--- res: bibo_abstract: - Persistent homology is a popular and powerful tool for capturing topological features of data. Advances in algorithms for computing persistent homology have reduced the computation time drastically – as long as the algorithm does not exhaust the available memory. Following up on a recently presented parallel method for persistence computation on shared memory systems [1], we demonstrate that a simple adaption of the standard reduction algorithm leads to a variant for distributed systems. Our algorithmic design ensures that the data is distributed over the nodes without redundancy; this permits the computation of much larger instances than on a single machine. Moreover, we observe that the parallelism at least compensates for the overhead caused by communication between nodes, and often even speeds up the computation compared to sequential and even parallel shared memory algorithms. In our experiments, we were able to compute the persistent homology of filtrations with more than a billion (109) elements within seconds on a cluster with 32 nodes using less than 6GB of memory per node.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Ulrich foaf_name: Bauer, Ulrich foaf_surname: Bauer foaf_workInfoHomepage: http://www.librecat.org/personId=2ADD483A-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-9683-0724 - foaf_Person: foaf_givenName: Michael foaf_name: Kerber, Michael foaf_surname: Kerber orcid: 0000-0002-8030-9299 - foaf_Person: foaf_givenName: Jan foaf_name: Reininghaus, Jan foaf_surname: Reininghaus foaf_workInfoHomepage: http://www.librecat.org/personId=4505473A-F248-11E8-B48F-1D18A9856A87 bibo_doi: 10.1137/1.9781611973198.4 dct_date: 2014^xs_gYear dct_language: eng dct_publisher: Society of Industrial and Applied Mathematics@ dct_title: Distributed computation of persistent homology@ ...