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 calcula...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.展开更多
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 proces...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。展开更多
针对大数据环境下并行深度森林算法中存在不相关及冗余特征过多、多粒度扫描不平衡、分类性能不足以及并行化效率低等问题,提出了基于互信息和融合加权的并行深度森林算法(parallel deep forest algorithm based on mutual information ...针对大数据环境下并行深度森林算法中存在不相关及冗余特征过多、多粒度扫描不平衡、分类性能不足以及并行化效率低等问题,提出了基于互信息和融合加权的并行深度森林算法(parallel deep forest algorithm based on mutual information and mixed weighting,PDF-MIMW)。首先,在特征降维阶段提出了基于互信息的特征提取策略(feature extraction strategy based on mutual information,FE-MI),结合特征重要性、交互性和冗余性度量过滤原始特征,剔除过多的不相关和冗余特征;接着,在多粒度扫描阶段提出了基于填充的改进多粒度扫描策略(improved multi-granularity scanning strategy based on padding,IMGS-P),对精简后的特征进行填充并对窗口扫描后的子序列进行随机采样,保证多粒度扫描的平衡;其次,在级联森林构建阶段提出了并行子森林构建策略(sub-forest construction strategy based on mixed weighting,SFC-MW),结合Spark框架并行构建加权子森林,提升模型的分类性能;最后,在类向量合并阶段提出基于混合粒子群算法的负载均衡策略(load balancing strategy based on hybrid particle swarm optimization algorithm,LB-HPSO),优化Spark框架中任务节点的负载分配,降低类向量合并时的等待时长,提高模型的并行化效率。实验表明,PDF-MIMW算法的分类效果更佳,同时在大数据环境下的训练效率更高。展开更多
文摘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.
文摘针对油浸式变压器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。
文摘针对大数据环境下并行深度森林算法中存在不相关及冗余特征过多、多粒度扫描不平衡、分类性能不足以及并行化效率低等问题,提出了基于互信息和融合加权的并行深度森林算法(parallel deep forest algorithm based on mutual information and mixed weighting,PDF-MIMW)。首先,在特征降维阶段提出了基于互信息的特征提取策略(feature extraction strategy based on mutual information,FE-MI),结合特征重要性、交互性和冗余性度量过滤原始特征,剔除过多的不相关和冗余特征;接着,在多粒度扫描阶段提出了基于填充的改进多粒度扫描策略(improved multi-granularity scanning strategy based on padding,IMGS-P),对精简后的特征进行填充并对窗口扫描后的子序列进行随机采样,保证多粒度扫描的平衡;其次,在级联森林构建阶段提出了并行子森林构建策略(sub-forest construction strategy based on mixed weighting,SFC-MW),结合Spark框架并行构建加权子森林,提升模型的分类性能;最后,在类向量合并阶段提出基于混合粒子群算法的负载均衡策略(load balancing strategy based on hybrid particle swarm optimization algorithm,LB-HPSO),优化Spark框架中任务节点的负载分配,降低类向量合并时的等待时长,提高模型的并行化效率。实验表明,PDF-MIMW算法的分类效果更佳,同时在大数据环境下的训练效率更高。
