A Visualization Framework For Earth Materials Studies Bijaya Bahadur Karki

1. 2nd Workshop: Minneapolis, August 5-10, 2007 A Visualization Framework For Earth Materials Studies Bijaya Bahadur Karki Graduate Students:Dipesh Bhattarai and Gaurav Khanduja Department of Computer Science Department of Geology and Geophysics Louisiana State University, Baton Rouge, LA 70803

2. Studying Materials Problems Simulation algorithms PWscf, VASP PCMD Parallel and distributed computing Tezpur (15.3 TFlops, 360 nodes 2 dual-core processor) Queen Bee (50.7 TFlops, 680 nodes 2 quad-core processors) Compute- and data-intensive applications Mantle materials: Silicates and oxides Rheology Liquids Visualization algorithms Massive multivariate data: MDV STMR ReVis

3. Visualization: Definition • Process of making a computer image for gaining insight onto data/information • Transform abstract, physical data/information to a form that can be seen (i.e., visual representation) • Enhance cognitive process

4. Visualizing Materials Data • Properties/processes of interest: • Microscopic • Atomic structure, dynamics • Electronic structure • Macroscopic • EoS, elasticity, thermodynamics • Data characteristics: • Three-dimensional, time-dependent • Multivariate • Massiveness, multiple sets • Computational, experimental origin

5. Application-Based Approach • Numerous visualization systems exist • None of them may be good enough • Lack of desired functionality and flexibility • How to meet domain-specific needs • Presentation and interactivity • On-the-fly data processing • Multiple sets of data • Visualization with database • Remote and collaborative visualization • Visualization/computational steering

6. Current Visualization Activities • Multiple datasets visualization (MDV) • Electron density distribution • Space-time multi-resolution (STMR) visualization • Atomic structure and dynamics • Remote visualization • Elastic moduli and wave propagation

7. Multiple Datasets Visualization Simultaneous rendering of more than one set of data to examine cross-correlation among them Isosurface extraction GPU-based visualization Adaptive scalable approach

8. Example: Electronic Structure Perfect Defect Defect - Perfect Difference in two images Mg2- vacancy defect in MgSiO3 post-perovskite Initial configurations Final configurations (after relaxation)

9. Scalable Adaptive Isosurface Extraction Original cell Octree nodes Multiresolution approach High resolution Low resolution Dual resolution Octree data structure

10. Performance Analysis All-in-memory approach Scalable adaptive approach Performance measurement on 64 sets of scalar volume data with size of 2563 and 5123

11. GPU-Based Visualization Graphics hardware assisted 3D textures Interactive clipping Isosurface Khanduja and Karki WSCG 2005 GRAPP 2006 WSCG 2007

12. Example: Electronic Structure Perfect Defect Defect - Perfect Difference in images Mg2- vacancy defect in MgSiO3 post-perovskite Initial configurations Final configurations (after relaxation)

13. MDV Example 25 sets of the scalar volume data of 2563 size in a planer clipped mode using 3D surface texture mapping Electron density in liquid MgO as a function of time Multi-scale color map: Blue: 0 to 0.05 Blue and green: 0.05 to 0.5 Red: above 0.5

14. Electron Density: Defects in MgSiO3 ppv Vacancies Mg Si O Migrating ions

15. Electron Density: Defects in MgSiO3 ppv Vacancies Mg Si O Migrating ions Spheres and lines Karki and Khanduja, EPSL, 2007

16. Defects in MgSiO3 ppv: Atomic Structure Vacancies Mg Si O Migrating ions Mg: Green Si: Blue O: Red Vacancy site: Black

17. Space-Time Multiresolution (STMR) Atomistic Visualization Integration of visualization and complex analysis On-the-fly extraction and rendering of a variety of data Pair correlation, coordination and cluster structures Dynamical behavior

18. Atomistic Visualization Modules • Approach • Spatial proximity • Temporal proximity • Spatio-temporal analysis • Model • Complete data rendering • Local/extracted data rendering

19. Position-Time Series Data Atomic Species: Mg Si O H Points: Complete data set Balls: Instantaneous configuration Data: {P(jt) | 0 ≤ j≤ N} where P(t) = {pi(t) | 1 ≤ i ≤ n}

20. Coordination Environment Radial distribution functions Given atomic system: Hydrous MgSiO3 liquid 16 different pair correlation structures Cutoff distances from partial RDFs Atomic species: spheres Si-O Coordination stability Coordination environment Coordination clusters

21. Pair Correlation Matrix Mg Si O H Mg Si 16 different types of nearest-neighbor pairs Diagonal: like atoms Off-diagonal: unlike atoms O H

22. Radial Distribution Function Spatial and temporal information on Si-O coordination

23. Coordination-Encoding Color map 2 3 4 5 6 Three-, four- and five-fold coordination

24. Coordination Stability Color map 2 3 4 5 6 The lines (thickness encoding the bond stability) and center atoms (size encoding the coordination stability) are color-coded to represent, respectively, the length distribution and coordination states. The stability represents the fraction of the total simulation time over which a given bond or coordination state exists. Bhattarai and Karki, ACMSE 2007

25. Stability of Different Coordination 16 coordination states 0 1 2 3 4 5 6 7 8 9 10 11 Four types exist 3 4 5 6 12 13 14 15

26. Coordination Cluster Spatial and temporal information on Si-O coordination The lines (thickness encoding the bond stability) and center atoms (size encoding the coordination stability) are color-coded to represent, respectively, the length distribution and coordination states. The stability represents the fraction of the total simulation time over which a given bond or coordination state exists. Bhattarai and Karki, ACMSE 2007

27. Coordination Cluster Per Atom Spatial and temporal information on Si-O coordination

28. Coordination Visualization Given atomic system: Hydrous MgSiO3 liquid 16 different pair correlation structures Cutoff distances from partial RDFs Atomic species: spheres Radial distribution functions Si-O Coordination stability Coordination environment Coordination clusters

29. Visualizing Dynamics Spheres for atomic displacements Ellipsoids for covariance matrices Diffusion in 80-atoms liquid MgSiO3 Diffusion in 64-atoms liquid MgO Bhattarai and Karki, ACMSE 2007

30. Elasticity visualization Remote execution Visualization and database server Online data reposition

31. Elasticity Visualization - ElasViz • Multivariate elastic moduli • Variation with pressure, temperature and composition • Elastic wave propagation in an anisotropic medium • Velocity-direction surfaces • Anisotropic factors Karki and Chennamsetty, Vis. Geosci., 2004

32. Modules of ElasViz ReadData GenerateDirection GenerateVelocity CijPlot DrawVelocity AnPlot Display Other Modules

33. Global Visualization Mode

34. Selective Visualization Mode

35. Summary • Visualization for gaining insight into a variety of datasets for important minerals properties and processes • Increasing amounts of data from simulations and other resources. • Important visualization systems under development: • Elasticity, atomic and electronic data • A lot needs to be done: • Adding more functionalities • Merging atomistic and electronic components • Extending for remote and distributed access • Adopting in virtual (immersive) environment. Support from NSF (EAR 0347204, ATM 0426601 and EAR 0409074).