dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
Providence |
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dc.citation.endPage |
128 |
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dc.citation.startPage |
127 |
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dc.citation.title |
1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 |
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dc.contributor.author |
Jeong, Won-Ki |
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dc.contributor.author |
Beyer, Johanna |
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dc.contributor.author |
Hadwiger, Markus |
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dc.contributor.author |
Schneider, Jens |
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dc.contributor.author |
Pfister, Hanspeter |
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dc.date.accessioned |
2023-12-20T02:38:00Z |
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dc.date.available |
2023-12-20T02:38:00Z |
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dc.date.created |
2015-07-01 |
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dc.date.issued |
2011-10-23 |
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dc.description.abstract |
Table 1 illustrates the impact of different distribution unit sizes, different screen resolutions, and numbers of GPU nodes. We use two and four GPUs (NVIDIA Quadro 5000 with 2.5 GB memory) and a mouse cortex EM dataset (see Figure 2) of resolution 21,494 x 25,790 x 1,850 = 955GB. The size of the virtual distribution units significantly influences the data distribution between nodes. Small distribution units result in a high depth complexity for compositing. Large distribution units lead to a low utilization of GPUs, because in the worst case only a single distribution unit will be in view, which is rendered by only a single node. The choice of an optimal distribution unit size depends on three major factors: the output screen resolution, the block cache size on each node, and the number of nodes. Currently, we are working on optimizing the compositing step and network communication between nodes. |
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dc.identifier.bibliographicCitation |
1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011, pp.127 - 128 |
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dc.identifier.doi |
10.1109/LDAV.2011.6092332 |
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dc.identifier.scopusid |
2-s2.0-84055192822 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/46809 |
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dc.identifier.url |
https://ieeexplore.ieee.org/document/6092332 |
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dc.language |
영어 |
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dc.publisher |
1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 |
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dc.title |
Distributed terascale volume visualization using distributed shared virtual memory |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2011-10-23 |
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