Submitted as: development and technical paper
10 May 2023
Submitted as: development and technical paper |  | 10 May 2023
Status: this preprint is currently under review for the journal GMD.

Graphics processing unit accelerated ice flow solver for unstructured meshes using the Shallow Shelf Approximation (FastIceFlo v1.0)

Anjali Sandip, Ludovic Räss, and Mathieu Morlighem

Abstract. Ice-sheet flow models capable of accurately projecting their future mass balance constitute tools to improve flood risk assessment and assist sea-level rise mitigation associated with enhanced ice discharge. Some processes that need to be captured, such as grounding line migration, require high spatial resolution (1 km or better). Conventional ice flow models may need significant computational resources because these models mainly execute Central Processing Units (CPUs), which lack massive parallelism capabilities and feature limited peak memory bandwidth. On the other side of the spectrum, Graphics Processing Units (GPUs) are ideally suited for high spatial resolution as the calculations at every grid point can be performed concurrently by thousands of threads or parallel workers. In this study, we combine GPUs with the pseudo-transient (PT) method, an accelerated iterative and matrix-free solving approach, and investigate its performance for finite elements and unstructured meshes applied to two-dimensional (2-D) models of real glaciers at a regional scale. For both Jakobshavn and Pine Island glacier models, the number of nonlinear PT iterations to converge for a given number of vertices N scales in the order of O(N1.2) or better. We compared the performance of PT CUDA C implementation with a standard finite-element CPU-based implementation using the metric: price and power consumption to performance. The single Tesla V100 GPU is 1.5 times the price of the two Intel Xeon Gold 6140 CPU processors. The power consumption of the PT CUDA C implementation was approximately one-seventh of the standard CPU implementation for the test cases chosen in this study. We expect a minimum speed-up of >1.5 to justify the Tesla V100 GPU price to performance. We report the performance (or the speed-up) across glacier configurations for degrees of freedom (DoFs) tested to be >1.5 on a Tesla V100. This study is a first step toward leveraging GPU processing power for accurate polar ice discharge predictions. The insights gained from this study will benefit efforts to diminish spatial resolution constraints at increased computing speed. The increased computing speed will allow running ensembles of ice-sheet flow simulations at the continental scale and at high resolution, previously not possible, enabling quantification of model sensitivity to changes in future climate forcings. These findings will be significantly beneficial for process-oriented and sea-level-projection studies over the coming decades.

Anjali Sandip et al.

Status: open (until 05 Jul 2023)

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Anjali Sandip et al.

Anjali Sandip et al.


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Short summary
We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPU) and compare it with a standard central processing units (CPU) implementation. We justify the GPU implementation by applying the price and power consumption to performance metrics for up to a million grid point spatial resolutions. The study is a first step toward leveraging GPU processing power for accurate polar ice discharge predictions.