Paper Info
Title
Distributed merge forest: a new fast and scalable approach for topological analysis at scale
Abstract
ABSTRACTTopological analysis is used in several domains to identify and characterize important features in scientific data, and is now one of the established classes of techniques of proven practical use in scientific computing. The growth in parallelism and problem size tackled by modern simulations poses a particular challenge for these approaches. Fundamentally, the global encoding of topological features necessitates interprocess communication that limits their scaling. In this paper, we extend a new topological paradigm to the case of distributed computing, where the construction of a global merge tree is replaced by a distributed data structure, the merge forest, trading slower individual queries on the structure for faster end-to-end performance and scaling. Empirically, the queries that are most negatively affected also tend to have limited practical use. Our experimental results demonstrate the scalability of both the merge forest construction and the parallel queries needed in scientific workflows, and contrast this scalability with the two established alternatives that construct variations of a global tree.
Year
DOI
Venue
2021
10.1145/3447818.3460358
ICS
DocType
Citations 
PageRank 
Conference
1
0.36
References 
Authors
0
6
Name
Order
Citations
PageRank
Xuan Huang110.36
Pavol Klacansky2152.70
Steve Petruzza333.79
Attila Gyulassy445323.11
P.-T. Bremer522.08
Valerio Pascucci63241192.33