Authors: Schwarz, N.

Publication: Thesis in partial fulfillment of the requirement for the degree of Master of Science in Computer Science, University of Illinois at Chicago, Chicago, IL

This thesis presents a methodology for rendering very large volume data on scalable high-resolution displays using a distributed-memory cluster.

The methodology uses a multi-resolution octree, an image-order data distribution method, a distributed shared-memory data management system, a multi-level cache, and hardware accelerated rendering techniques to produce a solution that is scalable in terms of input size and output resolution.

An analytical cost model validated by experimental results predicts the system’s behavior. The methodology’s usefulness is demonstrated with a number of domain specific datasets.

The primary contributions of this thesis include:

1. A review of research in the field of volume rendering and parallel volume rendering.
2. A methodology for rendering very large volume data on scalable high-resolution displays using a commodity distributed-memory cluster of computers that scales with the size of input data and the output resolution.
3. An analytical model validated by experimental results that predicts the methodology’s behavior.
4. The application of this methodology to domain specific problems in the fields of bioscience, geoscience and medicine.

Date: October 29, 2007

Document: View PDF

Related Entries

Directory:

• Nicholas Schwarz

Events:

• Nicholas Schwarz’s Thesis Defense

Research:

• Distributed Volume Rendering of Very Large Data on High-Resolution Scalable Displays

Related Categories

• Applications
• MS / PhD Thesis
• Software
• Visualization