对于局部化拓数据结构的任务并行方法
IEEE transactions on visualization and computer graphics
|October 31, 2023
概括
一种新的任务并行方法主动计算非结构化网格的网格连接性,提高性能. 加快集群TOPOlogical (ACTOPO) 数据结构提供了内存效率和加快速度.
科学领域:
- 计算机科学 计算机科学
- 计算几何学的计算几何学
- 数据结构 数据结构
背景情况:
- 非结构化的网格有不规则的分布数据点,使连接计算耗时且内存密集.
- 现有的动态数据结构在飞行时计算连接,这减缓了算法并影响了性能.
- 当前的数据并行模型将线程限制在相同的指令上,从而阻碍了并行处理的效率.
研究的目的:
- 引入一种新的任务并行方法,用于主动的网格连接计算.
- 开发一个新的数据结构,即加速集群TOPOlogical (ACTOPO),支持需要网格连接的算法.
- 为了提高在非结构化网格上运行的算法的性能.
主要方法:
- 实现了一个任务并行方法,其中线程同时执行不同的功能.
- 开发了加速集群TOPOlogical (ACTOPO) 数据结构,以主动管理网格连接.
- 将ACTOPO与最先进的内存效率 (TTK CompactTriangulation) 和时间效率 (TTK ExplicitTriangulation) 的结构进行了比较.
主要成果:
- ACTOPO显示了与TTK CompactTriangulation相比较的内存使用情况.
- 与TTK CompactTriangulation相比,ACTOPO的速度可以提高5倍.
- ACTOPO提供了类似于TTK ExplicitTriangulation的性能,同时只使用了一半的内存.
结论:
- 任务并行方法和ACTOPO数据结构有效地解决了非结构化网格连接的挑战.
- ACTOPO提供了一个平衡的解决方案,结合了内存效率和显著的时间性能改进.
- 这种方法有利于需要网状连接的算法,为更高效的科学计算铺平了道路.
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