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-like), resulting in a GPU-accelerated version denoted as 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 (MPI) 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 1.6.0 (EPICC-Model V1.6.0) 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 GPU-like using the compiler optimization option compared to the Fortran HADVPPM baseline compiled option for a data size of 10⁸. Integrating GPU-HADVPPM4HIP V1.0 into EPICC-Model V1.6.0 yielded three distinct versions: the initial HIP-based version (HIP-Ori), a version optimized for CPU and GPU communication frequency (HIP‑Opt1), and a further-optimized version employing a thread‑block coordinated indexing strategy (HIP‑Opt2). Compared to the HIP‑Ori version, HIP‑Opt1 achieved a model‑level computational efficiency improvement of 17.0x. Building upon HIP‑Opt1, HIP‑Opt2 delivered an additional 1.5x enhancement in computational efficiency. At the module level, including CPU and GPU data transfer overhead, the GPU implementation improves computational efficiency of the advection module by 39.3 %; when communication cost is excluded, the advection module attains a 20.5× acceleration relative to its CPU counterpart. This coupling establishes a foundational framework for adapting air quality models to 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.