Porting and Parallel Optimization of the Gas-Phase Chemistry Module of the Air Quality Model EPICC-Model on China’s Domestic Accelerators

doi:10.11871/jfdc.issn.2096-742X.2025.05.010

Abstract

Abstract:

[Objective] To address the consistency and stability of cross-architecture computing in the EPICC-Model air quality model and enhance its parallel computing efficiency, this study focuses on the model's hotspot module, namely the gas-phase chemistry module, and explores the porting and parallel optimization on domestic accelerators. [Methods] A fourth-order implicit Rosenbrock solver is used to solve the system of ordinary differential equations governing chemical reaction kinetics, with embedded third-order methods for error estimation. The porting of the gas-phase chemistry module onto domestic accelerators is implemented using C language and the HIP heterogeneous programming technology. Further optimization is achieved through strategies such as merged global memory access, computational optimizations, and “core-card” collaborative computing, aiming to improve the computational efficiency and parallel scalability of the EPICC-Model on domestic heterogeneous clusters. [Results] Compared to the elapsed time on a single-core and 32-core domestic CPU processor, the computational efficiency of the gas-phase chemistry module on the domestic accelerator increases by 61.2 times and 3.0 times, respectively. When coupled into the EPICC-Model, the issue of uneven model computational load is alleviated, and the total model computation time is reduced by more than 45% across different “core-card” configurations. [Limitations] Future work will focus on further improving the communication efficiency between the CPU processor and accelerator, increasing the code coverage of the EPICC-Model on domestic accelerators, and enabling parallel computing for other hotspot modules of the model, even the entire integration module excluding I/O. [Conclusions] The domestic accelerator significantly enhances the computational efficiency of the gas-phase chemistry module, laying a foundation for high-resolution air quality simulations and forecasts in the future.

Key words: air quality model, EPICC-Model, gas-phase chemistry module, domestic accelerators, heterogeneous porting, parallel computing

CAO Kai,TANG Xiao,CHEN Huansheng,MA Jingang,WU Qizhong,WANG Wending,CHEN Xueshun,LI Jinxi,WANG Zifa. Porting and Parallel Optimization of the Gas-Phase Chemistry Module of the Air Quality Model EPICC-Model on China’s Domestic Accelerators[J]. Frontiers of Data and Computing, 2025, 7(5): 123-137, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2025.05.010.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Table 2

Fig.5

Fig.6

Fig.7

Table 3

Fig.8

Fig.9

Fig.10

References 31

[1]	TOP500. El Capitan achieves top spot, Frontier and Aurora follow behind[EB/OL]. (2024-11-18). https://www.top500.org/lists/top500/2024/11/.
[2]	AMD. AMD Instinct™ MI300A Accelerators[EB/OL]. 2024, https://www.amd.com/en/products/accelerators/instinct/mi300/mi300a.html.
[3]	LINFORD J C, MICHALAKES J, VACHHARAJANI M, et al. Automatic Generation of Multicore Chemical Kernels[J]. IEEE Transactions on Parallel and Distributed Systems, 2011, 22(1): 119-131.
[4]	CAO K, WU Q, WANG L, et al. GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)[J]. Geoscientific Model Development, 2023, 16(15): 4 367-4383.
[5]	CAO K, WU Q, WANG L, et al. GPUHADVPPM-4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China’s domestic GPU-like accelerator[J]. Geoscientific Model Development, 2024, 17(17): 6 887-6901.
[6]	GROUP E-M W. Source code and input data of EPICC-Model[CP/DK]. https://earthlab.iap.ac.cn/re-sdown/info_388.html.
[7]	WALCEK C J, ALEKSIC N M. A simple but accurate mass conservative, peak-preserving, mixing ratio bounded advection algorithm with FORTRAN code[J]. Atmospheric Environment, 1998, 32(22): 3863-3880.
[8]	COLELLA P, WOODWARD P R. The Piecewise Parabolic Method (PPM) for gas-dynamical simulations[J]. Journal of Computational Physics, 1984, 54(1): 174-201.
[9]	WESELY M L. Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models[J]. Atmospheric Environment (1967), 1989, 23(6): 1293-1304.
[10]	ZHANG L, BROOK J R, VET R. A revised parameterization for gaseous dry deposition in air-quality models[J]. Atmospheric Chemistry and Physics, 20 03, 3(6): 2067-2082.
[11]	ZAVERI R A, PETERS L K. A new lumped structure photochemical mechanism for large-scale applicatio-ns[J]. Journal of Geophysical Research: Atmosph-eres, 1999, 104(D23): 30387-30415.
[12]	GREG YARWOOD Y S A R B. Impact of CB6r5 Mechanism Changes on Air Pollutant Modeling in Texas[R]. 2020.
[13]	CHANG J S, BROST R A, ISAKSEN I S A, et al. A three-dimensional Eulerian acid deposition model: Physical concepts and formulation[J]. Journal of Geophysical Research: Atmospheres, 1987, 92(D12): 14 681-14700.
[14]	GE B Z, WANG Z F, XU X B, et al. Wet deposition of acidifying substances in different regions of China and the rest of East Asia: Modeling with updated NAQPMS[J]. Environmental Pollution, 2014, 187: 10-21. doi: 10.1016/j.envpol.2013.12.014 pmid: 24418974
[15]	STRADER R, LURMANN F, PANDIS S N. Evaluation of secondary organic aerosol formation in winter[J]. Atmospheric Environment, 1999, 33(29): 4849-4863.
[16]	LANE T E, DONAHUE N M, PANDIS S N. Simulating secondary organic aerosol formation using the volatility basis-set approach in a chemical transport model[J]. Atmospheric Environment, 2008, 42(32): 7439-7451.
[17]	NENES A, PANDIS S N, PILINIS C. ISORROPIA: A New Thermodynamic Equilibrium Model for Multiphase Multicomponent Inorganic Aerosols[J]. Aqu-atic Geochemistry, 1998, 4(1): 123-152.
[18]	LI J, WANG Z, ZHUANG G, et al. Mixing of Asian mineral dust with anthropogenic pollutants over East Asia: a model case study of a super-duststorm in March 2010[J]. Atmospheric Chemistry and Physics, 2012, 12(16): 7591-7607.
[19]	ZHANG J, LIAN C, WANG W, et al. Amplified role of potential HONO sources in O3 formation in North China Plain during autumn haze aggravating processes[J]. Atmospheric Chemistry and Physics, 2022, 22(5): 3275-3302.
[20]	JACOB D J. Heterogeneous chemistry and tropospheric ozone[J]. Atmospheric Environment, 2000, 34(12): 2131-2159.
[21]	LI J, CHEN X, WANG Z, et al. Radiative and heterogeneous chemical effects of aerosols on ozone and inorganic aerosols over East Asia[J]. Science of The Total Environment, 2018, 622-623: 1327-1342.
[22]	YANG J, QU Y, CHEN Y, et al. Dominant physical and chemical processes impacting nitrate in Shandong of the North China Plain during winter haze events[J]. Science of The Total Environment, 2024, 912: 169065.
[23]	Z. JACOBSON M, TURCO R P. SMVGEAR: A sparse-matrix, vectorized gear code for atmospheric models[J]. Atmospheric Environment, 1994, 28(2): 2 73-284.
[24]	ELLER P, SINGH K, SANDU A, et al. Implementation and evaluation of an array of chemical solvers in the Global Chemical Transport Model GEOSCh-em[J]. Geoscientific Model Development, 2009, 2(2): 89-96.
[25]	LI M, LIU H, GENG G, et al. Anthropogenic emission inventories in China: a review[J]. National Science Review, 2017, 4(6): 834-866.
[26]	SKAMAROCK W, KLEMP J, DUDHIA J, et al. A Description of the Advanced Research WRF Version 3[M]. 2008.
[27]	CHAI Z, ZHANG H, ZHANG M, et al. China’s EarthLab—Forefront of Earth System Simulation Research[J]. Advances in Atmospheric Sciences, 2021, 38(10): 1611-1620.
[28]	LI Y, WU Q, WANG X, et al. Effects of chemical mechanism and meteorological factors on the concentration of atmospheric pollutants in the megacity Beijing, China[J]. Atmospheric Environment, 2024, 323: 120393.
[29]	MIELIKAINEN J, HUANG B, HUANG H-L A, et al. GPU Implementation of Stony Brook University 5-Class Cloud Microphysics Scheme in the WRF[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(2): 625-633.
[30]	MIELIKAINEN J, HUANG B, WANG J, et al. Compute unified device architecture (CUDA)-based parallelization of WRF Kessler cloud microphysics sche-me[J]. Computers & Geosciences, 2013, 52: 292-299.
[31]	HUANG M, HUANG B, MIELIKAINEN J, et al. Further Improvement on GPU-Based Parallel Implementation of WRF 5-Layer Thermal Diffusion Sch-eme[C]// proceedings of the 2013 International Conference on Parallel and Distributed Systems, 2013: 15-18.

	试验设置
模拟时段	2021.07.01.12:00-2021.07.02.15:00
气象场	WRF v3.9.1提供逐时气象数据
排放清单	MEIC排放清单
水平分辨率	d01：45 km；d02:15 km
水平网格数	d01：228×165；d02：465×300

	CPU	GPU
硬件配置	2*国产 CPU处理器， 3.0GHz，32 核心	2*国产加速卡，3840流处理器，16 GB 显存
软件环境	Intel-2021.3.0	dtk-23.04.1

	RMSE（μg?m^-3/ppbV）	std（μg?m^-3/ppbV）	RMSE/std（%）
ASO₄	0.0070	0.48	1.47%
NO₂	0.024	7.38	0.33%
O₃	0.067	11.36	0.59%