Visualization Challenges for Large Scale Astrophysical Simulation Data. Ultrascale Visualization Workshop
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1 Visualization Challenges for Large Scale Astrophysical Simulation Data Ralf Kähler (KIPAC/SLAC) Tom Abel (KIPAC/Stanford) Marcelo Alvarez (CITA) Oliver Hahn (Stanford) Hans-Christian Hege (ZIB) Ji-hoon Kim (KIPAC) Stuart Marshall (SLAC) Matthew Turk (Columbia) Risa Wechsler (Stanford) John Wise (Georgia Tech) Ultrascale Visualization Workshop November 13, 2011 Seattle, WA
2 COMPUTATIONAL ASTROPHYSICS Simulation: Wise & Abel Evolution of First Stars Simulation: Kim & Abel Evolution of First Galaxies COMPUTATIONAL PHYSICS DEPARTMENT Simulation: Wu, Hahn & Wechsler Large-Scale Structure Formation Simulation: Alvarez & Abel Re-ionization Epoch
3
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5 Simulation: Wu, Hahn & Wechsler Large-Scale Structure Formation
6 Evolution of First Stars Simulation: Turk & Abel Simulation: Kim & Abel Evolution of First Galaxies
7 Reionization Epoch Simulation: Alvarez & Abel
8 Numerical Astrophysics High dynamical range
9 Numerical Astrophysics High dynamical range Formation of First Stars Simulation: T. Abel - Protogalaxy > 10,000 light years
10 Numerical Astrophysics High dynamical range Formation of First Stars - Protogalaxy > 10,000 light years - Protostar ~ 10 solar radii Simulation: T. Abel
11 Numerical Astrophysics > 10 spatial orders of magnitude Simulation: T. Abel
12 STRUCTURED ADAPTIVE MESH REFINEMENT Simulation Data: Tom Abel
13 TEMPORAL REFINEMENT AMR schemes perform refinement in time Stability condition for solvers: - Δt proportional to Δx - global Δt determined by smallest Δx Computational overhead on coarse levels
14 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
15 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
16 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
17 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
18 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
19 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
20 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
21 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
22 TEMPORAL REFINEMENT Level 0 Level 1 Level TIME
23 enzo - Adaptive Mesh Refinement enzo - Astrophysical Adaptive Mesh Refinement code - Cosmological structure formation simulations
24 DATA SIZES levels of refinement 10, ,000 time steps up to 100 million patches - less than 32^3 cells per patch up to billions of cells per time step - different fields ( gas density, gas temperature, etc. ) 10s of TByte of data
25 VISUALIZATION SOFTWARE C++, JavaScript Graphical User Interface (Qt) OpenGL Available as Open Source soon Simulation data: Kim & Abel
26 VISUALIZATION SOFTWARE Modular Design: add vis-routines & readers Readers for Enzo, Gadget, RAMSES Simulation data: Kim & Abel
27 VISUALIZATION SOFTWARE Modular Design: add vis-routines & readers Readers for Enzo, Gadget, RAMSES Data nodes: Grid-based (AMR) Point-based (unstructured) Image data Simulation data: Kim & Abel
28 VISUALIZATION SOFTWARE Visualization methods: Simulation data: Kim & Abel
29 VISUALIZATION SOFTWARE Real-Time Rendering: OpenGL & OpenGL Shading Language Simulation data: Kim & Abel Customize rendering at run-time by modifying OpenGL Shaders
30 VISUALIZATION SOFTWARE Visualization methods: Slicing Direct Volume Rendering Streamlines
31 VISUALIZATION SOFTWARE Visualization methods: Slicing Direct Volume Rendering Streamlines Simulation Data: Mike Norman
32 VISUALIZATION SOFTWARE Visualization methods: Slicing Direct Volume Rendering Streamlines Simulation Data: John Wise & Tom Abel
33 VISUALIZATION SOFTWARE Visualization methods: Slicing Direct Volume Rendering Streamlines Simulation Data: Ji-hoon Kim & Tom Abel
34 VISUALIZATION SOFTWARE Visualization methods: Slicing Direct Volume Rendering Streamlines Simulation Data: Wu, Hahn & Wechsler
35 Pre-Rendering for American Museum of Natural History Show The Big Bang, Narrated by Liam Neeson Simulation data: Ji-hoon Kim (Stanford), Tom Abel (Stanford/SLAC)
36 DATA ACCESS PATTERNS Level 0 Level 1 Level TIME
37 DATA ACCESS PATTERNS Level 0 Level 1 Level TIME
38 DATA ACCESS PATTERNS Level 0 Level 1 Level TIME
39 DATA ACCESS PATTERNS Level 0 Level 1 Level TIME
40 Generation of Proxy-Grid Structure Time A Time B
41 Generation of Proxy-Grid Structure Time A Time B
42 Generation of Proxy-Grid Structure Time A Time B
43 Generation of Proxy-Grid Structure Time A Time B
44 Generation of Proxy-Grid Structure
45 DATA STORAGE HDF5 for data I/O - Enzo HDF5 output - each processor writes stream of separate HDF5 groups - no spatial or temporal relations GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" {. ATTRIBUTE "level" {. DATASET "Density" { DATASET "Temperature" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" {. ATTRIBUTE "level" {. DATASET "Density" { DATASET "Temperature" { GROUP "grid-n" { ATTRIBUTE "origin" { ATTRIBUTE "dims" {. ATTRIBUTE "level" {. DATASET "Density" { DATASET "Temperature" {
46 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { Post-processing: - create HDF5 index file - match the AMR grid structure GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
47 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { available time steps GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
48 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { time groups GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
49 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { level groups GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
50 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { grids on this level GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
51 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { grid meta data GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
52 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { previous & next time steps GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
53 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { links to datasets GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "level-2" { GROUP "time-1" {
54 HDF5 INDEX FILE HDF5 "./rho.a5" { GROUP "/" { GROUP "globalmetadata" { DATASET "times" { DATASET "timesteps" { links to datasets GROUP "time-0" { GROUP "level-0" { ATTRIBUTE "predecessor" { ATTRIBUTE "successor" { GROUP "level-1" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" { ATTRIBUTE "data_reference" { GROUP "grid-0" { ATTRIBUTE "origin" { ATTRIBUTE "dims" {. ATTRIBUTE "level" {. DATASET "Density" { DATASET "Temperature" { GROUP "grid-1" { ATTRIBUTE "origin" { ATTRIBUTE "dims" {. ATTRIBUTE "level" {. DATASET "Density" { DATASET "Temperature" { GROUP "grid-n" { ATTRIBUTE "origin" { ATTRIBUTE "dims" {. ATTRIBUTE "level" {. DATASET "Density" { DATASET "Temperature" {
55 POSTGRES DATABASE Alternative: - Index-structure via databases Meta data in database, data (fields) in original HDF5 files QtSQL Module: PostgreSQL plugin
56 COMPARISON File sizes - Postgres database ~ 20% of HDF5 index file Metadata access - Postgres twice as fast as HDF5
57 GPU-Assisted Ray Casting Input data 3D texture Render front faces Fragment Shader for each covered pixel: Compute ray-direction Sampling and color mapping Resulting intensities --> frame buffer [Stegmaier, et al. 2005]
58 GPU-Assisted Ray Casting for AMR Data Problem: Overlapping regions Decomposition of data domain Adaptive kd-tree - Nodes represent of non-overlapping blocks of cells - View-dependent sorting (front-to-back or back-to-front)
59 GPU-Assisted Ray Casting for AMR Data View-dependent selection of nodes: - Resolution level based on distance to viewpoint ~200,000 nodes ~20,000 nodes
60 Simulation data: John Wise (Georgia Tech), Tom Abel (Stanford/KIPAC)
61 Pre-Rendering for California Academy of Sciences, Dome Show Life: A Cosmic Journey, Narrated by Jodie Foster, November 2010 Simulation data: John Wise (Princeton), Tom Abel (Stanford/SLAC)
62 POINT-BASED DATASETS Unstructured point datasets - Dark matter density - Single stars and star clusters - Point attributes - Position Simulation: Wu, Hahn & Wechsler - Mass - Age - Accretion rate -
63 GPU-Raycasting of Combined Grid- and Point-based Data Solution for opaque point representation 1. Pass Enable depth-buffer updates Render points 2. Pass Perform ray-tracing up to pixels depth value 3. Blending step
64 GPU-Raycasting of Combined Grid- and Point-based Data Usually semi-transparent point representation gaussian opacity profile for galaxies, stars, DM, etc. Potential solution: Resampling of point data to grid structure Simultaneous rendering of both (grid) data sets
65 GPU-Raycasting of Combined Grid- and Point-based Data Requires highly resolved grids High (texture-)memory consumption and/or sacrifices data resolution Low-pass filtering Rendering artifacts
66 GPU-Raycasting of Combined Grid- and Point-based Data Point data Octree structure - Efficient GPU-representation - Implicit bounding-box information Recursive refinement of octree nodes Stopping criteria - Number of inserted points < threshold
67 Combining Grid and Point Data Node texture 3D-RGBA texture One texel per node Alpha-channel stores node type Internal node Leaf node RGB-channel Index of first child node Index into data texture Simulation: Alvarez & Abel
68 Combining Grid and Point Data Data texture 3D-RGBA floating point RGB-channels: center Alpha-channel: radius Radius=0 indicates end of list Simulation: Alvarez & Abel
69 GPU-Raycasting of Combined Grid- and Point-based Data 1. Grid data 3D-texture 2. Point set two 3D-textures (GPU-octree representation) 3. Render front faces of bounding box 4. In Fragment shader Compute ray-direction Sampling of grid-based data Sampling of point-based data Combination of partial intensities Combination with total intensity 5. Resulting intensity frame-buffer
70 Summation of Intensities Relation between opacity and extinction coefficient Combination of opacities for segment si Combination with accumulated intensity/opacity of ray Combined Point & Grid Raycasting
71 Performance Optimizations Example: Gaussian profiles Combined Point & Grid Raycasting
72 Performance Optimizations ROI around camera location Combined Point & Grid Raycasting
73 Performance Optimizations Point and grid data inside ROI rendered with GPU approach Combined Point & Grid Raycasting
74 Performance Optimizations Outside ROI: - Process grid data until depth of first pass - blending with point splats Combined Point & Grid Raycasting
75 Performance Optimizations Combined Point & Grid Raycasting
76 Performance Optimizations Correct result if only one splat hit by ray outside ROI Combined Point & Grid Raycasting
77 Performance Optimizations Partially incorrect depth sorting for more than one splat Artifacts usually not visible in far-field Combined Point & Grid Raycasting
78 Comparison of Rendering Quality ROI = 100% of volume ROI = 0% of volume Simulation: Alvarez & Abel ROI = 25% of volume Combined Point & Grid Raycasting
79 Numerical Simulation: Marcelo Alvarez (CITA), Tom Abel (KIPAC/Stanford) Combined Point & Grid Raycasting
80 Numerical Simulation: Marcelo Alvarez (CITA), Tom Abel (KIPAC/Stanford) Combined Point & Grid Raycasting
81 Numerical Simulation: Marcelo Alvarez (CITA), Tom Abel (KIPAC/Stanford)
82 Numerical Simulation: Marcelo Alvarez (CITA), Tom Abel (KIPAC/Stanford)
83 Visit us as the SLAC booth (303) and watch some visualizations in 3D!
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