盖尔:利用异质系统进行高效的非结构化网状数据分析
IEEE transactions on visualization and computer graphics
|December 5, 2025
概括
本研究介绍了GPU辅助本地化数据结构 (GALE),这是一种用于对非结构化网格的科学数据分析的新方法. 通过将网状连接计算卸载到GPU,GALE加速了可视化算法,实现了显著的加快速度.
科学领域:
- 科学数据分析科学数据分析.
- 计算机图形 计算机图形
- 高性能计算的高性能计算.
背景情况:
- 由于复杂的连接性,非结构化的网格在科学数据分析中带来了挑战.
- 网状连接计算是可视化算法的性能瓶,影响时间和内存.
- 现有的任务并行方法是CPU-bound,限制了性能增长.
研究的目的:
- 开发一种针对异质CPU-GPU系统优化的非结构化网格分析的新型任务并行方法.
- 通过将计算卸载到GPU来克服CPU-bound方法的局限性.
- 引入第一个基于CUDA的开源数据结构,用于异质任务并行性.
主要方法:
- 开发了基于CUDA的GPU辅助本地化数据结构 (GALE).
- 实现异质任务并行性,将网格连接计算卸载到GPU线程.
- 启用了CPU线程专注于执行可视化算法.
主要成果:
- 与最先进的本地化数据结构相比,GALE的速度提高了2.7倍.
- 这种方法保持了内存效率.
- 实验是在两个20核CPU和一个NVIDIA V100 GPU上进行的.
结论:
- 通过利用异构的CPU-GPU系统,GALE有效地加速了对非结构化网格的科学数据分析.
- 拟议的方法克服了CPU-bound限制,提供了显著的性能改进.
- 在科学可视化中,GALE代表了任务平行数据结构的新进展.
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