A SCALABLE HYBRID CPU-GPU COMPUTATIONAL FRAMEWORK FOR A FINITE ELEMENT-BASED AIR QUALITY MODEL

We propose a scalable computational framework for the hybrid CPU-GPU implementation ofa traffic-induced and finite element-based air quality model. The hybrid computing paradigm we investigate consists in combining the CPU-based distributed-memory programming approach using Message Passing Interface (MPI) and a GPU programming model for the finite element numerical integration using Compute Unified Device Architecture (CUDA), a general purpose parallel computing platform released by NVIDIA Corporation and featured on its own GPUs. The scalability results obtained from numerical experiments on two major road traffic-induced air pollutants, namely the fine and inhalable particulate matter PM$_{2.5}$ and PM$_{10}$, are illustrated. These achievements, including speedup and efficiency analyses, support that this framework scales well up to 256 CPU cores used concurrently with GPUs from a hybrid computing system.