The science of Geomorphology characterizes processes that sculpt Earth's surface – including the associated transfers and trajectories of mineral mass and chemical constituents, and resulting in the formation and destruction of sedimentary deposits. LLEM is a massively parallel computer model that simulates Earth Surface Dynamics from mountains to continental margins.
Geomorphology began as a descriptive science with qualitative conceptual models for landscape evolution over larger temporal and spatial scales. In recent decades the discipline has undergone a quantitative revolution whereby many fundamental Earth surface processes are predictable utilizing calibrated mathematical models. Synthesizing and upscaling such numerical constructs into overarching ‘surface system’ perspectives is now accomplished quantitatively through Landscape Evolution Models (LEMs). However, scales beyond relatively small landscapes or simple formulae have proved computationally challenging for LEMs utilizing single-threaded CPU-based algorithms with low memory bandwidth. LEMs often avoid (or greatly simplify) lowland fluvial landscapes characterized by ‘morphodynamics’ that are fast-evolving and complex (eg., numerically intensive and massively interconnected) compared to those for uplands rivers now represented with simplistic, steepest-descent, single-node-wide, single-directional flow (unrealistic for large rivers that migrate and bifurcate). Two centuries after von Humboldt, the father of environmental science, emphasized the how Earth surface and biological systems were fundamentally characterized by interconnectivity at all scales, it is unfortunate that most available environmental models are written for computer architectures that have poor interconnectivity when scaled up to larger simulations.
Recent, transformative advances in massively parallel Graphics Processing Units (GPUs) can now facilitate holistic modeling of processes, fluxes, and stratigraphy over extraordinary temporal and spatial scales while efficiently representing a high level of system interconnectivity – on the land, across continental shelves, and over millions of years. For example, it is possible to model the Quaternary evolution of the central Amazon basin, or 3.2 million km2 of the Sunda Shelf (above image shows the Gulf of Thailand and the Mekong Delta at 488ka, with Sea Level at -32 m).
LLEM (pronounced ‘Elm’) is a GPU-based Landscape-Linked Environmental Model that leverages massively parallel computer architectures (of thousands to millions of GPU cores) to simulate a diverse suite of geomorphic processes (for millions to billions of model nodes), including:
1) Mountain & uplands processes, including soil formation, erosion, & (simple) landslides
2) Fast multi-directional flux routing with NIAGRA (Networking Incorporating A Gpu-paRallel Algorithm)
3) Water surface levelling & flows that respond robustly to changing sea levels & other perturbations with HYDRA (HYDraulic paRallel Algoritm)
4) Water & sediment partitioning (for all topographic configurations) accounting for channel width and back-water effects
5) Particulate transport simulated throughout the model domain, both fluvial & marine, and separately for sand, silt, clay, & organic carbon fractions
6) Detailed fluvial processes (meandering, anastomosing, avulsions, bars, levees & ridges, back-swamps, & floodplain channels)
7) Stratigraphy fully accounted for all nodes (stratigraphic ‘big data’ is load balanced between RAM and Optane drives)
8) Vertical deformation fields, including Glacial Isostatic Adjustment (GIA) & tectonic uplift
9) Continental shelf processes (diffusive, advective, & tidal), including waves for shelves and lakes
10) Mixture modeling for conservative constituents & tracers [in testing]
11) Shoreline and estuarine processes, including prediction of palaeo-waves [in development]
12) Reaction-Transport modeling for labile biogeochemical constituents [in development]
LLEM incorporates established mathematical constructs from the published literature, synthesized within the framework of a novel suite of massively parallel GPU-based algorithms. LLEM is designed to simulate ‘Grand Scientific Challenges’ of morphodynamic connectivity through ‘Source-to-Sink’ systems, and therefore employs an adaptable and scalable architecture (with optimization of memory bandwidth, cache size, and latency).
Geometric structure is a triangular irregular network (TIN), allowing adaptable spatial configurations and density variations for model nodes. While any Delaunay triangulation is possible, the fastest models are D8 or D6 configurations – regular geometries that offer significant increases in speed and other computational benefits. Flexible TIN structures require look-up indexes (for neighbors) that represent a significant calculations and memory bandwidth bottleneck for CPUs, single-threaded architectural challenges that can be parallelized across thousands of GPU cores with extraordinarily fast memory. While LLEM is exceptionally fast for TINs compared to CPU models, it is even faster for regular meshes – current LLEM development is focused on maximizing the advantages of hexagonal meshes.
Modern GPUs offer ~15 Tflops (FP32) of computational power per chip (each containing >5000 processors) and memory bandwidths of ~1 TB/s (~20x faster than CPUs and ~200x faster than 5GB Infiniband interconnects between traditional supercomputer blades). With 2-8 GPUs installable per ‘blade’, ~120 Tflops of effective computation (eg., high bandwidth & low latency to shared memory necessary for simulations of interconnected environmental systems) is possible for a specially built workstation (figures that are increasing at a dramatic pace). A decade ago this desk-top workstation would be the fastest supercomputer on Earth! LLEM also runs well on custom ‘gamer’ systems (both desktop and portable) that feature superior cooling and over-clocked GPUs.
Written from scratch in CUDA, C++, and Matlab with massively parallel GPU-optimized code throughout, LLEM has the upscale potential to run across billions of cores on GPU-based supercomputers such as TITAN (17 petaFLOPS) or the upcoming SUMMIT (>200 petaFLOPS). LLEM is also positioned to take advantage of recent advances in FP16 and tensor computations on Volta-class GPUs, which now offer up to ~1 PetaFLOP of tensor performance for a single desktop GPU-based system. Coding and model testing continues, with tens of thousands of lines of code under active development.
This site is under development. Information and videos will be posted as simulation of different landscapes continues … although coding remains the priority.