Simulating flood dynamics on dynamic HPC resource sets

Flood dynamics are transitions between low-flow stages which result in small wet areas and high-flow stages which naturally result in large flooded areas. The response of the dynamics of a flood to the time-varying forcing (may it be a hydrograph or precipitation) is precisely what flood models attempt to simulate. Therefore, it is a priori unknown. 

The computational load of 2D shallow water simulators is strongly dependent on the number of flooded cells, and thus the flooded area. Consequently, the dynamics of the flooded area translates into time-varying computational demands: low flow stages can be simulated with fewer resources, whereas peak-flow stages demand significantly higher computational capacity. Typically, modellers will choose a set of computational resources which suits the problem size and demands based on experience and preliminary tests. However, these static (used throughout the simulation) resource sets either slow down computations when they are too small for the high flow stages, or make inefficient use of resources when they are too large for the low flow stages. It follows that dynamic resource allocations, based on the computational demands, would be optimal.

In this contribution we present the integration of the SERGHEI-SWE hydrodynamic model with the Dynamic Management of Resources library (DMRlib) to enable malleability —i.e., the runtime adjustment of MPI process counts and computational resources— to improve computational efficiency in shallow-flow simulations. By coupling SERGHEI-SWE with DMRlib, we enable the solver to dynamically expand or shrink its resource set during execution, adapting to these changing computational needs based on minimal heuristics.

SERGHEI-SWE is a high-performance, exascale-ready, scalable shallow water solver supporting CPUs and GPUs. DMRlib extends it with lightweight runtime support for process-level malleability, coordinating with the MPI runtime and job scheduler to manage resource adaptations. Within SERGHEI-SWE, resource reconfiguration is fundamentally a generalization of dynamic domain decomposition, to allow both the size and number of subdomains to change during execution. As a proof-of-concept, we implement minimal heuristics to trigger malleability based on wet-cell fractions: as flooded areas increase, additional resources are requested; when they decrease, resources are released.

The malleable SERGHEI-SWE was evaluated using dam-breaks, river flood, and catchment runoff tests. Numerical accuracy was preserved, with negligible differences relative to static (non-malleable) runs. Dynamic resource management improved computational efficiency relative to minimal fixed-resource configurations. However, performance remained below the best-case static maximum-resource setup, and communication overheads limited gains in low-demand phases. Nonetheless, the proof-of-concept demonstrates both feasibility and potential at larger scales.

The approach is accurate, robust, and promising for improving resource utilization in large-scale hydrodynamic modeling. Future work will focus on refining reconfiguration heuristics, improving understanding of overheads, and combining malleability with dynamic load balancing to better exploit scalable HPC environments.