Accelerating a fluvial incision and landscape evolution model with parallelism

TL;DR

This paper introduces a new parallel algorithm for landscape evolution models that significantly accelerates computations using modern hardware, enabling faster inverse problem solving and landscape simulations.

Contribution

A novel parallel algorithm that leverages GPUs, many-core processors, and SIMD instructions to speed up landscape evolution modeling by over 40 times.

Findings

43x faster than previous methods

Sublinear scaling with input size

Effective methods for flow routing and depression elimination

Abstract

Solving inverse problems 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 algorithm is well-suited to leveraging modern parallelism. Here, I describe an algorithm that can utilize the parallel potential of GPUs, many-core processors, and SIMD instructions, in addition to working well in serial. The new algorithm runs 43x faster (70s vs. 3,000s on a 10,000x10,000 input) than the previous state of the art and exhibits sublinear scaling with input size. I also identify methods for using multidirectional flow routing and quickly eliminating landscape depressions and local minima. Tips for parallelization and a step-by-step guide to achieving it are given to help others achieve good performance with their own code. Complete, well-commented, easily adaptable source code for all versions of the algorithm is available as a supplement and on Github.