| Publication Type | poster |
| School or College | College of Engineering |
| Department | Kahlert School of Computing |
| Creator | Pascucci, Valerio; Swank, William Lorenzo |
| Title | Scalable scientific data |
| Date | 2010-02-26 |
| Description | Question Hierarchial Z-Order Evaluation How can we present hundreds or thousands of gigabytes of scientific data to a user for analysis and interpretation? • The Scientific Computing and Imaging Institute is responsible for helping scientists visualize massive amounts of data. • Sources of large scientific data include medical imaging equipment (CAT, PET, MRI, etc.), fluid dynamics simulations, and genetic sequence mapping • Some of these simulations produce hundreds of gigabytes of data per simulation time step. Evaluating the speed of loading a set of random samples from an 8GB 3D image showed that: •Both Z and HZ-order significantly outperform the standard Row Major mode representation •HZ-order also outperforms Z-order for progressive requests Based on the Lebesque curve • Indexes Z-curve resolution levels in hierarchical order from coarser to finer. • Maintains the same geometric locality for each Z-curve resolution level • Beneficial for progressive resolution requests. (e.g. an "object search" application may first attempt to perform filtering on a coarser resolution) |
| Type | Text |
| Publisher | University of Utah |
| Language | eng |
| Bibliographic Citation | Pascucci, V., & Swank, W. L. (2010). Scalable scientific data. University of Utah. |
| Rights Management | ©Valerio Pascucci, William Lorenzo Swank |
| Format Medium | application/pdf |
| Format Extent | 2,739,048 bytes |
| Identifier | ir-main/14962 |
| ARK | ark:/87278/s6000kt9 |
| Setname | ir_uspace |
| ID | 707637 |
| Reference URL | https://collections.lib.utah.edu/ark:/87278/s6000kt9 |