Scalable Lagrangian-Based Attribute Space Projection for Multivariate Unsteady Flow Data

In this paper, we present a novel scalable approach for visualizing multivariate unsteady flow data with Lagrangian-based Attribute Space Projection (LASP). The distances between spatial temporal samples are evaluated by their attribute values along the advection directions in the flow field. The massive samples are then projected into 2D screen space for feature identification and selection. A hybrid parallel system, which tightly integrates a MapReduce-style particle tracer with a scalable algorithm for massive projection, is designed to support the large scale analysis. Results show that the proposed methods and system are capable of visualizing features in the unsteady flow, which couples multivariate analysis of vector and scalar attributes with projection.

[1]  Peter Eades,et al.  A Heuristic for Graph Drawing , 1984 .

[2]  Robert S. Laramee,et al.  The State of the Art in Flow Visualization: Dense and Texture‐Based Techniques , 2004, Comput. Graph. Forum.

[3]  Ulrik Brandes,et al.  Eigensolver Methods for Progressive Multidimensional Scaling of Large Data , 2006, GD.

[4]  David F. Tate,et al.  A Novel Interface for Interactive Exploration of DTI Fibers , 2009, IEEE Transactions on Visualization and Computer Graphics.

[5]  Lijie Xu,et al.  A flow-guided file layout for out-of-core streamline computation , 2011, 2012 IEEE Pacific Visualization Symposium.

[6]  Xiaoru Yuan,et al.  Ieee Transactions on Visualization and Computer Graphics 1 Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates , 2022 .

[7]  Han-Wei Shen,et al.  Load-Balanced Parallel Streamline Generation on Large Scale Vector Fields , 2011, IEEE Transactions on Visualization and Computer Graphics.

[8]  Gordon Erlebacher,et al.  Lagrangian-Eulerian Advection of Noise and Dye Textures for Unsteady Flow Visualization , 2002, IEEE Trans. Vis. Comput. Graph..

[9]  Robert S. Laramee,et al.  The State of the Art in Flow Visualisation: Feature Extraction and Tracking , 2003, Comput. Graph. Forum.

[10]  Robert B. Ross,et al.  A Study of Parallel Particle Tracing for Steady-State and Time-Varying Flow Fields , 2011, 2011 IEEE International Parallel & Distributed Processing Symposium.

[11]  Kenneth I. Joy,et al.  Streamline Integration Using MPI-Hybrid Parallelism on a Large Multicore Architecture , 2011, IEEE Transactions on Visualization and Computer Graphics.

[12]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

[13]  J. Hyvärinen,et al.  An Arbitrary Lagrangian-Eulerian finite element method , 1998 .

[14]  Christian Rössl,et al.  Streamline Embedding for 3D Vector Field Exploration , 2012, IEEE Transactions on Visualization and Computer Graphics.

[15]  Helwig Hauser,et al.  Interactive Feature Specification for Focus+Context Visualization of Complex Simulation Data , 2003, VisSym.

[16]  Y. Takane,et al.  Multidimensional Scaling I , 2015 .

[17]  Gerik Scheuermann,et al.  Brushing of Attribute Clouds for the Visualization of Multivariate Data , 2008, IEEE Transactions on Visualization and Computer Graphics.

[18]  Kwan-Liu Ma,et al.  Parallel hierarchical visualization of large time-varying 3D vector fields , 2007, Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07).

[19]  Cláudio T. Silva,et al.  Interactive Vector Field Feature Identification , 2010, IEEE Transactions on Visualization and Computer Graphics.

[20]  Tom Peterka,et al.  Parallel particle advection and FTLE computation for time-varying flow fields , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[21]  Gunther H. Weber,et al.  Scalable computation of streamlines on very large datasets , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[22]  Hans-Peter Seidel,et al.  Path Line Attributes - an Information Visualization Approach to Analyzing the Dynamic Behavior of 3D Time-Dependent Flow Fields , 2009, Topology-Based Methods in Visualization II.

[23]  Jian Huang,et al.  Simplified parallel domain traversal , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[24]  Min Chen,et al.  Over Two Decades of Integration-Based, Geometric Flow Visualization , 2009, Eurographics.

[25]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[26]  W. Torgerson.  Multidimensional scaling: I. Theory and method , 1952 .

[27]  Geoffrey C. Fox,et al.  Applying Twister to Scientific Applications , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[28]  G. Haller.  Distinguished material surfaces and coherent structures in three-dimensional fluid flows , 2001 .

[29]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[30]  Kenneth Moreland,et al.  A Survey of Visualization Pipelines , 2013, IEEE Transactions on Visualization and Computer Graphics.

[31]  Gerik Scheuermann,et al.  Streamline Predicates , 2006, IEEE Transactions on Visualization and Computer Graphics.

[32]  Shian‐Jiann Lin.  A “Vertically Lagrangian” Finite-Volume Dynamical Core for Global Models , 2004 .

[33]  Xiaomin Zhu,et al.  Coupled Ensemble Flow Line Advection and Analysis , 2013, IEEE Transactions on Visualization and Computer Graphics.

[34]  Li Chen,et al.  Optimizing Parallel Performance of Streamline Visualization for Large Distributed Flow Datasets , 2008, 2008 IEEE Pacific Visualization Symposium.

[35]  Robert Latham,et al.  Toward a General I/O Layer for Parallel-Visualization Applications , 2011, IEEE Computer Graphics and Applications.

[36]  Min Chen,et al.  Over Two Decades of Integration‐Based, Geometric Flow Visualization , 2010, Comput. Graph. Forum.