Distributed merge forest
| Autor: | Valerio Pascucci, Attila Gyulassy, Steve Petruzza, Xuan Huang, Peer-Timo Bremer, Pavol Klacansky |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Scientific visualization 020207 software engineering 02 engineering and technology Construct (python library) Topology computer.software_genre 01 natural sciences Inter-process communication Tree (data structure) Distributed algorithm Encoding (memory) 0103 physical sciences Scalability 0202 electrical engineering electronic engineering information engineering 010303 astronomy & astrophysics computer Merge (linguistics) |
| Zdroj: | ICS |
| Popis: | Topological 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. |
| Databáze: | OpenAIRE |
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