Accelerating the A2D Ocean Model With Standard Language Parallelism

Abstract

Parallel computing is crucial for enhancing the computational efficiency of ocean numerical models. Traditionally, parallelization in such models relied primarily on MPI. Subsequent developments introduced GPU-oriented computing tools, including CUDA and OpenACC, which enable accelerated computation on NVIDIA GPUs. However, each tool has its own syntax and lacks interoperability. Recent support for Standard Language Parallelism from major Fortran compiler vendors (e.g., Intel, NVIDIA) has unlocked efficient parallelization across multi-core CPUs and GPUs from a single Fortran codebase. This study employs Standard Language Parallelism to accelerate the A2D model, a two-dimensional unstructured-grid ocean numerical model, by parallelizing loops in its source code. The refactored code can be compiled for either multi-core CPU or GPU execution simply by selecting appropriate compiling flags. This approach offers advantages including high code readability, broad hardware compatibility, and reduced maintenance overhead. Using compilers such as ifort or nvfortran, the parallelized model achieves speedups exceeding $23\times $ in both multi-core and GPU configurations compared to the original serial implementation.