Benchmarks and applications

GeoSWE has been benchmarked head-to-head against three established GPU shallow-water codes on audited-identical inputs, and exercised from county to continental scale.

Multi-GPU scaling

On a synthetic uniform domain (so the result isolates the solver and halo from real-terrain load imbalance), both storage paths scale near-ideally. The sweep was run on two configurations: 1–16 NVIDIA H100 GPUs across two eight-GPU nodes, and 1–32 MIG 2g.48gb slices of RTX PRO 6000 Blackwell GPUs across two sixteen-slice nodes, which reaches twice the cell count.

  • Weak scaling (fixed work per GPU, 640 M cells each): 99.5% efficiency for the compressed path at 16 H100 GPUs, reaching 10.24 billion cells, and 98.8% at 32 Blackwell MIG slices across two nodes, reaching 20.48 billion cells. Per-step time stays nearly flat with rank count, including across the node boundary, because the halo exchange plus the scalar dt all-reduce is an \(O(1)\) overhead.

  • Strong scaling (fixed total problem): 15.5× speedup at 16 H100 GPUs (96.8% efficiency) and 26.5× at 32 Blackwell slices across two nodes (82.9%), tapering once each rank holds too few cells (the surface-to-volume trade-off).

  • The flat-full configuration is faster per step than the dense one at constant per-rank work, while producing bit-identical residuals on the same cells. The margin depends on the machine, so take the number with its machine: 9 % on the hardware of this study, one H100 MIG slice at 640 M cells per rank (115.1 against 127.7 ms/step). The same two harnesses on one full L40S at 320 M cells per rank give 22.6 against 28.4 ms/step, a 26 % margin, and under the first campaign’s split-forcings flags (SWE_FUSE_FORCINGS=0, CFL_RESAMPLE_EVERY=5) 37.7 against 66.0 ms/step, a factor 1.75. Both tiers are bandwidth-bound at these sizes, and the dense tier is the one that moves: it is more sensitive to the card and to which forcings are fused. (These runs keep every cell active, so they measure the layout, not the active mask; see the three configurations.)

Do not difference the two hardware configurations against each other for a per-device ratio: they are separate machines with different interconnects and MIG partitioning, not a controlled per-device comparison.

Reproduce the shape of these curves with examples/ex05_scaling_bench.py. See multi-GPU & MPI.

Accuracy: four-code comparison

On a 214-million-cell, 3 m Pinellas County benchmark (a Hurricane-Helene standing-tide case and a rain-driven ×10 variant), GeoSWE was compared against three established GPU shallow-water codes (ORNL’s TRITON, SynxFlow of the HiPIMS lineage, and SERGHEI) on audited-identical inputs:

  • GeoSWE’s flat-full configuration reproduces its dense solution to 0.05 cm RMSE (CSI 1.000) with identical step counts on the standing-tide benchmark, and the flat-active configuration agrees to 1.36 cm within its active set: the active-cell mesh is faithful.

  • GeoSWE’s flat-active configuration has the lowest wall time and peak GPU memory of the tested configurations at every GPU count: 3.5–3.9× faster per step, with 1.5–1.9× less per-rank memory, than the next-fastest comparison code; GeoSWE dense is 1.6–1.7× faster than TRITON on the same cells. The dense-to-flat-active speedup is not the storage layout alone: it also carries kernel fusion, and flat-full is the configuration that isolates the layout. The four codes’ scored standing-tide fields agree to pairwise CSI 0.989–0.994.

  • On the rain-driven (×10 rainfall) stress test, GeoSWE and SynxFlow complete the hour and agree closely (CSI 0.990 at 0.3 m, 1.5 cm RMSE); TRITON’s fp32 run and SERGHEI’s run at the benchmark floor do not reach a valid end field. On a steady rained slope with a high-accuracy reference solution, GeoSWE stays within 1 % of the reference film depth where TRITON and SERGHEI depart by tens of percent. GeoSWE is the only one of the four demonstrated at the continental scales below.

Applications

Case

Resolution

Active cells

Hardware

Pinellas County, FL (Helene)

3 m

125.6 M

1 GPU

Florida (Helene)

10 m

1.78 B

4 GPUs

CONUS (Helene)

30 m

8.88 B

single node, 8 × H100

The continental cases use the compressed active-cell mesh with build-once caching and checkpoint/resume, so a 72-hour simulation survives job-time limits by resuming from the last checkpoint. Florida’s forcing spans most of its domain; on CONUS, Helene’s heavy rain covers only the Southeast, so that run is a capacity and workflow envelope rather than a continental flood simulation. The Florida and CONUS pipelines are not included in this release; the Pinellas benchmark exercises the same solver paths.

Note

These results and the full methodology are described in the GeoSWE paper; see citing. The cross-code benchmark establishes consistency between independently developed solvers on identical inputs, not validation against observations, and the application runs are computational demonstrations under stated, uncalibrated settings, not validated flood hindcasts.