Air pollution modelling using a graphics processing unit with CUDA

TL;DR

This paper demonstrates that using CUDA-enabled GPUs significantly accelerates environmental pollution modeling, specifically radionuclide transport simulations, with minimal differences compared to CPU results, enabling faster decision-making tools.

Contribution

First application of GPU with CUDA for environmental radionuclide transport modeling, achieving 80-120x speedup over CPU with comparable accuracy.

Findings

GPU implementation achieves 80-120x speedup.

Results are comparable to CPU simulations with minimal differences.

Potential for widespread environmental application due to high speed and low cost.

Abstract

The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) - a parallel computing architecture - has been developed by NVIDIA to utilize this performance in general purpose computations. Here we show for the first time a possible application of GPU for environmental studies serving as a basement for decision making strategies. A stochastic Lagrangian particle model has been developed on CUDA to estimate the transport and the transformation of the radionuclides from a single point source during an accidental release. Our results show that parallel implementation achieves typical acceleration values in the order of 80-120 times compared to CPU using a single-threaded implementation on a 2.33 GHz desktop computer. Only very small differences have been found between the results obtained from GPU and CPU simulations, which are comparable with the effect of stochastic transport phenomena in atmosphere. The relatively high speedup with no additional costs to maintain this parallel architecture could result in a wide usage of GPU for diversified environmental applications in the near future.