Abstract
Abstract. Atmospheric composition models require efficient computation of surface fluxes from anthropogenic emissions and terrestrial biosphere exchange. This is particularly true for applications such as ensemble-based inverse emission modeling where these calculations constitute significant performance bottlenecks. We present the directive-based GPU acceleration and comprehensive refactoring of two critical surface-flux modules within the ICON-ART atmospheric modeling framework: the Online Emission Module (OEM) for anthropogenic emissions and the Vegetation Photosynthesis and Respiration Model (VPRM) for biospheric Carbon Dioxide (CO2) exchange. The implementation employs OPENACC compiler directives to expose inherent data parallelism while maintaining a single portable source code for both CPU and GPU architectures. The optimization strategy addresses fundamental incompatibilities between the original CPU-oriented algorithms and GPU execution requirements through three principal modifications: (i) elimination of runtime string comparisons via pre-computed integer-indexed lookup tables constructed during initialization, (ii) temporal decomposition of computations into sections with distinct update frequencies (initialization (once), boundary conditions (periodic), temporal scaling (hourly), and emission kernels (every timestep)) to minimize redundant calculations, and (iii) reorganization of loop hierarchies to maximize memory coalescing across ICON’s horizontal grid dimension. The hybrid CPU–GPU architecture executes initialization and I/O-intensive boundary updates on the host while offloading the computationally dominant temporal scaling and emission kernels to the device, with all tracer tendencies accumulated in device-resident arrays to eliminate host–device transfers within the physics integration loop. Scientific equivalence between CPU and GPU implementations is validated using a probabilistic testing framework, which establishes tolerance envelopes from perturbed CPU ensemble runs. This is consistent with spatial comparisons of simulated CO2 concentration fields over a European domain, which demonstrate pixel-wise agreement within ±0.01 %, corresponding to the numerical noise floor from floating-point arithmetic variations. Performance benchmarks on NVIDIA Grace–Hopper (GH200) superchips of the ALPS supercomputer in Switzerland reveal speedups of ∼1.9× on a single GPU node to 5.8× on five GPU nodes relative to a 278-rank CPU baseline. Despite 54–93 % higher absolute energy consumption on GPUs, the energy-delay product improves by 20–67 %, demonstrating favorable computational efficiency for time-critical applications. These advances enable ensemble-based atmospheric inversions with hundreds of tracers at kilometer-scale resolution within operationally feasible time constraints, supporting assimilation of dense satellite observations from instruments such as TROPOMI and the forthcoming CO2M mission. The GPU-enabled modules have been successfully deployed in recent ICON-ART applications for regional greenhouse-gas inversion and ensemble data assimilation. The single-source, directive-based implementation ensures long-term maintainability and portability across GPU generations while providing a reusable template for accelerating additional model components.