GPU Acceleration of CALPUFF

Abstract

Weather and climate prediction, as well as air quality software models are very computing intensive, requiring high processing power in order to achieve precise results in a reasonable time. For many years, performance improvement has come from increasing processors speed. However, processor speed cannot be indefinitely increased. An alternative strategy for increasing performance is through the use of large-scale parallelism architectures. While recent models show the benefits of parallel computing, multicore systems or cluster may be cost ineffective in certain scenarios. CUDA (Compute Unified Device Architecture) is a general purpose parallel architecture introduced by NVIDIA®. CUDAenabled graphics processing units have hundreds of cores that can collectively run thousands of threads at a fraction of a cost of other parallel computer classes. This paper shows the performance improvement achievable in CALPUFF, an advanced nonsteady-state meteorological and air quality modeling system, through parallelization and CUDA computing architecture. A runtime analysis of the model was conducted in order to find a candidate module for parallelization. Results from the optimized version are compared to those from original serial version of CALPUFF for error analysis.