Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU v1.0

被引:0
|
作者
Yuan, Ye [1 ,2 ]
Yu, Fujiang [1 ,2 ]
Chen, Zhi [1 ,2 ]
Li, Xueding [3 ]
Hou, Fang [1 ]
Gao, Yuanyong [1 ,2 ]
Gao, Zhiyi [1 ,2 ]
Pang, Renbo [1 ,2 ]
机构
[1] Natl Marine Environm Forecasting Ctr China, Beijing 100081, Peoples R China
[2] Minist Nat Resources China, Key Lab Res Marine Hazards Forecasting, Beijing 100081, Peoples R China
[3] Fujian Marine Forecasts, Fuzhou 350003, Peoples R China
关键词
ADAPTIVE MESH REFINEMENT; FORECASTING SYSTEM; TURBULENCE;
D O I
10.5194/gmd-17-6123-2024
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The spectral wave model WAM (Cycle 6) is a commonly used code package for ocean wave forecasting. However, it is still a challenge to include it into the long-term Earth system modeling due to the huge computing requirement. In this study, we have successfully developed a GPU-accelerated version of the WAM model that can run all its computing-demanding components on GPUs, with a significant performance increase compared with its original CPU version. The power of GPU computing has been unleashed through substantial efforts of code refactoring, which reduces the computing time of a 7 d global 1/10 degrees wave modeling to only 7.6 min in a single-node server installed with eight NVIDIA A100 GPUs. Speedup comparisons exhibit that running the WAM6 with eight cards can achieve the maximum speedup ratio of 37 over the dual-socket CPU node with two Intel Xeon 6236 CPUs. The study provides an approach to energy-efficient computing for ocean wave modeling. A preliminary evaluation suggests that approximately 90 % of power can be saved.
引用
收藏
页码:6123 / 6136
页数:14
相关论文
共 10 条
  • [1] Towards real-time DNA biometrics using GPU-accelerated processing
    Reja, Mario
    Pungila, Ciprian
    Negru, Viorel
    LOGIC JOURNAL OF THE IGPL, 2021, 29 (06) : 906 - 924
  • [2] GPU-Accelerated Real-Time Path Planning and the Predictable Execution Model
    Forsberg, Bjorn
    Palossi, Daniele
    Marongiu, Andrea
    Benini, Luca
    INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS 2017), 2017, 108 : 2428 - 2432
  • [3] Real-time dose computation: GPU-accelerated source modeling and superposition/convolution
    Jacques, Robert
    Wong, John
    Taylor, Russell
    McNutt, Todd
    MEDICAL PHYSICS, 2011, 38 (01) : 294 - 305
  • [4] Towards real-time detection of seizures in awake rats with GPU-accelerated diffuse optical tomography
    Zhang, Tao
    Zhou, Junli
    Carney, Paul R.
    Jiang, Huabei
    JOURNAL OF NEUROSCIENCE METHODS, 2015, 240 : 28 - 36
  • [5] Amplitude and Phase Computable Ocean Wave Real-Time Modeling with GPU Acceleration
    Wang, Guigui
    Tan, Shihan
    Song, Ge
    Wang, Sheng
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (09)
  • [6] GPU-accelerated low-latency real-time searches for gravitational waves from compact binary coalescence
    Liu, Yuan
    Du, Zhihui
    Chung, Shin Kee
    Hooper, Shaun
    Blair, David
    Wen, Linqing
    CLASSICAL AND QUANTUM GRAVITY, 2012, 29 (23)
  • [7] RETRACTION: Amplitude and Phase Computable Ocean Wave Real-Time Modeling with GPU Acceleration.
    Wang, Guigui
    Tan, Shihan
    Song, Ge
    Wang, Sheng
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2025, 13 (02)
  • [8] GPU-Accelerated PD-IPM for Real-Time Model Predictive Control in Integrated Missile Guidance and Control Systems
    Lee, Sanghyeon
    Lee, Heoncheol
    Kim, Yunyoung
    Kim, Jaehyun
    Choi, Wonseok
    SENSORS, 2022, 22 (12)
  • [9] Celeris: A GPU-accelerated open source software with a Boussinesq-type wave solver for real-time interactive simulation and visualization
    Tavakkol, Sasan
    Lynett, Patrick
    COMPUTER PHYSICS COMMUNICATIONS, 2017, 217 : 117 - 127
  • [10] Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data
    Wu, Luolin
    Chang, Ming
    Wang, Xuemei
    Hang, Jian
    Zhang, Jinpu
    Wu, Liqing
    Shao, Min
    GEOSCIENTIFIC MODEL DEVELOPMENT, 2020, 13 (01) : 23 - 40