Large Data Visualization on Distributed Memory Multi-GPU Clusters
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Date
2010
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets.
Description
@inproceedings{:10.2312/EGGH/HPG10/057-066,
booktitle = {High Performance Graphics},
editor = {Michael Doggett and Samuli Laine and Warren Hunt},
title = {{Large Data Visualization on Distributed Memory Multi-GPU Clusters}},
author = {Fogal, Thomas and Childs, Hank and Shankar, Siddharth and Krüger, Jens and Bergeron, R. Daniel and Hatcher, Philip},
year = {2010},
publisher = {The Eurographics Association},
ISSN = {2079-8687},
ISBN = {978-3-905674-26-2},
DOI = {/10.2312/EGGH/HPG10/057-066}
}