compared with the original algorithm.To avoid the time consuming of serial coding on HTM,a parallelization design of the proposed algorithm based on reconfigurable array processor(DPR-CODEC)is proposed.The parallelization design reduces the storage access time,configuration time and saves the storage cost.Verified with the Xilinx Virtex 6 FPGA,experimental results show that parallelization design is capable of processing HD 1080p at a speed above 30 frames per second.Compared with the related work,the scheme reduces the LUTs by 42.3%,the REG by 85.5%and the hardware resources by 66.7%.The data loading speedup ratio of parallel scheme can reach 3.4539.On average,the different sized templates serial/parallel speedup ratio of encoding time can reach 2.446.

Parallelization of a Branch and Bound Algorithm on Multicore Systems

The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In this paper, we present an improved sequential algorithm which is based on a strict alternation of Generation and Exploration execution modes as well as Depth-First/Best-First hybrid strategies. The experimental results show that the proposed scheme exhibits improved performance compared with the algorithm in [1]. More importantly, our method can be easily extended and implemented with lightweight threads to speed up the execution times. Good speedups can be obtained on shared-memory multicore systems.

ANALYSIS OF MULTIGRID PARALLELIZATION ON MESSAGE PASSING COMPUTERS

This paper studies the;complexity of multighd mpllelization on message PaSsing computers. Parallelization is by domain decomposition. An optimal strip decomposition is proposed. With natural ordering of the grid points,the strip decomposition leads to good processor utilization. The efficiency could be significantly improved. Better performances could be achieved by making use of Van der Vorst ordering.

Parallelization and I/O Performance Optimization of a Global Nonhydrostatic Dynamical Core Using MPI

The Global-Regional Integrated forecast System(GRIST)is the next-generation weather and climate integrated model dynamic framework developed by Chinese Academy of Meteorological Sciences.In this paper,we present several changes made to the global nonhydrostatic dynamical(GND)core,which is part of the ongoing prototype of GRIST.The changes leveraging MPI and PnetCDF techniques were targeted at the parallelization and performance optimization to the original serial GND core.Meanwhile,some sophisticated data structures and interfaces were designed to adjust flexibly the size of boundary and halo domains according to the variable accuracy in parallel context.In addition,the I/O performance of PnetCDF decreases as the number of MPI processes increases in our experimental environment.Especially when the number exceeds 6000,it caused system-wide outages(SWO).Thus,a grouping solution was proposed to overcome that issue.Several experiments were carried out on the supercomputing platform based on Intel x86 CPUs in the National Supercomputing Center in Wuxi.The results demonstrated that the parallel GND core based on grouping solution achieves good strong scalability and improves the performance significantly,as well as avoiding the SWOs.

Implementation of OpenMP Parallelization of Rate-Dependent Ceramic Peridynamic Model

A rate-dependent peridynamic ceramic model,considering the brittle tensile response,compressive plastic softening and strain-rate dependence,can accurately represent the dynamic response and crack propagation of ceramic materials.However,it also considers the strain-rate dependence and damage accumulation caused by compressive plastic softening during the compression stage,requiring more computational resources for the bond force evaluation and damage evolution.Herein,the OpenMP parallel optimization of the rate-dependent peridynamic ceramicmodel is investigated.Also,themodules that compute the interactions betweenmaterial points and update damage index are vectorized and parallelized.Moreover,the numerical examples are carried out to simulate the dynamic response and fracture of the ceramic plate under normal impact.Furthermore,the speed-up ratio and computational efficiency by multi-threads are evaluated and discussed to demonstrate the reliability of parallelized programs.The results reveal that the totalwall clock time has been significantly reduced after optimization,showing the promise of parallelization process in terms of accuracy and stability.

Wedge template optimization and parallelization of depth map in intra-frame prediction algorithms

To reduce the computational complexity and storage cost caused by wedge segmentation algorithm,a scheme of simplifying wedge matching is proposed.It takes advantage of the correlation of the wedge separation line of depth map and the direction of intra-prediction for 3D high-efficiency video coding(3D-HEVC).According to the difference of wedge segmentation between adjacent edge and opposite edge,a set only including 104×4 wedgelet templates is given.By expanding of the wedge wave of a certain minimum unit,a simple separation line acquisition method for different size of depth block is put forward.Furthermore,based on the array processor(DPR-CODEC)developed by project team,an efficient parallel scheme of the improved wedge segmentation mode prediction is introduced.By the scheme,prediction unit(PU)size can be changed randomly from 4×4 to 8×8,16×16,and 32×32,which is more in line with the needs of the HEVC standard.Veri-fied with test sequence in HTM16.1 and the Xilinx virtex-6 field programmable gate array(FPGA)respectively,the experiment results show that the proposed methods save 99.2%of the storage space and 63.94%of the encoding time,the serial/parallel acceleration ratio of each template reaches 1.84 in average.The coding performance,storage and resource consumption are considered for both.

Comparative Study of the Parallelization of the Smith-Waterman Algorithm on OpenMP and Cuda C

In this paper, we present parallel programming approaches to calculate the values of the cells in matrix’s scoring used in the Smith-Waterman’s algorithm for sequence alignment. This algorithm, well known in bioinformatics for its applications, is unfortunately time-consuming on a serial computer. We use formulation based on anti-diagonals structure of data. This representation focuses on parallelizable parts of the algorithm without changing the initial formulation of the algorithm. Approaching data in that way give us a formulation more flexible. To examine this approach, we encode it in OpenMP and Cuda C. The performance obtained shows the interest of our paper.

A Parallelization Research for FY Satellite Rainfall Estimate Day Knock off Product Algorithm

With the development of satellite remote sensing technology, more and more requirements are put forward on the timeliness and stability of the satellite weather service system. The FY satellite rainfall estimate day knock off product algorithm runs longer, about 20 minutes, which affects the estimated rainfall product generated timeliness. Research and development of parallel optimization algorithms based on the needs of satellite meteorological services and their effectiveness in practical applications are necessary ways to enhance the high-performance and high-availability capabilities of satellite meteorological services. So aiming at this problem, we started the parallel algorithm research based on the analysis of precipitation estimation algorithm. Firstly, we explained the steps of precipitation estimated date knock off product algorithm;secondly, we analyzed the four main calculation module calculating the amount of algorithms;thirdly, multithreaded parallel algorithm and MPI parallelization was designed. Finally, the multithreaded parallel and MPI parallelization were realized. Experimental results show that the multithreaded parallel and MPI parallelization algorithm could greatly improve the overall degree of computational efficiency. And, MPI parallelization mode has a higher operating efficiency. The performance of parallel processing is closely related to the architecture of the computer. From the perspective of service scheduling and product algorithms, the MPI parallelization approach is adopted to achieve the purpose of improving service quality.

Parallelization of Diagnostics for Climate Model Development

The parallelization of the diagnostics for climate research has been an important goal in the performance testing and improvement of the diagnostics for the Department of Energy’s (DOE’s) Accelerated Climate Modeling for Energy (ACME) project [1]. The primary mission of the ACME project is to build and test the next-generation Earth system model for current and future generations of computing systems operated by the DOE office of science computing facilities, including the envisioned exascale systems foreseen in the early part of the next decade. As part of the underpinning workflow environment, a diagnostics, model metrics, and intercomparison Python framework, called UVC Metrics was created to aid in testing and production execution of the model. This framework builds on common methods and similar metrics to accommodate and diagnose individual component models, such as atmosphere, land, ocean, sea ice, and land ice. This paper reports on initial parallelization of UVC Metrics for the atmosphere model component using two popular frameworks: MPI and SPARK. A timing study is presented to assess the performance of each method in which significant improvement was achieved for both frameworks despite I/O contentions with NFS. The advantages and disadvantages of each framework are also presented.

基于平行测试的认知自动驾驶智能架构研究

在大数据、云计算和机器学习等新一代人工智能技术的推动下,自动驾驶的感知智能在近年来得到显著的提升与发展.然而,与人类驾驶过程中隐含的以自我目的实现为引导的自探索性和自主性相比,现阶段自动驾驶技术主要以辅助驾驶功能为主,还停留在以被动感知、规划与控制为主的初级智能自动驾驶阶段.为实现车辆智能从数据驱动的环境感知、辅助决策、被动规划到知识驱动的场景认知、推理决策、主动规划的提升,亟需增强车辆自身对复杂外界信息归纳提炼、推理决策、评价估计等类人能力.首先回顾自动驾驶关键技术演化及其应用发展历程;随后分析测试对车辆智能评估的效用;然后基于平行测试理论,提出自动驾驶车辆认知智能训练、测试与评估空间的构建方法,并设计基于平行测试的认知自动驾驶智能训练框架.该项研究工作预期能为推动自动驾驶从感知智能向认知智能的升级提供可行的技术支撑与实现路径.

改进注意力机制嵌入PR-Net模型的水稻病害识别仿真

针对现有的CNN模型在水稻叶部病害的识别中准确率较低的问题,提出了一种结合并行结构和残差结构的混合卷积神经网络模型PRC-Net(parallel residual with coordinate attention network)。引入并行结构,提高卷积的感受野;结合残差结构,使特征信息完整的连续传递;在骨干模型PR-Net中嵌入改进的空间注意力机制,增强对不同尺度病斑特征信息的凝聚程度;为进一步提升病害识别的准确率,并减少模型的训练时间和推理时间,通过改变加权方式对模型结构进行优化。仿真结果表明:与InceptionResNetV2等分类模型相比,PRC-Net具有更少的训练参数、更短的训练时间和更高的识别精度,性能优于其他作物病害识别模型。

平行智能与平行教育:对教育教学的审视与展望

近年来,在人工智能技术的强力推动下,一场教育模式的重大变革——智能教育支撑的平行教育变革已开始在全球范围内蔓延开来,这就需要从平行智能与平行教育的角度重新审视教育教学。基于此,文章首先探讨了教育的本源与目的,以作为梳理平行智能与平行教育关系的理论基础;接着介绍了平行智能与平行教育的定义、基本框架及其应用发展过程,指出平行智能为平行教育提供了有力的智能技术支撑;之后重点阐释了智能教育支撑的平行教育变革,认为这场变革不仅包含教学内容和内涵、学科结构和考核方式的变革,还涉及教育形态的创新;最后展望未来,强调要引入平行师生、平行学校的理念与相关技术来保障平行教育变革的成功,以此开创教育变革新方向,谋求可持续的人类福祉。

基于混合分解和PCG-BiLSTM的风速短期预测

为降低风速的随机性对风力发电的影响,提高风速短期预测的精准度,提出一种基于混合分解、双通道输入、多分支PCG-BiLSTM深度学习模型的短期风速预测方法。首先,将全年风速数据分为春、夏、秋、冬4个季度,选取春季作为主要实验对象;其次,利用奇异谱分解(SSD)和变分模态分解(VMD)以降低原始春季风速数据复杂度,生成具有不同模态且复杂度低的子分量,两种不同模式子分量组合为混合分量,实现不同模式分解算法的优势互补;最后,将混合分量以双通道的形式输入到多分支PCG-BiLSTM深度学习模型中,其模型的每个分支由卷积神经网络(CNN)与门控循环单元(GRU)并联组成时空特征提取模块,用于提取两种分解分量组合的混合分量的时空特征,各分支提取对应混合分量的时空特征经聚合后再由双向长短期记忆网络(BiLSTM)进一步提取风速信号的正向和反向双向波动规律,进而得到最终的风速预测结果。多组实验结果表明:提出的组合预测方法在短期风速预测中具有较高的精度和泛化能力,优于其他传统预测方法。

基于混合有限元法的油浸式变压器稳态流-热耦合场并行计算方法

针对油浸式变压器2维流-热耦合仿真计算效率低的问题,提出了基于混合有限元法的并行计算方法。首先,在Visual Studio 2019中采用C++语言实现无量纲最小二乘有限元法以及迎风有限元法的串行计算方法。然后,基于图形处理器(graphic processing unit,GPU)实现流体场的并行计算,针对单分区分匝模型对比分析了不同GPU卡在不同网格条件下的并行计算效率,分析结果表明数据规模越大,GPU卡流处理器越多并行效果越好。其次,基于Intel MKL(Intel math kernel library)函数库结合共享存储并行编程(open multi-processing,OpenMP)实现了2维温度场的并行计算,并对比分析了不同网格数量对并行效率的影响。最后,在此基础上提出了根据不同仿真条件的混合并行计算方法,并应用到大型油浸式变压器绕组模型的2维温升热点分析中。结果表明,相较于串行程序,混合有限元并行计算方法的加速比达到了69.5,实验测试结果进一步验证了并行计算结果的准确性,研究成果为大型油浸式变压器流-热耦合问题的快速计算奠定了基础。