Integrated parallelization of computation and visualization for large-scale weather applications.

Preeti Malakar.
Dissertation Research Showcase, SC 2012.


Critical applications like cyclone tracking require simultaneous high-performance simulations and online visualization for timely analysis. These simulations involve large-scale computations and generate large amount of data. Faster simulations and simultaneous visualization enable scientists provide real-time guidance to decision makers. However, resource constraints like limited storage and slow networks can limit the effectiveness of on-the-fly visualization. We have developed an integrated user-driven and automated steering framework InSt that simultaneously performs simulations and efficient online remote visualization of critical weather applications in resource-constrained environments. InSt considers application dynamics like the criticality of the application and resource dynamics like the storage space and network bandwidth to adapt various application and resource parameters like simulation resolution and frequency of visualization. We propose adaptive algorithms to reduce the lag between the simulation and visualization times. We also improve the performance of multiple high-resolution nested simulations like simulations of multiple weather phenomena, which are computationally expensive.