Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learning but, as demonstrated here, also for scientific modeling and computer simulations. To study TPU hardware for distributed scientific computing, we solve partial differential equations (PDEs) for the physics simulation of fluids to model riverine floods. We demonstrate that TPUs achieve a two orders of magnitude speedup over CPUs. Running physics simulations on TPUs is publicly accessible via the Google Cloud Platform, and we release a Python interactive notebook version of the simulation.
@article{arxiv.2204.10323,
title = {Accelerating Physics Simulations with TPUs: An Inundation Modeling Example},
author = {Damien Pierce and R. Lily Hu and Yusef Shafi and Anudhyan Boral and Vladimir Anisimov and Sella Nevo and Yi-fan Chen},
journal= {arXiv preprint arXiv:2204.10323},
year = {2022}
}
Comments
14 pages, 10 figures. Accepted for publication in The International Journal of High Performance Computing Applications