Publication Type |
poster |
School or College |
Scientific Computing and Imaging Institute |
Department |
Computing, School of |
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; Image |
Publisher |
University of Utah |
Language |
eng |
Bibliographic Citation |
Pascucci, V., & Swank, W. L. (2010). Scalable scientific data. University of Utah. |
Rights Management |
(c)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 |