基于OpenMP Offload与Codee的天气研究与预报模型优化
Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee
AI总结：
针对WRF模型的GPU适配需求，研究采用OpenMP设备卸载指令移植FSBM方案的计算密集型例程，结合Codee等工具优化代码，在Perlmutter超算的CONUS-12km雷暴测试中实现2.08倍整体加速。
摘要：
当前，天气研究与预报模型（WRF）采用共享内存（OpenMP）和分布式内存（MPI）并行机制。为利用NERSC的Perlmutter超级计算机上的GPU资源，我们使用OpenMP设备卸载指令，将快速谱分档微物理方案（FSBM）中部分计算密集型例程移植到NVIDIA GPU上。为推进该过程，我们探索了一种结合运行时分析工具与静态代码检查工具Codee来重构子例程的优化工作流。在CONUS-12km雷暴测试用例中，我们观测到整体加速比达2.08倍。
英文摘要：
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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