Parallel Mapper
This work addresses the scalability problem for researchers and practitioners using Mapper in topological data analysis, though it is incremental as it focuses on parallelizing an existing method.
The paper tackles the computational bottleneck of constructing Mapper, a topological data analysis tool, by developing a provably correct parallel algorithm that runs on multiple processors, demonstrating efficiency through performance experiments compared to a sequential implementation.
The construction of Mapper has emerged in the last decade as a powerful and effective topological data analysis tool that approximates and generalizes other topological summaries, such as the Reeb graph, the contour tree, split, and joint trees. In this paper, we study the parallel analysis of the construction of Mapper. We give a provably correct parallel algorithm to execute Mapper on multiple processors and discuss the performance results that compare our approach to a reference sequential Mapper implementation. We report the performance experiments that demonstrate the efficiency of our method.