Porting the WAVEWATCH III (v6.07) wave action source terms to GPU

Journal Article · 03 March 2023 · Geoscientific Model Development (Online)

DOI: https://doi.org/10.5194/gmd-16-1445-2023 · OSTI ID:1959840

Ikuyajolu, Olawale James ^[1]; Van Roekel, Luke  ^[2]; Brus, Steven Richard  ^[3]; Thomas, Erin Emily ^[2]; Deng, Yi ^[1]; Sreepathi, Sarat  ^[4]

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1. Georgia Institute of Technology, Atlanta, GA (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
3. Argonne National Lab. (ANL), Argonne, IL (United States)
4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Surface gravity waves play a critical role in several processes, including mixing, coastal inundation, and surface fluxes. Despite the growing literature on the importance of ocean surface waves, wind–wave processes have traditionally been excluded from Earth system models (ESMs) due to the high computational costs of running spectral wave models. The development of the Next Generation Ocean Model for the DOE’s (Department of Energy) E3SM (Energy Exascale Earth System Model) Project partly focuses on the inclusion of a wave model, WAVEWATCH III (WW3), into E3SM. WW3, which was originally developed for operational wave forecasting, needs to be computationally less expensive before it can be integrated into ESMs. To accomplish this, we take advantage of heterogeneous architectures at DOE leadership computing facilities and the increasing computing power of general-purpose graphics processing units (GPUs). This paper identifies the wave action source terms, W3SRCEMD, as the most computationally intensive module in WW3 and then accelerates them via GPU. Our experiments on two computing platforms, Kodiak (P100 GPU and Intel(R) Xeon(R) central processing unit, CPU, E5-2695 v4) and Summit (V100 GPU and IBM POWER9 CPU) show respective average speedups of 2× and 4× when mapping one Message Passing Interface (MPI) per GPU. An average speedup of 1.4× was achieved using all 42 CPU cores and 6 GPUs on a Summit node (with 7 MPI ranks per GPU). However, the GPU speedup over the 42 CPU cores remains relatively unchanged (~1.3×) even when using 4 MPI ranks per GPU (24 ranks in total) and 3 MPI ranks per GPU (18 ranks in total). This corresponds to a 35 %–40 % decrease in both simulation time and usage of resources. Due to too many local scalars and arrays in the W3SRCEMD subroutine and the huge WW3 memory requirement, GPU performance is currently limited by the data transfer bandwidth between the CPU and the GPU. Ideally, OpenACC routine directives could be used to further improve performance. However, W3SRCEMD would require significant code refactoring to make this possible. We also discuss how the trade-off between the occupancy, register, and latency affects the GPU performance of WW3.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Office of Biological & Environmental Research (BER)
• Grant/Contract Number: AC05-00OR22725; 89233218CNA000001; ESMD-SFA; AC02-06CH11357
• OSTI ID: 1959840
• Alternate ID(s): OSTI ID: 1965244; OSTI ID: 1969236; OSTI ID: 2382742
• Report Number(s): LA-UR-22-24512
• Journal Information: Geoscientific Model Development (Online), Vol. 16, Issue 4; ISSN 1991-9603
• Publisher: Copernicus Publications, EGUCopyright Statement
• Country of Publication: United States
• Language: English