Numerics-driven uplifting, automatic parallelization, and performance optimizations with deep kernel fusion for ocean models on heterogeneous architectures

Achieving peak performance for ocean models on modern high-performance computing (HPC) architectures requires extensive, costly code rewrites that are not only time-consuming and error-prone but also highly architecture-specific and require numerics experts to be proficient in parallel programming models or domain-specific languages (DSLs). In this paper we introduce Poseidon , a source-to-source code optimization tool that employs numerics & HPC co-design. We developed an uplifting approach to a hypergraph for the data flow and to our own Poseidon Intermediate Representation (PosIR) for the computations, which recovers for the first time high-level information and semantics about the computations and memory management that are typically lost during the conversion of numerical algorithms to source code. This representation is then employed for model-driven optimizations. In the backend, we inject source code back into the original code supporting different target HPC architectures, reusing the existing model as the runtime. Our evaluation investigates OpenMP and OpenACC parallel programming models on CPUs and GPUs. We demonstrate Poseidon’s capability of automatic parallelization and optimization of existing Fortran code, with comparisons to state-of-the-art parallelized code with performance improvements of up to × 2.60 and automatically reducing the required memory footprint by up to × 2.29.