FastFlow: GPU Acceleration of Flow and Depression Routing for Landscape Simulation

In this section, we compare our approach against other parallel GPU and CPU implementations of flow and depression routing.

Our performance tests were executed on a computer equipped with an Nvidia RTX A6000 GPU with 48GB of memory and used 20 cores of an Intel Xeon Gold CPU clocked at 2.10GHz with 128GB RAM.

For our test dataset, we incorporated a mix of real and synthetic terrains. We began by extracting a set of 10 real terrains from the IGN RGE ALTI Digital Elevation 1m dataset [IGN22] chosen on the basis of topographic variety. These 10 terrains were sampled at cell resolutions of $512\; \backslash times\; 512$, $1024\; \backslash times\; 1024$, $2048\; \backslash times\; 2048$ and $4196\; \backslash times\; 4196$ to support scaling experiments.

We also included 5 synthetic terrains at the same resolution levels, and each with a controlled proportion of depression coverage (at synth-1%, synth-5% and synth-15% levels). These were generated by erosion simulation (Section 6), followed by layering different amplitudes of uniformly distributed noise. Note that it is common practice to add this type of noise during erosion simulation to mimic natural stochastic processes.

Finally, we included a uniformly flat terrain as an extreme test case, since in this instance all cells are local minima and hence coded as depressions.