TTK正在为MPI做好准备
IEEE transactions on visualization and computer graphics
|April 17, 2024
概括
拓工具包 (TTK) 现在支持使用消息传递接口 (MPI) 的分布式内存并行性,使大数据集上的多功能拓分析管道成为可能. 这种扩展为复杂的科学工作流提供了高效的并行化.
科学领域:
- 科学可视化科学可视化
- 计算拓学的计算拓学
- 高性能计算 高性能计算
背景情况:
- 对于分布式内存环境的现有基于拓的方法通常使用定制的单个算法实现.
- 需要多功能框架,并行支持复杂的拓分析管道.
研究的目的:
- 用消息传递接口 (MPI) 将拓工具包 (TTK) 扩展到分布式内存并行性的技术基础.
- 为拓分析管道引入一种多功能方法,支持三角化域和正规网格.
主要方法:
- 扩展TTK的数据结构,用于三角形表示和横跨,以支持MPI.
- 在全球管道和细粒度算法层面开发TTK和MPI之间的中间接口.
- 基于通信需求的分布式内存拓算法的分类以及混合MPI+线程并行化的例子.
主要成果:
- 对分布式内存拓分析管道的多功能方法的演示.
- 性能分析显示,从20%到80%的平行效率与MPI特定预先调节的微不足道的开销.
- 成功地将扩展的TTK应用于1536核心集群上的1200亿顶点数据集.
结论:
- TTK 扩展为分布式内存环境中的大规模拓分析提供了一个强大的和多功能框架.
- 开发的方法解决了平行拓计算中的算法和软件工程挑战.
- 为未来开发TTK的MPI扩展提供了路线图和建议.
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