一种高阶平滑表面并行提取方法

摘要

Considering the problems ( complicated computation and huge data ) of SEBVHOS ( surface extraction from binary volumes with high-order smoothness) , we propose a parallel algorithm to accelerate the SEBVHOS execution. Firstly, SEBVHOS is parallelized. Secondly, optimization techniques are applied to improve performance of the algorithm. Meanwhile, matrix compression is applied to improve performance of memory space. Experiments show that the average speed-up ratio achieves 1. 87 in a dual-core system.%针对高阶平滑表面算法计算复杂和数据量大的问题,提出一种加快高阶平滑表面算法速度的并行方法.首先对高阶平滑表面算法进行并行化,然后采用优化技术提高算法性能,同时采用矩阵压缩改善内存空间性能.实验表明,在双核处理器上平均加速比达到1.87.