Large Data Visualization on Distributed Memory Multi-GPU Clusters

dc.contributor.authorFogal, Thomasen_US
dc.contributor.authorChilds, Hanken_US
dc.contributor.authorShankar, Siddharthen_US
dc.contributor.authorKrüger, Jensen_US
dc.contributor.authorBergeron, R. Danielen_US
dc.contributor.authorHatcher, Philipen_US
dc.contributor.editorMichael Doggett and Samuli Laine and Warren Hunten_US
dc.date.accessioned2013-10-28T10:21:24Z
dc.date.available2013-10-28T10:21:24Z
dc.date.issued2010en_US
dc.description.abstractData 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.en_US
dc.description.seriesinformationHigh Performance Graphicsen_US
dc.identifier.isbn978-3-905674-26-2en_US
dc.identifier.issn2079-8687en_US
dc.identifier.urihttps://doi.org/10.2312/EGGH/HPG10/057-066en_US
dc.publisherThe Eurographics Associationen_US
dc.titleLarge Data Visualization on Distributed Memory Multi-GPU Clustersen_US
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