This system paper presents a software framework for the support of
topological analysis pipelines in a distributed-memory model. While several
recent papers introduced topology-based approaches for distributed-memory
environments, these were reporting experiments obtained with tailored,
mono-algorithm implementations. In contrast, we describe in this paper a
general-purpose, generic framework for topological analysis pipelines, i.e. a
sequence of topological algorithms interacting together, possibly on distinct
numbers of processes. Specifically, we instantiated our framework with the MPI
model, within the Topology ToolKit (TTK). While developing this framework, we
faced several algorithmic and software engineering challenges, which we
document in this paper. We provide a taxonomy for the distributed-memory
topological algorithms supported by TTK, depending on their communication needs
and provide examples of hybrid MPI+thread parallelizations. Detailed
performance analyses show that parallel efficiencies range from $20\%$ to
$80\%$ (depending on the algorithms), and that the MPI-specific preconditioning
introduced by our framework induces a negligible computation time overhead. We
illustrate the new distributed-memory capabilities of TTK with an example of
advanced analysis pipeline, combining multiple algorithms, run on the largest
publicly available dataset we have found (120 billion vertices) on a standard
cluster with 64 nodes (for a total of 1,536 cores). Finally, we provide a
roadmap for the completion of TTK's MPI extension, along with generic
recommendations for each algorithm communication category.

这篇论文介绍了一个软件框架，用于在分布式内存模型中支持拓扑分析管道。作者描述了一个通用的、泛化的用于拓扑分析管道的框架，详细介绍了面临的算法和软件工程挑战，并展示了该框架的分布式内存能力以及性能分析结果。