Enhancing the advection module performance in the EPICC-Model V1.6.0 via GPU-HADVPPM4HIP V1.0 coupling and GPU-optimized strategies
- 1Beijing Normal University (Beijing, China)
- 2Institute of Atmospheric Physics (Beijing, China)
- 3University of Chinese Academy of Sciences (Beijing, China)
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, for a one-dimensional input array containing 10 ⁸ double-precision floating-point values, GPU-HADVPPM4HIP V1.0 compiled with the −O3 optimization option achieved a maximum speedup of 556.5× on a GPU-like accelerator relative to the Fortran HADVPPM implementation compiled with the −O0 option as the baseline. 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 17.0×. Building upon HIP-Opt1, HIP-Opt2 delivered an additional 1.5× 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.
Versions
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BibTeX
@article{cao2026enhancing,
title = {{Enhancing the advection module performance in the EPICC-Model V1.6.0 via GPU-HADVPPM4HIP V1.0 coupling and GPU-optimized strategies}},
author = {Kai Cao and Qizhong Wu and Xiao Tang and Jinxi Li and Xueshun Chen and Huansheng Chen and Wending Wang and Huangjian Wu and Lei Kong and Jie Li and Jiang Zhu and Zifa Wang},
journal = {Geoscientific Model Development},
year = {2026},
month = oct,
doi = {10.5194/gmd-19-9807-2026},
url = {https://gmd.copernicus.org/articles/19/9807/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Enhancing the advection module performance in the EPICC-Model V1.6.0 via GPU-HADVPPM4HIP V1.0 coupling and GPU-optimized strategies
AU - Kai Cao
AU - Qizhong Wu
AU - Xiao Tang
AU - Jinxi Li
AU - Xueshun Chen
AU - Huansheng Chen
AU - Wending Wang
AU - Huangjian Wu
AU - Lei Kong
AU - Jie Li
AU - Jiang Zhu
AU - Zifa Wang
PY - 2026
DA - 2026/10/13
JO - Geoscientific Model Development
DO - 10.5194/gmd-19-9807-2026
UR - https://gmd.copernicus.org/articles/19/9807/2026/
AB - 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, for a one-dimensional input array containing 10 ⁸ double-precision floating-point values, GPU-HADVPPM4HIP V1.0 compiled with the −O3 optimization option achieved a maximum speedup of 556.5× on a GPU-like accelerator relative to the Fortran HADVPPM implementation compiled with the −O0 option as the baseline. 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 17.0×. Building upon HIP-Opt1, HIP-Opt2 delivered an additional 1.5× 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.
SN - 1991-9603
ER -