SIParCS 2018- Julia Piscioniere

Julia Piscioniere

Julia Piscioniere, College of Charleston

Evaluating the Performance of Large Scale Data Assimilation in Modern Geophysical Models

Recorded Talk

This project focused on the optimization of DART: Data Assimilation Research Testbed. Data assimilation is when a numerical model uses observations to produce more accurate forecasts. The original DART code processes one observation at a time, updating the model states after each observation. The optimized code uses a graph coloring algorithm, so observations are put in independent groups (colors). These groupings enable us to reduce communication time by processing multiple observations at a time. My project was to run comparisons of the two codes; we put timers in the code to collect data on how the graph code scales in comparison to the original code. I ran the comparisons on Cheyenne using a bash script that looped through different variable values and ran DART with those specific parameters, then output timer files. The graph code, using pre-calculated colors, has consistently faster performance compared to the original code. It also scales well up to 256 nodes on a one-degree CAM case, does better with a higher number of ensemble members, and decreases the broadcast time by an average of about 90%.

Mentors: John Dennis, Brian Dobbins