Enhancing the advection module performance in the EPICC-Model V1.6.0 via GPU-HADVPPM4HIP V1.0 coupling and GPU-optimized strategies

Abstract. The rapid development of Graphics Processing Units (GPUs) has established new computational paradigms for enhancing air quality modeling efficiency. In this study, the heterogeneous-compute interface for portability (HIP) was implemented to parallel computing of the piecewise parabolic method (PPM) advection solver (HADVPPM) on China’s domestic GPU-like accelerators (GPU-HADVPPM4HIP V1.0). Computational performance was enhanced through three strategic optimizations: reducing the central processing unit (CPU) and GPU (CPU-GPU) data transfer frequency, thread-block coordinated indexing, and the Message Passing Interface and HIP (“MPI+HIP”) hybrid parallelization across heterogeneous computing clusters. Following validation of the GPU-HADVPPM4HIP V1.0 program’s offline computational consistency and the pollutant simulation performance of the Emission and atmospheric Processes Integrated and Coupled Community version 1.0 (EPICC-Model V1.0) on the Earth System Numerical Simulation Facility (EarthLab), comprehensive performance testing was conducted. Offline benchmark results demonstrated that GPU-HADVPPM4HIP V1.0 achieved a maximum speedup of 556.5x on a domestic GPU-like accelerator with compiler optimization. Integration of GPU-HADVPPM4HIP V1.0 into EPICC-Model V1.0, combined with optimized CPU-GPU communication frequency and thread-block coordinated indexing strategies, yielded model-level computational efficiency improvements of 17.0x and 1.5x, respectively. At the module level, GPU-HADVPPM4HIP V1.0 exhibited a 39.3 % computational efficiency gain when accounting for CPU-GPU data transfer overhead, which escalated to 20.5x acceleration when excluding communication costs. This coupling establishes a foundational framework for adapting air quality models to China’s domestic GPU-like architectures and identifies critical optimization pathways. Moreover, the methodology provides essential technical support for achieving full-model GPU implementation of the EPICC-Model, addressing both current computational constraints and future demands for high-resolution air quality simulations.