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Visualizing Large, Complex Data Outline • Visualizing Large Scientific Simulation Data – Importance-driven visualization – Multidimensional filtering • Visualizing Large Networks – A layout method – Filtering methods Supernova Supernova Shared resources The Large Data Problem Supercomputer Storage Visualization Machine A Turbulent Lifted Autoignitive Ethylene/air Jet Flame HO2 and mixture fraction isosurface HO2 and OH Complex, Multi-Scale Nature of Turbulent Flow Small eddies are hidden in the multi-layer flow Feature-directed Data Reduction and Visualization Feature-directed Data Reduction and Visualization • Achieved over 80% saving • In situ data reduction and triage can facilitate following data analysis and visualization! In Situ Methods Enables • Seeing all the data and capturing transient events at highest possible detail • More effective data reduction • More efficient postprocessing analysis and viz • Monitoring/debugging of the simulation (ensuring the calculation is running well) • Steering the simulation and driving the simulation with interactive analysis • Tuning and optimizing the performance of the simulation/machine Fusion Multidimensional Particles Filtering Multidimensional Particles Filtering Trapped particles that change direction frequently Visualizing Large, Complex Networks • Data are created and collected for a variety of purposes – – – – – – – – Internet is a source of massive data Cyber security Homeland security Business transactions Mobile device user data Health care data Email … • Relations in these data are often represented with graph/ networks for analysis • To visualize a network, we need to lay it out The Graph Layout Problem • The cost of displaying a graph • The hairball problem of large graph layouts – Large, dense graphs become a mess – Inefficient use of space – Details cluttered • Solutions – – – – Filtering Clustering Abstraction Focus+context California data 6,107 nodes 15,160 edges High dimensional embedding method Space Partitioning Based Layout California data 6,107 nodes 15,160 edges Hibert curve Treemap Linlog Method 10,737s Radial Treemap Gosper curve Space Filling Curve Based Layout Layout defined by clustering Space filing Interaction is very fast O(|V|) Scales to large graphs Effective for Focus+Context Guaranteed aspect ratios Nodes don’t become colinear Rendering is slower than layout A protein homology graph. Color corresponds to depth in the clustering hierarchy. |V| = 28,854, |E| = 1,180,816 Visualizing Internet Connectivity Centrality Sensitivity • Centralities (degree, between-ness, closeness, eigenvector, Markov, …) indicate how important a node is in a network. • Studying the sensitivity and stability of a network in terms of different metrics for centrality allow us to – Filter the network – Search and explore in the network – Obtain an overview of the network • Compute sensitivity as the derivative of the centrality function, approximate derivatives of centrality using finite difference, and validate by computing the mean square error of the linear fit between the approximated and analytical values Centrality Sensitivity Minimum spanning tree as the core network with centrality derivatives as edge weights Central nodes remain central Network of protein-protein interaction (~1500 nodes) Overview of Sensitivity Friendster social network Links exhibit negative sensitivity (red) between cluster centers Astrophysics co-author network One competitive network (red) and one collaborative network (blue) Summary • Visualization, as a tool complementing conventional data analysis and mining methods, enhances our ability to utilize and communicate with data and knowledge • In situ visualization and data reduction/triage is the most plausible solution to extreme scale scientific supercomputing • Visual-based network analysis has become an essential tool, and interactivity is the key to understanding complex networks. • The 1st IEEE Symposium on Large Data Analysis and Visualization (LDAV), Oct 23-24, Providence, RI • The 6th Ultrascale Visualization Workshop, Supercomputing Conference (SC11), November 13, Seattle, WA