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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 2nd Workshop: Minneapolis, August 5-10, 2007

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

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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

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

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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

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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

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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

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Example: Electronic Structure Mg 2- vacancy defect in MgSiO 3 post-perovskite PerfectDefectDefect - Perfect Difference in two images Initial configurations Final configurations (after relaxation)

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Scalable Adaptive Isosurface Extraction Octree data structure High resolutionLow resolutionDual resolution Multiresolution approach Original cell Octree nodes

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Performance Analysis Performance measurement on 64 sets of scalar volume data with size of 256 3 and 512 3 All-in-memory approach Scalable adaptive approach

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

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Example: Electronic Structure Mg 2- vacancy defect in MgSiO 3 post-perovskite PerfectDefectDefect - Perfect Difference in images Initial configurations Final configurations (after relaxation)

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MDV Example 25 sets of the scalar volume data of 256 3 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

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Electron Density: Defects in MgSiO 3 ppv MgSiO Vacancies Migrating ions

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Electron Density: Defects in MgSiO 3 ppv Spheres and lines Karki and Khanduja, EPSL, 2007 MgSiO Vacancies Migrating ions

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Defects in MgSiO 3 ppv: Atomic Structure Mg: GreenSi: BlueO: RedVacancy site: Black MgSiO Vacancies Migrating ions

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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

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

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Position-Time Series Data OHMgSi Atomic Species: Points: Complete data set Balls: Instantaneous configuration Data: {P(j t) | 0 ≤ j ≤ N} where P(t) = {p i (t) | 1 ≤ i ≤ n}

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Coordination Environment Given atomic system: Hydrous MgSiO 3 liquid Atomic species: spheres 16 different pair correlation structures Cutoff distances from partial RDFs Si-O Coordination environment Coordination stability Coordination clusters Radial distribution functions

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

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

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Coordination-Encoding 23456 Color map Three-, four- and five-fold coordination

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Coordination Stability 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 23456 Color map

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Stability of Different Coordination 3456 16 coordination states Four types exist 0123 4567 891011 12131415

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Coordination Cluster 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. Spatial and temporal information on Si-O coordination Bhattarai and Karki, ACMSE 2007

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

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Coordination Visualization 16 different pair correlation structures Cutoff distances from partial RDFs Si-O Coordination environment Coordination stability Coordination clusters Radial distribution functions Atomic species: spheres Given atomic system: Hydrous MgSiO 3 liquid

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Visualizing Dynamics Diffusion in 80-atoms liquid MgSiO 3 Spheres for atomic displacements Ellipsoids for covariance matrices Diffusion in 64-atoms liquid MgO Bhattarai and Karki, ACMSE 2007

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Elasticity visualization Remote execution Visualization and database server Online data reposition

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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

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ReadData CijPlot Modules of ElasViz GenerateDirectionGenerateVelocity AnPlot DrawVelocity Other ModulesDisplay

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Global Visualization Mode

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Selective Visualization Mode

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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).

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*Partially funded by the Austrian Grid Project (BMBWK GZ 4003/2-VI/4c/2004) Making the Best of Your Data - Offloading Visualization Tasks onto the Grid.

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