EPICC-Model V1.6.0 Adveksiyon Modülü Performansının GPU-HADVPPM4HIP V1.0 Eşleştirmesi ve GPU Optimize Stratejileri ile İyileştirilmesi

Özet (abstract)

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.

Yazarların özeti; kaynağından alınmıştır. Geoscientific model development, 2026 · DOI ↗