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.

Parallel Image Processing: Taking Grayscale Conversion Using OpenMP as an Example

In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.

基于半精度浮点数优化与OpenMP的地震数值模拟

研究地震波场的传播特征是地震勘探的基础,利用有限差分法求解波动方程进行地震正演,其优势在于占用内存低、计算速度快、易于实现。但在三维情况下,计算量和数据量会急剧增加,传统串行地震数值模拟将无法满足计算效率的需求。因此,本文提出一种基于半精度浮点数优化与OpenMP的三维波动方程地震数值模拟方法,该方法首先利用半精度浮点数对地震常用的浮点型数据进行优化;其次利用应用程序接口OpenMP在多核CPU下通过以分割波场计算区域的方式实现并行计算;在保证计算结果满足精度需求的同时,能有效提高三维地震数值模拟的计算效率,并减少近一半的内存需求。通过数值试验证明该方法的有效性和实用性。

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.

基于OpenMP的航迹融合并行优化方法

针对组网雷达中分布式数据融合计算量随数据量增大急剧增加的问题及航迹融合中最耗时的航迹关联环节,提出一种基于OpenMP的多线程优化方法。通过OpenMP并行计算雷达航迹间的关联距离,提升内部算法运算速度,并将数据接收、融合处理和结果输出过程进行多线程处理,进一步加快外部数据交互速度,提升融合处理整体时间性能。以大容量目标场景为测试用例,评估处理时间和优化加速比。仿真结果表明,所提并行优化方法能够有效提升运算速度。

基于OpenMP的堆芯中子学软件性能优化研究

CORCA-3D软件是中国核动力研究设计院自主研发的先进节块法堆芯三维少群中子学计算软件,提升CORCA-3D软件运行速度可以提高反应堆系统分析的效率。目前CORCA-3D软件采用单线程的方式运行,并没有充分利用计算机的多核硬件资源,对CORCA-3D软件进行性能分析,发现其存在运行时间较长的热点函数,CPU利用率较低,因此可引入并行编程技术来加速CORCA-3D软件的计算。文中将OpenMP编程技术运用到CORCA-3D软件中,并介绍了CORCA-3D软件并行优化的设计与实现。通过对方家山1号机组全堆芯进行测试,证明并行编程技术可大幅提升CORCA-3D软件的运行效率,平均加速比约为2左右。此并行编程技术的运用为后续堆芯数值软件应用提供技术支撑。

Parallel Quick Search Algorithm for the Exact String Matching Problem Using OpenMP

String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.

Parallel Technologies with Image Processing Using Inverse Filter

Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.

一种利用并行复算实现的OpenMP容错机制

基于并行复算的故障恢复技术,将故障恢复的计算任务分配至未发生故障的结点上并行执行,从而显著缩短复算时间,有效降低故障恢复开销,提高并行程序容错性能.基于该故障恢复技术,提出了一种针对OpenMP并行程序的容错机制PR-OMP,有效解决了分段复算、复算负载重分布等问题;此外,还扩展了传统编译数据流分析技术,提出了针对OpenMP并行程序的数据流分析技术,并基于该技术计算状态保存开销进行优化.设计实现了用于支持PR-OMP的编译工具GiFT-OMP,并通过实验证明了PR-OMP机制及其支持工具的有效性,评估并分析了其性能和可扩展性.

OpenMP并行计算在卫星重力数据处理中的应用

对新一代卫星重力数据处理中的计算密集型任务进行分析,总结出需要采用并行计算技术提高效率的几个关键任务。对不同的并行手段进行比较,采用OpenMP并行方法,并通过算例验证并行设计方法的有效性。结果表明,并行计算能显著提高卫星重力任务的数据处理效率。

MKL和OpenMP多核并行算法解算高阶地球重力场的效率分析

将OpenMP并行运算库和Intel Math Kernel Library10.2科学计算库运用到高阶地球重力场反演,显著提高了计算效率。模拟结果表明:1)在以单历元为解算单元形成子法方程系数矩阵时,OpenMP可下三角压缩存储,其内存销耗和时间销耗均比较小;2)当利用多个历元组成高维系数矩阵,然后再形成法方程时,MKL算法才能体现出高效性能;3)MKL求逆算法效率远高于OpenMP算法;4)综合利用OpenMP和MKL算法的优势,可显著提高高阶地球重力场反演的效率。

OpenMP在水动力数学模型并行计算中的应用

采用PGI Fortran7.1-2(Portland Group)的OpenMP技术对二维水动力数学模型进行了并行优化试验。并行后,数学模型运行时间明显减少,在对同一算例的水流计算模拟中,串行和并行所需运行时间分别为5 336.781 s和3 454.296 s,平均加速比为1.56,平均并行计算节省时间36%,明显提高了水动力学数学模型的运算速度。

OpenMP在MM5中尺度模式中的应用试验

简要介绍了一种新的并行编程标准——ＯｐｅｎＭＰ，通过与其他几种并行编程标准的比较，了解其优越与不足之处。详细介绍了 ＯｐｅｎＭＰ在 ＰＳＵ／ ＮＣＡＲ的中尺度模式ＭＭ５中的运用。分别用 ＯｐｅｎＭＰ并行方式与 ＭＰＩ并行方式在 ＳＧＩ Ｏｒｉｇｉｎ ２０００上进行了并行效能测试。测试结果表明： ＯｐｅｎＭＰ是一种简单、高效的并行编程标准，非常适合于气象科研与业务领域应用。

一种基于动态并行区的OpenMP程序开发模式

通常,OpenMP程序开发将开发过程、程序正确性检测和性能分析分离开来.为此,提出动态并行区的概念,并在此基础上提出一种新的OpenMP程序开发模式,将OpenMP程序的开发过程、正确性检测和性能分析紧密地联系起来.在OpenMP程序开发的每一阶段,都能确保程序的正确性;同时,通过精确的性能分析与细微的性能调整,使得OpenMP程序的性能随开发的不断深入而逐步得到改进.据此开发的NPB2.3OpenMP Fortran版的实测结果显示出该模式的可行性.

基于OpenMP的压缩感知并行处理算法

针对压缩感知重建算法复杂度高、运行时间长等缺点,提出一种应用于多核处理器的压缩感知并行算法。在认真分析压缩感知算法的基础上,利用OpenMP对压缩感知的编码测量和正交匹配追踪(OMP)算法进行并行处理,提升程序的性能。实验结果表明,随着线程数的增加,程序的执行效率显著提高,加速比呈线性增长;并且重构过程越复杂,其性能优化越明显。

基于MPI+OpenMP的多层次并行偏移算法研究

根据PC集群的系统特性和叠前深度偏移的理论特点,提出了多层次并行单平方根和双平方根叠前深度偏移算法。从波动方程叠前深度偏移的概念和特征入手,针对单平方根和双平方根波动方程的实现特点,利用PC集群各节点内的每个内核,构建两类多层次并行叠前深度偏移算法。理论模型验证了其正确性,得到了较好的成像效果,不同类型的多层次并行偏移算法计算结果有所差异。多层次并行偏移算法是在实现MPI并行偏移的基础上,对集群各节点内核的充分利用,有利于提升PC集群的计算性能,为叠前深度偏移的实际推广奠定基础。

基于OpenMP的快速并行分层算法

为了充分利用现有的多核CPU计算资源,提出一种基于OpenMP框架的快速并行分层算法,并对其性能进行讨论.该算法利用模型自然分组特征建立拓扑关系分组,使得模型拓扑数据结构的建立时间缩短;在此基础上,采用基于OpenMP的多线程计算,将拓扑结构的建立过程和求取层片轮廓的过程并行化计算,可以取得接近CPU核数的加速比,因此分层时间明显降低.对于复杂三维模型的超大STL文件进行分层处理,实例计算表明文中算法是一种高效且易于实现的方法.

基于OpenMP的3维粒子模拟并行计算

基于OpenMP标准分别设计了粒子模拟方法中电磁场计算、粒子运动求解、电荷密度和电流密度更新的并行计算实现算法。在多核计算机上对所设计并行算法进行了性能测试和分析,根据分析结果在3维并行粒子模拟软件CHIPIC3D上实现了基于OpenMP的并行计算功能,并应用其对一种扩展互作用振荡器进行了基于OpenMP的并行模拟和基于OpenMP/MPI混合模式的并行模拟。模拟结果表明并行算法正确并能取得较高的加速比。

基于OPENMP求解旅行商问题的并行蚁群算法

蚁群算法具有自组织性、正反馈性、较强的鲁棒性和天生的并行性等优点,不足之处是需要较长的搜索时间.为了解决其搜索时间过长的问题,文中提出了一种并行实现策略,用OPENMP应用编程接口采用C++语言编程实现,能明显减少搜索所用的时间.给出了一种解决旅行商问题(TSP)的并行蚁群算法,并通过串并算法执行时间的比较,说明并行算法的优越性.

基于OpenMP的压缩感知多描述并行处理算法

针对压缩感知算法重建时间长、图像重建质量不高等不足,在认真分析压缩感知算法的基础上,提出一种压缩感知多描述并行算法。为了提高系统运行速度和重建图像质量,将经过稀疏变换后的系数进行交织抽取,分成多个子图像,再利用OpenMP将子图像分配到各线程中并行实现分块压缩感知。实验结果表明,随着抽取数的增加,图像的重建质量呈上升趋势,在32-抽取时图像的重建质量比单抽取的高出了7.24 dB;随着线程数的增加,程序的执行效率不断提高,最高可达单线程的7.35倍,有效地解决了压缩感知算法重建时间长以及图像重建质量差的问题。