TY - GEN
T1 - Distributed terascale volume visualization using distributed shared virtual memory
AU - Beyer, Johanna
AU - Hadwiger, Markus
AU - Schneider, Jens
AU - Jeong, Won Ki
AU - Pfister, Hanspeter
PY - 2011
Y1 - 2011
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.
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.
UR - https://www.scopus.com/pages/publications/84055192822
U2 - 10.1109/LDAV.2011.6092332
DO - 10.1109/LDAV.2011.6092332
M3 - Conference contribution
AN - SCOPUS:84055192822
SN - 9781467301541
T3 - 1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings
SP - 127
EP - 128
BT - 1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011 - Proceedings
T2 - 1st IEEE Symposium on Large-Scale Data Analysis and Visualization 2011, LDAV 2011
Y2 - 23 October 2011 through 24 October 2011
ER -