A distributed multi‐architecture design for massively parallel hyperbolic solvers is herein introduced and benchmarked. A unified object‐oriented central processing unit (CPU) + graphics processing unit (GPU) approach is complemented with an inter‐device communication layer, enabling both coarse and fine‐grain parallelism on hyperbolic solvers. The approach involves the combination of three different programming platforms, namely OpenMP, CUDA and MPI. The efficiency of this distributed‐heterogeneous approach is quantified under static and dynamic loads on consumer and professional grade CPUs and GPUs. An asynchronous communications scheme is implemented and described, showing very reduced overheads and a nearly linear scalability for multiple device combinations. For simulations (or systems) with non‐homogeneous workloads (or devices) the domain decomposition algorithm incorporates a low‐frequency load‐to‐device fitting function to ensure computational balance. A real‐world application to high‐resolution hydrodynamic modelling is presented: the propagation of a tsunami in the estuary a large river and its run‐up in an urban mesh. The proposed implementation shows speedups of up to two orders of magnitude, opening new perspectives for solvers with high‐demand requirements but relatively simple hardware in multi‐architecture machines.
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Conde et al. (2025) studied this question.
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