On the Migration of OpenACC-Based Applications into OpenMP 5+

Servat, Harald; Rossi, Giacomo; Durán, Alejandro; Narayanaswamy, Ravi

doi:10.1007/978-3-031-15922-0_9

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…The PUMAS source code is being actively developed at https://github.com/ESCOMP/PUMAS. The OpenMP target offload code for the PUMAS stand‐alone kernel is generated by an auto‐migration tool provided by the Intel Corporation (Servat et al., 2022).…”

Section: Data Availability Statementmentioning

confidence: 99%

Acceleration of the Parameterization of Unified Microphysics Across Scales (PUMAS) on the Graphics Processing Unit (GPU) With Directive‐Based Methods

Sun

Dennis

Mickelson

et al. 2023

J Adv Model Earth Syst

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Cloud microphysics is one of the most time‐consuming components in a climate model. In this study, we port the cloud microphysics parameterization in the Community Atmosphere Model (CAM), known as Parameterization of Unified Microphysics Across Scales (PUMAS), from CPU to GPU to seek a computational speedup. The directive‐based methods (OpenACC and OpenMP target offload) are determined as the best fit specifically for our development practices, which enable a single version of source code to run either on the CPU or GPU, and yield a better portability and maintainability. Their performance is first examined in a PUMAS stand‐alone kernel and the directive‐based methods can outperform a CPU node as long as there is enough computational burden on the GPU. A consistent behavior is observed when we run PUMAS on the GPU in a practical CAM simulation. A 3.6× speedup of the PUMAS execution time, including data movement between CPU and GPU, is achieved at a coarse horizontal resolution (8 NVIDIA V100 GPUs against 36 Intel Skylake CPU cores). This speedup further increases up to 5.4× at a high resolution (24 NVIDIA V100 GPUs against 108 Intel Skylake CPU cores), which highlights the fact that GPU favors larger problem size. This study demonstrates that using GPU in a CAM simulation can save noticeable computational costs even with a small portion of code being GPU‐enabled. Therefore, we are encouraged to port more parameterizations to GPU to take advantage of its computational benefit.

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Section: Data Availability Statementmentioning

confidence: 99%