TY - GEN
T1 - Distributed terascale volume visualization using distributed shared virtual memory
AU - Beyer, Johanna
AU - Hadwiger, Markus
AU - Schneider, Jens
AU - Jeong, Wonki
AU - Pfister, Hanspeter
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2011/10
Y1 - 2011/10
N2 - 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. © 2011 IEEE.
AB - 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. © 2011 IEEE.
UR - http://hdl.handle.net/10754/575803
UR - http://ieeexplore.ieee.org/document/6092332/
UR - http://www.scopus.com/inward/record.url?scp=84055192822&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2011.6092332
DO - 10.1109/LDAV.2011.6092332
M3 - Conference contribution
SN - 9781467301541
SP - 127
EP - 128
BT - 2011 IEEE Symposium on Large Data Analysis and Visualization
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -