DA14 Abstracts 27 IP1
[email protected] Shape, Homology, Persistence, and Stability CP0 My personal journey to the fascinating world of geomet- Distributed Computation of Persistent Homology ric forms started 30 years ago with the invention of al- pha shapes in the plane. It took about 10 years before Advances in algorithms for computing persistent homol- we generalized the concept to higher dimensions, we pro- ogy have reduced the computation time drastically – as duced working software with a graphics interface for the long as the algorithm does not exhaust the available mem- 3-dimensional case, and we added homology to the com- ory. Following up on a recently presented parallel method putations. Needless to say that this foreshadowed the in- for persistence computation on shared memory systems, we ception of persistent homology, because it suggested the demonstrate that a simple adaption of the standard reduc- study of filtrations to capture the scale of a shape or data tion algorithm leads to a variant for distributed systems. set. Importantly, this method has fast algorithms. The Our algorithmic design ensures that the data is distributed arguably most useful result on persistent homology is the over the nodes without redundancy; this permits the com- stability of its diagrams under perturbations. putation of much larger instances than on a single machine. The parallelism often speeds up the computation compared Herbert Edelsbrunner to sequential and even parallel shared memory algorithms. Institute of Science and Technology Austria In our experiments, we were able to compute the persis-
[email protected] tent homology of filtrations with more than a billion (109) elements within seconds on a cluster with 32 nodes using less than 10GB of memory per node.