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Parallel Volume Rendering for Ocean Visualization in a Cluster of PCs Alexandre Coelho Marcio Nascimento Cristiana Bentes Maria Clicia S. de Castro Ricardo Farias – Geomática/UERJ – IME/UERJ – COPPE/UFRJ

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Outline Volume Visualization Overview Parallel Rendering System Experimental Results Conclusions

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What is Volume Visualization? Volumetric data images 3D data 2D plane Gains: –Understanding –Visual analysis and interpretation –Meaningful information

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Volume Visualization Applications Medicine, Geology, Chemistry, Industry Geographical Information Systems –Ocean Modeling –Monitoring the atmospheric pollution –Terrain Modeling –Analyzing natural phenomena (cyclones)

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Volumetric Data Generated: –Sensors (CT scanner) –Simulation (Fluid Dynamic) –Measured Data (Ocean Buoys) Representation: –3D grid of voxels (Regular or Irregular)

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Volume Visualization Methods Surface Rendering –Generates image of the surface –Throws away data between surfaces Direct Volume Rendering –Treats object as semi- transparent –Can see entire volume

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Volume Visualization Methods Surface RenderingVolume Renderingx

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Volume Rendering Challenge Large scale 3D data –Computational intensive –Unacceptably long time on uniprocessors

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Efficient Volume Rendering Parallel Processing –Multiple processors: Parallel Machines Cluster of PCs

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Parallel Volume Rendering Clusters of PCs Low cost High availability Easy to update Parallel Machines Good speedups Expensive

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Our goal Parallel Volume Rendering System for Ocean Visualization –Efficient and scalable –Low-cost –All software implementation –Portable and Free software –Out-of-core execution

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Our goal Allows Visualization of Ocean Inner Structure –Climate research –Offshore industries –Fishing and Mammal Management

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The Parallel Rendering System DPZSweep –Based on PZSweep Sweeping plane paradigm Projection of the faces in depth order –Two modules: Pre-processing Parallel Rendering

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The Parallel Rendering System Grid Generation Ocean data Octree Creation Irregular grid Octree Parallel Algorithm Pre-processing Parallel Rendering

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Pre-Processing Grid Generation: –Latitude/Longitude data irregular grid

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Pre-Processing Octree Creation: –Out-of-core execution Octree file

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Parallel Rendering Algorithm Parallelization: –Breaking the screen into rectangles - tiles Image portion that can be computed independently

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Parallel Rendering Algorithm Parallelization: –Breaking the screen into rectangles - tiles

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Parallel Rendering Algorithm Tile distribution –Random assignment –Dynamic distribution

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Parallel Rendering Algorithm Dynamic Load Balancing –Rebalance the work –Distributed information diffusion algorithms –Work stealing

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Load Balancing Algorithms Nearest Neighbor (NN) –Steal work from the nearest neighbor Longest Queue (LQ) –Steal work from overloaded node –Token ring to distribute load information Circular Distribution (CD) –Dynamic distribution with token ring

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Experimental Results Cluster: –16 processors –512M bytes –Fast Ethernet 100Mbits/sec –Linux –MPI

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Ocean Dataset Gulf of Mexico Data– NRL/ERC-MSU –Thanks to Dr. Robert Moorhead Resolution: 1 degree latitude and longitude 6 depth levels 1 time step – Velocity 3 tetrahedralized versions: –Ocean (44K cells) –Ocean1 (356K cells) –Ocean2 (2854K cells)

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Performance Analysis

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Ocean Results

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Conclusions Distributed parallel volume rendering for ocean datasets on cluster of PCs: –Dynamic load balancing – low overhead –Out-of-core execution –Portable and free software infrastructure Great reductions in execution time Allows ocean researchers to interactively visualize large volumes of 3D data

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Future Work Fault-tolerance Grid execution Handheld interface Handling Time-varying data

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For your attention. Thank you

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Load Balancing Algorithms Nearest Neighbor Proc 1 Proc 0 Proc 2

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Load Balancing Algorithms Circular Distribution

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