Emulating massively parallel computer architectures represents a very important tool for the parallel programmers. It allows them to implement and validate their algorithms. Due to the high cost of the massively paral...Emulating massively parallel computer architectures represents a very important tool for the parallel programmers. It allows them to implement and validate their algorithms. Due to the high cost of the massively parallel real machines, they remain unavailable and not popular in the parallel computing community. The goal of this paper is to present an elaborated emulator of a 2-D massively parallel re-configurable mesh computer of size n x n processing elements (PE). Basing on the object modeling method, we develop a hard kernel of a parallel virtual machine in which we translate all the physical properties of its different components. A parallel programming language and its compiler are also devel-oped to edit, compile and run programs. The developed emulator is a multi platform system. It can be installed in any sequential computer whatever may be its operating system and its processing unit technology (CPU). The size n x n of this virtual re-configurable mesh is not limited;it depends just on the performance of the sequential machine supporting the emulator.展开更多
从三维Mesh数据中分割建筑物立面以识别对象,是三维场景理解的关键,但现有方法多依赖高成本的精细标注数据。针对该问题,提出了一种半监督学习方法,引入一种基于对比学习和一致性正则化的半监督语义分割(semi-supervised semantic segme...从三维Mesh数据中分割建筑物立面以识别对象,是三维场景理解的关键,但现有方法多依赖高成本的精细标注数据。针对该问题,提出了一种半监督学习方法,引入一种基于对比学习和一致性正则化的半监督语义分割(semi-supervised semantic segmentation based on contrastive learning and consistency regularization,SS_CC)方法,用于分割三维Mesh数据的建筑物立面。在SS_CC方法中,改进后的对比学习模块利用正负样本之间的类可分性,能够更有效地利用类特征信息;提出的基于特征空间的一致性正则化损失函数,从挖掘全局特征的角度增强了对所提取建筑物立面特征的鉴别力。实验结果表明,所提出的SS_CC方法在F1分数、mIoU指标上优于当前一些主流方法,且在建筑物的墙面和窗户上的分割效果相对更好。展开更多
文摘Emulating massively parallel computer architectures represents a very important tool for the parallel programmers. It allows them to implement and validate their algorithms. Due to the high cost of the massively parallel real machines, they remain unavailable and not popular in the parallel computing community. The goal of this paper is to present an elaborated emulator of a 2-D massively parallel re-configurable mesh computer of size n x n processing elements (PE). Basing on the object modeling method, we develop a hard kernel of a parallel virtual machine in which we translate all the physical properties of its different components. A parallel programming language and its compiler are also devel-oped to edit, compile and run programs. The developed emulator is a multi platform system. It can be installed in any sequential computer whatever may be its operating system and its processing unit technology (CPU). The size n x n of this virtual re-configurable mesh is not limited;it depends just on the performance of the sequential machine supporting the emulator.
文摘从三维Mesh数据中分割建筑物立面以识别对象,是三维场景理解的关键,但现有方法多依赖高成本的精细标注数据。针对该问题,提出了一种半监督学习方法,引入一种基于对比学习和一致性正则化的半监督语义分割(semi-supervised semantic segmentation based on contrastive learning and consistency regularization,SS_CC)方法,用于分割三维Mesh数据的建筑物立面。在SS_CC方法中,改进后的对比学习模块利用正负样本之间的类可分性,能够更有效地利用类特征信息;提出的基于特征空间的一致性正则化损失函数,从挖掘全局特征的角度增强了对所提取建筑物立面特征的鉴别力。实验结果表明,所提出的SS_CC方法在F1分数、mIoU指标上优于当前一些主流方法,且在建筑物的墙面和窗户上的分割效果相对更好。
