TY - GEN
T1 - Visualizing large 3D geodesic grid data with massively distributed GPUs
AU - Xie, Jinrong
AU - Yu, Hongfeng
AU - Maz, Kwan Liu
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/16
Y1 - 2014/1/16
N2 - Geodesic grids become increasingly prevalent in large weather and climate applications. The deluge amount of simulation data demands efficient and scalable visualization capabilities for scientific exploration and understanding. Given the unique characteristics of geodesic grids, no current techniques can scalably visualize scalar fields defined on a geodesic grid. In this paper, we present a new parallel ray-casting algorithm for large geodesic grids using massively distributed GPUs. We construct a spherical quadtree to adaptively partition and distribute the data according to the grid resolution of simulation, and ensure a balanced workload assignment over a large number of processors from different view angles. We have designed and implemented the entire rendering pipeline based on the MPI and CUDA architecture, and demonstrated the effectiveness and scalability of our approach using an example of large application on a supercomputer with thousands of GPUs.
AB - Geodesic grids become increasingly prevalent in large weather and climate applications. The deluge amount of simulation data demands efficient and scalable visualization capabilities for scientific exploration and understanding. Given the unique characteristics of geodesic grids, no current techniques can scalably visualize scalar fields defined on a geodesic grid. In this paper, we present a new parallel ray-casting algorithm for large geodesic grids using massively distributed GPUs. We construct a spherical quadtree to adaptively partition and distribute the data according to the grid resolution of simulation, and ensure a balanced workload assignment over a large number of processors from different view angles. We have designed and implemented the entire rendering pipeline based on the MPI and CUDA architecture, and demonstrated the effectiveness and scalability of our approach using an example of large application on a supercomputer with thousands of GPUs.
UR - http://www.scopus.com/inward/record.url?scp=84946685182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946685182&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2014.7013198
DO - 10.1109/LDAV.2014.7013198
M3 - Conference contribution
AN - SCOPUS:84946685182
T3 - IEEE Symposium on Large Data Analysis and Visualization 2014, LDAV 2014 - Proceedings
SP - 3
EP - 10
BT - IEEE Symposium on Large Data Analysis and Visualization 2014, LDAV 2014 - Proceedings
A2 - Childs, Hank
A2 - Childs, Hank
A2 - Pajarola, Renato
A2 - Vishwanath, Venkatram
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2014
Y2 - 9 October 2014 through 10 October 2014
ER -