Accelerated Prediction of the Polar Ice and Global Ocean (APPIGO)

Lead PI: Dr. Eric Chassignet, Florida State University
Start Year: 2014 | Duration: 5 Years
Partners: Florida State University, LANL, NRL, Stennis Space Center & University of Miami


Arctic change and reductions in sea ice are impacting Arctic communities and are leading to increased commercial activity in the Arctic. Improved forecasts will be needed at a variety of timescales to support Arctic operations and infrastructure decisions. Increased resolution and ensemble forecasts will require significant computational capability. At the same time, high performance computing architectures are changing in response to power and cooling limitations, adding more cores per chip and using Graphics Processing Units (GPUs) as computational accelerators. We describe here an effort to improve Arctic forecast capability by modifying component models to better utilize new computational architectures. Specifically, we will focus on the Los Alamos Sea Ice Model (CICE), the HYbrid Coordinate Ocean Model (HYCOM) and the Wavewatch III models and optimize each model on both GPU-accelerated and MIC-based architectures. These codes form the ocean and sea ice components of the Navy’s Arctic Cap Nowcast/Forecast System (ACNFS) and the Navy Global Ocean Forecasting System (GOFS), with the latter scheduled to include a coupled Wavewatch III by 2016. An incremental acceleration approach will begin by improving selected sections of each code and expanding those regions to accelerate the three application codes [OpenACC, OpenCL, supplemented for functionality with CUDA Fortran as needed]. This approach provides early successes and opportunities to test the changes as they are made. A second approach will redesign code infrastructure to incorporate a multi-level parallelism by design. The modified codes will be validated both on a single component basis and within the forecast systems. This work will contribute to improved Arctic forecasts and the Arctic ice prediction demonstration project for the Earth System Prediction Capability (ESPC).


Annual PI Reports:
FY 2015 PI Report
FY 2016 PI Report