harnessing the power of visualization 'concept to execution' - XSEDE PDF
Preview harnessing the power of visualization 'concept to execution' - XSEDE
HARNESSING THE POWER OF VISUALIZATION ‘CONCEPT TO EXECUTION’ Instructors Amit Chourasia, San Diego Supercomputer Center, UCSD Kelly Gaither, Texas Advanced Computing Center, UT Austin Sean Ahern, National Institute for Computational Sciences, UTK Hank Childs, Lawrence Berkley National Laboratory TUTORIAL SESSIONS LEVEL: BEGINNER TO INTERMEDIATE SESSION 1 SESSION 2 SESSION 3 Visualization Fundamentals Visualization with Paraview Visualization with VisIt (Lecture) (Hands On) (Hands On) 8:00am – 9:15am 9:30am – 10:45am 10:45am – 12:00pm Amit Chourasia Kelly Gaither Sean Ahern and Sean Ahern SESSION 1: VISUALIZATION FUNDAMENTALS WHAT IS SCIENTIFIC VISUALIZATION? • Visually extracting meaning from a scientific data set • Data set: Could come from a simulation, a physical experiment, a scientific instrument, anywhere. • Techniques: art, statistics, image processing, “info” vis, “gaming” techniques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01100000000000101110100010011011010100000000001001110101011011010111000000000010 01100010001010010100100000000010010000011100111001101000000000100101100010001010 VISUALIZATION PROGRESSES UP THE SCIENTIFIC DATA MODEL atoms collections meshes multidimensional materials arrays mesh arrays coordinate bytes refinement named fields vectors scalars bits floats arrays interpolation bonds time Simple Complex Each step gets us closer to the “ideal”. But each step is more domain-specific. DATA • Data • High Dimensional (structured and Unstructured) • Mesh • Meshes Discretizes space into points and cells • 1D, 2D, 3D • All of these over time (up to 4D) • Can have lower-dimensional meshes in a higher-dimensional space (e.g. 2D surface in 3D space) • Provides a place for data to be located • Defines how data is interpolated VARIABLES • Scalars, Vectors, Tensors • Sits on points or cells of a mesh • Points: linear interpolation • Cells: piecewise constant • Could have different dimensionality than the mesh (e.g. 3D vector data on a 2D mesh) MOTIVATION FOR VISUALIZATION To develop visual representations based on underlying data that are • Concise (Yes) • Unambiguous (Preferably) • Intuitive (Trainable) • Interactive (Desirable) • Scalable (We wish) VISUALIZATION COMPONENTS Viz Elements Viz Attributes View Attributes • Glyphs • Transforms • Orthographic Projection (e.g. Alphabets, Arrows) (Position, Rotation, Scale) • Perspective Projection • Lines • Color • Canvas Size • Triangles • Opacity • Light • Voxels* • Shade/Texture *Cannot be directly represented on displays
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