大致的拼图片组合组合
概括
本研究介绍了近似拼图组合,这是一个用于复杂模拟的新染算法. 它可以快速,高精度的可视化自适应网格精细化 (AMR) 和非结构化的网格,而无需数据重新分区.
科学领域:
- 科学可视化科学可视化
- 高性能计算 (HPC) 是一种高性能计算.
背景情况:
- 适应性网状精细化 (AMR) 和非结构化的网状网对于高效的大规模模拟至关重要.
- 对HPC的数据分区通常会导致复杂的,非凸起的边界,阻碍了标准的可视化技术.
- 现场和后期可视化面临挑战,因为复杂的网格数据难以重新分区.
研究的目的:
- 为AMR和非结构化网格开发一种新的分布式体积染和组合算法.
- 为了实现复杂的模拟数据的快速,高精度,现场染.
- 为了克服标准染技术的局限性,这些技术需要凸和不连接的数据分区.
主要方法:
- 介绍了近似拼图组合,一个分布式体积染算法.
- 杆式基于时刻的有序独立透明度,用于可扩展的,有序独立的编译.
- 设计了一个算法,需要最小的通信和没有数据重新分区.
主要成果:
- 展示了AMR和非结构化的网格的快速和高精度的现场染.
- 在更高的核心数量下实现了可扩展的,独立于顺序的编译,在更高的核心数量下最小的开销.
- 与HPC系统上最新的排序-最后组合技术相比,经过验证的性能和图像质量.
结论:
- 大致拼图组合提供了一个可扩展,高性能和高质量的染解决方案.
- 该算法适用于大型计算流体动力学 (CFD) 模拟中的复杂数据分布.
- 消除了需要昂贵的数据重新分区的需求,促进了有效的现场和后期分析.
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