Progressive Visualization of Large Data Sets. Aim: Introduction: ViSUS: Volume Renderer: 1 Abhishek Tripathi (U )

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1 1 Abhishek Tripathi (U ) Progressive Visualization of Large Data Sets Aim: The project aims at effectively visualizing very large data sets, typically, above the Gigabyte range.such data sets are hard to visualize on Desktop environments as they generally do not fit into the memory available on Desktop workstations. The project utilizes the out of core technique incorporated into the ViSUS I/O infrastructure and creates a volume rendering of the data sets. The project also aims at visualizing large data progressively, i.e. the data can be viewed at different resolutions. This facilitates the visualization of large data sets on Desktop environments. The project can be divided into the following phases: 1. Creating an interface to utilize the I/O infrastructure of ViSUS. 2. Creating a Volume rendering infrastructure. 3. Integrating the I/O interface and the Volume Renderer to visualize large data sets. Introduction: The project utilizes the following infrastructures to implement the visualizations of large scientific datasets: ViSUS: The ViSUS project develops data streaming techniques for progressive processing and visualization of large scientific datasets. The strategy is to exploit the coupling between time- critical algorithms and progressive multi- resolution data- structures to realize an end- to- end optimized flow of data from the original source, such as remote storage or large scientific simulation, to the rendering hardware. The implementation of this approach enables three major visualization modalities. (i) Interactive visualization on high resolution power- walls. (ii) Interactive visualization on desktop workstations of large datasets that cannot be stored locally. (iii) Immediate monitoring of remote simulations from a desktop workstation. These modalities target multiple phases in the process of generating and exploring very large simulation datasets where real- time user interaction can increase the productivity of scientists. Volume Renderer: The homegrown Volume rendering library, VolRend, developed at Lawrence Livermore National Laboratory utilizes OpenGL infrastructure and multithreading to create the infrastructure needed to

2 2 Abhishek Tripathi (U ) Volume render raw data sets. The library maps 2D /3D textures depending on the Look Up Tables supplied by the user. These textures are used to create the volume rendering effect. Implementation: The implementation consists of the following phases: Phase I: Building an interface for interacting with the ViSUS I/O infrastructure.the interface provides users a simple approach to read and write data without getting into the intricacies of ViSUS. The users can read and write data as simple raw data blocks. The interface also allows users to query the data at a particular resolution. For large datasets this becomes necessary as the data at full resolution cannot be stored locally in the memory. This enables users to visualize the whole dataset at a lower resolution and get a high resolution view of the particular parts of interest. As part of the implementation of the interface a header file was created. The header file can be included in the implementation of the viewer to get data from the large data sets. Phase II Volume Rendering: The VolRend Library was used to create the basic infrastructure needed to perform volume rendering of the data. The library maps 2D/3D textures for the data to create render the volume. The library takes as input a look up table containing red, green, blue and alpha values to decide the color in the texture. The library takes the raw data and the dimensions of x, y, z as input. It then creates the textures to be mapped to the volume. The VolRend Library also uses multi- threading to accelerate the texture mapping and preparation for volume rendering. Phase III This phase includes the integration of the ViSUS I/O infrastructure with the Volume rendering library. This phase consists of the following parts:

3 3 Abhishek Tripathi (U ) Interface for the viewer: The viewer is implemented using OpenGL. It includes the basic functionality of the viewer. The Interface is used to create the rendering window, adding interactive functionalities such as panning, moving and rotating the rendering. It also has functionalities to get data at different resolutions. Transfer function: The transfer function used for this viewer is a 2D grayscale map. The color of the scalar is determined by the value of r, g, b and a based on the lookup table. Data Access: The raw data is queried from the IDX files using the ViSUS I/O infrastructure and served to the volume renderer. IDX infrastructure provides mechanism to fetch the data at different resolutions. This mechanism is leveraged upon in order to visualize the data sets effectively while keeping the viewer interactive. The user can select the resolution at which they want to see the volume being rendered. The viewer also gives the flexibility to view a part or whole of the dataset. The viewing volume can be moved around to help visualize the part of interest at the desired resolution. Results:

4 4 Abhishek Tripathi (U ) Volume rendering of Nucleon Dataset at full resolution Moving the volume to get another view of the data

5 5 Abhishek Tripathi (U ) Volume Rendering of Nucleon Dataset at lower resolution Iso surface rendering of Nucleon Dataset Volume Rendering of Combustion dataset at lower resolution

6 6 Abhishek Tripathi (U ) Limitations /Future Work: Limitations: Transfer Function: Currently the transfer function is fixed. Future Work: User Controlled Transfer function Include functionality for time- varying data Remote visualization Hardware based volume rendering