Solving inverse problems, performing sensitivity analyses, and achieving statistical rigour in landscape evolution models requires running many model realizations. Parallel computation is necessary to achieve this in a reasonable time. However, no previous landscape evolution algorithm is able to leverage modern parallelism. Here, I describe an algorithm that can utilize the parallel potential of GPUs and many-core processors, in addition to working well in serial. The new algorithm runs 43x faster (70s vs. 3000s on a 10,000 x 10,000 input) than the previous state of the art and exhibits sublinear scaling with input size. I also identify key techniques for multiple flow direction routing and quickly eliminating landscape depressions and local minima. Complete, well-commented, easily adaptable source code for all versions of the algorithm is available on Github and Zenodo.
Open access through arrive.org: https://arxiv.org/abs/1803.02977.
