Shared Memory and GPU Parallelization of an Operational Atmospheric Transport and Dispersion Application

The HYSPLIT air concentration model is an operational Lagrangian trajectory and dispersion model that calculates the concentration or the distribution of pollutants by releasing and tracking particles or puffs. It is widely used for tracking and forecasting the release of pollutants such as radioactive material, dust storms and volcanic ash. Due to the massively parallel nature of the particle tracking system, the HYSPLIT model shows potential to be accelerated by multiple CPUs and GPUs. However, porting such a legacy application to a GPU requires non-trivial work to isolate the parallel code regions and correctly adapt the code to a parallel programming model. This paper presents methods to port the current HYSPLIT code to the shared-memory OpenMP and the CUDA programming models. In the OpenMP approach, the original HYSPLIT concentration model is analyzed, profiled and refactored to remove barriers for parallelizing on multi-core CPUs. A linear speed-up with some serial overhead is achieved for the OpenMP version. In the CUDA approach, we utilize the computing power of NVIDIA GTX960 and Tesla P100 GPUs, providing 4-5× faster overall performance than the original. With the help of GPU coarse-grained parallelism, a maximum 12.9× speedup has been measured, compared to the original program running on a CPU. The disturbance of different parallelization approaches to the original code base shows that most of the highly difficult work is in the refactoring, and that CUDA requires extensive, but mostly relatively shallow, changes.