T. Peterka, W. Kendall, D. Goodell, B. Nouanesengsey, H.-W. Shen, J. Huang, K. Moreland, R. Thakur, and R. B. Ross, "Performance of Communication Patterns for Extreme-Scale Analysis and Visualization," Preprint ANL/MCS-P1774-0610, June 2010. [pdf]
Efficient data movement is essential for extreme-scale parallel visualization and analysis algorithms. In this research, we benchmark and optimize the performance of collective and point-to-point communication patterns for data-parallel visualization of scalar and vector data. Two such communication patterns are global reduction and local nearest-neighbor communication. We implement scalable algorithms at tens of thousands of processes, in some cases to the full scale of leadership computing facilities, and benchmark performance using large-scale scientific data.