Computing contour trees in all dimensions
Computational Geometry: Theory and Applications - Fourth CGC workshop on computional geometry
Path seeds and flexible isosurfaces using topology for exploratory visualization
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Parallel Computation of the Topology of Level Sets
Algorithmica
Simplifying Flexible Isosurfaces Using Local Geometric Measures
VIS '04 Proceedings of the conference on Visualization '04
Persistence-sensitive simplification functions on 2-manifolds
Proceedings of the twenty-second annual symposium on Computational geometry
Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities
IEEE Transactions on Visualization and Computer Graphics
Analyzing and Tracking Burning Structures in Lean Premixed Hydrogen Flames
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Optimal Topological Simplification of Discrete Functions on Surfaces
Discrete & Computational Geometry
Parallel Computation of 3D Morse-Smale Complexes
Computer Graphics Forum
The Parallel Computation of Morse-Smale Complexes
IPDPS '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium
Parallel Computation of 2D Morse-Smale Complexes
IEEE Transactions on Visualization and Computer Graphics
Topology-based visualization of transformation pathways in complex chemical systems
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Exploring power behaviors and trade-offs of in-situ data analytics
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Improved simulations and sensors are producing datasets whose increasing complexity exhausts our ability to visualize and comprehend them directly. To cope with this problem, we can detect and extract significant features in the data and use them as the basis for subsequent analysis. Topological methods are valuable in this context because they provide robust and general feature definitions. As the growth of serial computational power has stalled, data analysis is becoming increasingly dependent on massively parallel machines. To satisfy the computational demand created by complex datasets, algorithms need to effectively utilize these computer architectures. The main strength of topological methods, their emphasis on global information, turns into an obstacle during parallelization. We present two approaches to alleviate this problem. We develop a distributed representation of the merge tree that avoids computing the global tree on a single processor and lets us parallelize subsequent queries. To account for the increasing number of cores per processor, we develop a new data structure that lets us take advantage of multiple shared-memory cores to parallelize the work on a single node. Finally, we present experiments that illustrate the strengths of our approach as well as help identify future challenges.