R. L. Jacob, J. Krishna, X. Xu, S. Mickelson, T. Tautges, M. Wilde, R. Latham, I. Foster, R. Ross, M. Hereld, J. Larson, P. Bochev, K. Peterson, M. Taylor, K. Schuchardt, J. Yin, D. Middleton, M. Haley, D. Brown, R. Brownrigg, W. Huang, D. Shea, M. Vertenstein, K. Ma, J. Xie, "Bringing Task and Data Parallelism to Analysis of Climate Model Output," Preprint ANL/MCS-P4003-1212, December 2012. [pdf]
Climate models are both outputting larger and larger amounts of data and are doing it on more sophisticated numerical grids. The tools climate scientists have used to analyze climate output, an essential component of climate modeling, are single threaded and assume rectangular structured grids in their analysis algorithms. We are bringing both task- and data parallelism to the analysis of climate model output. We have created a new data-parallel library, the Parallel Gridded Analysis Library (ParGAL) which can read in data using parallel I/O, store the data on a compete representation of the structured or unstructured mesh and perform sophisticated analysis on the data in parallel. ParGAL has been used to create a parallel version of a script-based analysis and visualization package. Finally, we have also taken current workflows and employed task-based parallelism to decrease the total execution time.