Preserving and improving the legacy of eScience: the GLOBO experience

Legacy scientific codes are essential to long-standing operational workflows but often cannot fully exploit modern heterogeneous high-performance computing (HPC) architectures. GLOBO, a global atmospheric circulation model developed at CNR-ISAC, is one such model that is still used for research and operational weather forecasting. In this work, we present an ongoing refactoring of GLOBO to improve its scalability and prepare it for GPU acceleration and cloud execution. The modernization focuses on three main areas: dynamic memory allocation, replacement of legacy point-to-point MPI communications with optimized collective operations, and OpenACC-based GPU offloading. We describe the redesign of the communication strategy, which replaces multiple MPI_Isend/MPI_Irecv exchanges with collective primitives (MPI_Bcast, MPI_Scatterv, MPI_Sendrecv, MPI_Reduce) to reduce overhead and improve parallel efficiency. Scalability experiments across multiple spatial resolutions show that a new collective communication strategy consistently matches or exceeds the original implementation, delivering up to 40% runtime reduction in high-resolution, multi-node configurations. These results lay the groundwork for refactoring GLOBO toward a fully dynamic, GPU-accelerated, cloud-ready execution, preserving its scientific heritage while ensuring sustainable performance on future HPC platforms.