利用 OpenMP 卸载和 Codee 优化天气研究和预测模型

arXiv - CS - Distributed, Parallel, and Cluster Computing Pub Date : 2024-09-11 DOI:arxiv-2409.07232

ChayanonNamo, WichitrnithedHelen, Woo-Sun-YangHelen, YunHelen, He, Brad Richardson, Koichi Sakaguchi, Manuel Arenaz, William I. Gustafson Jr., Jacob Shpund, Ulises Costi Blanco, Alvaro Goldar Dieste

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引用次数: 0

摘要

目前，天气研究与预报模型（WRF）使用共享内存（OpenMP）和分布式内存（MPI）并行。为了充分利用 NERSC Perlmutter 超级计算机上的 GPU 资源，我们使用 OpenMP 设备卸载指令，将快速光谱斌微物理（FSBM）微物理方案中部分计算成本较高的例程移植到英伟达™（NVIDIA®）GPU 上。为了促进这一过程，我们探索了一种优化工作流程，该流程同时使用运行时剖析器和静态代码检查工具 Codee 来重构子例程。在 CONUS-12km 雷暴测试案例中，我们观察到整体速度提高了 2.08 倍。

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Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Currently, the Weather Research and Forecasting model (WRF) utilizes shared memory (OpenMP) and distributed memory (MPI) parallelisms. To take advantage of GPU resources on the Perlmutter supercomputer at NERSC, we port parts of the computationally expensive routines of the Fast Spectral Bin Microphysics (FSBM) microphysical scheme to NVIDIA GPUs using OpenMP device offloading directives. To facilitate this process, we explore a workflow for optimization which uses both runtime profilers and a static code inspection tool Codee to refactor the subroutine. We observe a 2.08x overall speedup for the CONUS-12km thunderstorm test case.

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arXiv - CS - Distributed, Parallel, and Cluster Computing

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