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Advanced Visualization How to make pretty pictures for science! Aaron Knoll Research Scientist SCI Institute, University of Utah Roadmap • What is visualization, why do it? • How do we do visualization? • Visualization software • Large-scale visualization 4-8 August 2014 2 1 What is visualization? 4-8 August 2014 3 Two kinds of visualization 1. Scientific visualization • Data have spatial context • Map data features to colors in a way that makes visual sense • F(space, time) RGBA • i.e., “rendering stuff” 4-8 August 2014 4 2 Two kinds of visualization 2. Information visualization • Data have no spatial context (or, space is secondary) • Illustrate relationships between diverse attributes • i.e., “plots, graphs and diagrams” 4-8 August 2014 5 Two kinds of visualization • There is no “line in the sand” between InfoVis and SciVis. • Today we’ll mostly talk about SciVis... 4-8 August 2014 6 3 Why do visualization? 4-8 August 2014 7 Why do visualization? • Illustration 4-8 August 2014 8 4 Why do visualization? • Illustration • Analysis 4-8 August 2014 9 Why do visualization? • Illustration • Analysis • Validation DFT computation - nuclear fission of Plutonium (data courtesy of Nicolas Schunck, LLNL) 4-8 August 2014 10 5 Napoleon in Russia, 1812-1813 4-8 August 2014 11 Florence Nightingale Cox Comb 4-8 August 2014 12 6 Growth of Data 1000 • As of February 100 2011, ) approximately 295 18 exabytes of data (10 exist 10 all printed documents • Every two days we all spoken words in all lives create as much Exabytes data as we did 1 from the beginning of mankind until 2003 amount human minds can store in 1yr 0.1 Sources: Lesk, Berkeley SIMS, Landauer, EMC, TechCrunch, Smart Planet “The purpose of computing is insight not numbers.” -- R. W. Hamming (1961) 4-8 August 2014 14 7 How do we do visualization? 4-8 August 2014 15 Scientific Visualization basics Computational data scalar/vector/tensor fields structured, unstructured, atomistic Visualization Reader Filters Color maps Computer graphics Rendering! Camera Lighting model Rendering algorithm 4-8 August 2014 16 8 Computational Data • Typically continuous fields 0 4 8 0 • Scalar, vector, tensor 4 14 9 0 • 2D, 3D, 2.5D 6 11 1 0 2 1 0 0 4-8 August 2014 17 Computational Data • Typically continuous fields • Scalar, vector, tensor • 2D, 3D, 2.5D • Geometry (“topology”) • Structured (finite differences) • Unstructured • Tet,hex meshes (finite elements) • Particles -- n-body simulations, (molecular dynamics, cosmology, blood flow) • AMR • Other (spectral / functional / wavelet) • DFT, FMM, DNS • Usually generate large structured data as postprocess 4-8 August 2014 18 9 Computer graphics (ideally) Primitives Camera Rendering (points, lines, Display (Rasterization, triangles, (perspective, ray tracing, voxels, (pixels) orthographic) splatting) elements) 4-8 August 2014 19 Computer graphics (reality) Camera Primitives Rendering Display (perspective, (triangles) (rasterization) (pixels) orthographic) 4-8 August 2014 20 10 The visualization pipeline • Convert (volume) vis data into renderable (triangle) primitives 4-8 August 2014 21 The visualization pipeline • Convert (volume) vis data into renderable (triangle) primitives • Simplify data to visualize what we want in the process. 4-8 August 2014 22 11 The visualization pipeline • Convert (volume) vis data into renderable (triangle) primitives • Simplify data to visualize what we want in the process. • Each step in the pipeline is a filter. 4-8 August 2014 23 Scalar field visualization Pseudocolor and cut planes. 4-8 August 2014 24 12 Isocontours 0 1 3 2 3 1 5 0 0 3 Isocontour for isovalue = 4? 3 8 6 3 2 0 2 6 1 1 0 1 2 3 1 Isosurfaces • An isosurface is a contour of a 3D volume. • Marching cubes • in each voxel, extract the isosurface as triangles • Wyvill & McPeeters 86 (blobbies), Lorensen & Cline 87 (structured volumes) – cited over 10,000 times! 4-8 August 2014 26 13 Volume rendering • Alternative to isosurfacing. • Less of a filter, more of a full rendering technique • Filtering is done through the transfer function. • Can be implemented numerous ways (slicing, splatting, ray casting) 4-8 August 2014 27 Volume rendering • Alternative to isosurfacing. • Less of a filter, more of a full rendering technique • Filtering is done through the transfer function. • Can be implemented numerous ways (slicing, splatting, ray casting) • Renders “all” isosurfaces at once, with control over opacity • Disadvantages: slow, hard to create transfer functions… 4-8 August 2014 28 14 Volume rendering • Alternative to isosurfacing. • Less of a filter, more of a full rendering technique • Filtering is done through the transfer function. • Can be implemented numerous ways (slicing, splatting, ray casting) • All 4-8 August 2014 29 Flow vis • So far, we have discussed scalar fields. • What do we do for vector field data? • Seed + advect particles: Pnew = Pcurrent + VPDt • Streamlines advect in space • Pathlines advect in time 4-8 August 2014 30 15 Flow vis – texture methods (2D) • Line integral convolution (LIC) • spot noise 4-8 August 2014 31 Flow vis – volumetric methods • 3D LIC • FTLE (VisIt) • Virtual rheoscopic fluids (VisIt) 4-8 August 2014 32 16 Visualization software 4-8 August 2014 33 VTK • http://www.vtk.org • Open source, developed by Kitware • The standard bearer for scientific visualization • Marching cubes, streamlines, cut/clip planes, etc. • Hundreds of filters for data resampling, geometry processing, analysis • VTK data model • Supports any structured/unstructured data, fully-featured C++/templates data model • Call as a library from C++, Java, Python, Tcl/Tk • What VTK does not do: • Full-featured UI • Exploit latest OpenGL 4.X features • Use GPUs for filter computation (VTK-M initiative) • Parallel rendering 4-8 August 2014 34 17 ParaView • http://www.paraview.org/ • Open source, built on top of VTK • Maintained by Kitware, Sandia, LANL, ARL • Provides data-parallel reading, filtering and rendering • Qt UI, tree hierarchy of filters, camera controls, etc. • Many, many parallel readers • Reasonably good volume rendering 4-8 August 2014 35 VisIt • http://visit.llnl.gov • Contributors at LLNL, LBNL and many others • Also built on top of VTK • Also provides parallel reading, filtering and rendering! • Additional “AVT” layer • Capabilities outside of the VTK pipeline • Filters replaced with plots and operators • Huge array of flow visualization techniques • Fast streamlines, particle tracing, FTLE, VRF • Also, many different readers • Enzo AMR, other data 4-8 August 2014 36 18 SCIRun • http://www.sci.utah.edu/cibc-software/scirun.html • Open source, maintained by the University of Utah • Designed for efficient operation on multithreaded workstations (SMP) • “Computational workbench”: a single pipeline for both simulation and visualization • Bioelectric field FE simulation codes • Computer aided surgery • Unique visualization modalities • Volume rendering with 2D transfer functions 4-8 August 2014 37 Visus • http://www.pascucci.org/visus/ • Maintained by Valerio Pascucci, SCI Institute, University of Utah • Streaming visualization framework for remote and in situ visualization • IDX file format for efficient data processing and rendering. • Fast out-of-core queries and analysis • 2D and 3D gigapixel imagery, large structured data, AMR 4-8 August 2014 38 19 Volume renderers • ImageVis3D • http://www.sci.utah.edu/cibc-software/imagevis3d • Maintained by SCI and TU Duisberg • Fast, out-of-core GPU volume renderer with Qt UI, transfer function editors • Voreen • http://www.voreen.org • University of Munster and Linkoping University • Supports complicated workflows, variety of rendering and shading methods • Exposure renderer • https://code.google.com/p/exposure-render/ • CUDA-based photorealistic volumetric path tracing • These only support structured volume data. 4-8 August 2014 39 Many, many others • Molecular • VMD • PyMol • Avogadro • Info Vis • Microsoft Excel • Matplotlib (python) • R • Matlab • IVTK • Google charts • Rendering frameworks with vis capabilities • Intel Embree • NVIDIA Optix 4-8 August 2014 40 20 Remote visualization demo 4-8 August 2014 41 21.
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