ncsa blue waters visualization course - osc

NCSA Blue Waters Visualization Course

Han-Wei Shen Department of Computer Science and Engineering

The Ohio State University

Course Objectives • Target students: Graduate students (or advanced undergraduate students)

in computer science or other science/engineering disciplines • Prerequisite:

– Algorithm, undergraduate linear algebra/calculus – Familiar with C/C++ or Python programming – Graphics and parallel programming skills are plus, but not required

• Topics: – Scientific data models and file formats – Basic data plotting and visualization (Python) – Popular visualization software (ParaView and VTK) – Standard visualization algorithms: volume rendering, isosurface, flow

visualization – Strategies for handling large data – Parallel visualization algorithms – Large scale data analysis and visualization approaches

Class Schedule

• Week 1: – Overview of scientific data visualization – Scientific data model

• Week 2: – Scientific data file formats (VTK, NetCDF, Plot3D) – Data processing and plotting using Python

• Week 3: – Basic visualization techniques – Visualization software: ParaView

Class Schedule • Week 4

– Visualization pipeline – Programming visualization using VTK: C++ or Python

• Week 5/6 – Visualization algorithm I: volume rendering

• Optical Model • Ray casting • Advanced topics in volume rendering

• Week 6/7 – Visualization algorithm II: isosurface

• Marching Cubes • Analysis of isosurfaces (topology, geometry, and advanced search

algorithms)

Class Schedule

• Week 8/9 – Visualization algorithm III: flow visualization

• Particle tracing and numerical integration • Flow texture generation • Advanced flow analysis

• Week 9/10 – Strategies for handling large data sets

• Level of detail • Data partitioning and distribution • Out-of-core processing • Parallel computation • Query-driven processing

Class Schedule

• Week 10/11 – Large scale data visualization using ParaView – Parallel visualization algorithm I

• Overview of MPI • Parallel volume rendering and image composition

• Week 11/12 – Parallel visualization algorithm II

• Parallel flow visualization algorithms – Parallel streamline, stream surface generation

Class Schedule

• Week 12/13: – Advanced visualization topics:

• Time-varying visualization • Query driven visualization

• Week 13/14: – Advanced visualization topics:

• Distributional and information-theoretical based approaches

• Week 14/15: – Student project highlights

Course Grading • Students will be asked to do 1-2 (small) assignments for

each lecture topic – Programming or visualization design with existing software

• One term project, one final project • The term project will ask students to use existing software

and/or additional programming to visualize certain data sets given by the instructor

• The final project will be more open-ended. Students can choose their applications and data to conduct a visualization design and evaluation

• Grading criteria: – Homework: 50% – Term project: 20% – Final Project: 30%

Sample Assignment

• Data model:

Sample Assignment

• NetCDF read and write

Sample Assignment

• Python data processing and plotting

Sample Assignment

• Use ParaView

Sample Course Slides

Scientific Data Model

Han-Wei Shen The Ohio State University

What is a Data Model?

How do you describe the data represented by this image?

Data Model

• Describe the objects represented by the data

Data Model

• Describe the objects represented by the data – Structures of the objects

u

v

Data Model

• Describe the objects represented by the data – Structures of the objects – Properties of the objects

u

v Temperature Pressure Cloud density …

(u,v)

Data Model

• Describe the objects represented by the data – Structures of the objects – Properties of the objects – Relationships between the objects

u

v Temperature Pressure Cloud density …

(u,v)


Temperature Pressure Cloud density …

• Data set – a single or multiple valued function

Data Model

m dependent variables xi (i=1..m) n independent variable vj (j = 1..n)

Each dependent variable yi can have a tensor rank k

– k = 0 : scalar; k = 1: vector; k = 2; 2D matrix, etc.

…


• Independent variables (dimensions)

– Spatial coordinates (longitude, latitude, height)

– Time – Zone ID – …

• Dimensionality - number of independent variables

u

v

• Data set – a single or multiple valued function

Temperature Pressure Cloud density …

(u,v)

• Dependent variables – The function values of

independent variables – The number of values

associated with each dependent variable can be described by its tensor rank

– 0: scalar – 1: vector – 2: n x n matrix …

Scientific Data Set

Scientific Data Set =

u

v

Domain Structure - Topology: property invariant under transformation - Geometry: instantiation of topology with specific positions - Consists of Points and Cells, which define the Mesh

u

v

Attributes One or multiple values (scalars, vectors, tensors) defined at points or cells

Domain Structure + Attributes

Domain Structure - Cell

u

v

u

v

• Cells are the fundamental building blocks of scientific data sets

• Cells define how points are connected together to form the basis for interpolation

• Cells can be of different dimensionality – 0 D: Vertices – 1 D: Line; Polylines; – 2 D: Triangle; Quadrilateral; Polygon – 3 D: Tetrahedron; Hexahedron; Voxel;

Cell Types

Vertices Line Polyline

1D

Triangle Quad Polygon

2D

Tetrahedron Cube Hexahedron Pyramid

3D

Scientific Dataset Types

• Data sets are categorized into different types based on their underlying grid (domain structures)

• Structured Grid

• Unstructured Grid



– Structured Grid – Consisting of a collection of points and cells arranged on a

regular lattice




regular lattice – Every point in the structured grid can be indexed by (i,j) in 2D,

(i,j,k) in 3D, etc.




regular lattice – Every point in the structured grid can be indexed by (i,j) in 2D,

(i,j,k) in 3D, etc. – The position of the points, and hence the geometry of the cells,

can be either implicitly defined (Cartesian gird), or explicitly specified (rectilinear or curvilinear grid)



Cartesian Grid

– Structured Grid • Cartesian mesh



Cartesian Grid

Rectilinear Grid

– Structured Grid • Cartesian mesh • Rectilinear mesh



Cartesian Grid

Rectilinear Grid

Curvilinear Grid

– Structured Grid • Cartesian mesh • Rectilinear mesh • Curvilinear mesh


• Data sets are categorized into different types based on their underlying grid (domain structure) – Unstructured Grid

• Also called irregular grid data



• Also called irregular grid data • Unstructured grid points are irregularly distributed in space



• Also called irregular grid data • Unstructured grid points are irregular located in space • It is often a result of space tessellation with simple shapes



• Also called irregular grid data • Unstructured grid points are irregular located in space • It is often a result of space tessellation with simple shapes • Explicit connectivity information to form cells is

necessary



• Polygonal mesh Polygonal mesh



• Polygonal mesh • Tetrahedral mesh

Polygonal mesh Tetrahedral mesh



• Polygonal mesh • Tetrahedral mesh • Hybrid Mesh

Polygonal mesh Tetrahedral mesh

Hybrid mesh

ncsa blue waters visualization course - osc

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