Accelerating physics simulations with tensor processing units: An inundation modeling example

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.

Automated summary

Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) have not only sped up computations for machine learning, but also for scientific modeling and computer simulations, as demonstrated in this study. By utilizing TPUs for distributed scientific computing, we were able to solve partial differential equations (PDEs) for simulating fluid physics and modeling riverine floods. The results show that TPUs achieve a significant speedup of two orders of magnitude compared to CPUs. Access to running physics simulations on TPUs is publicly available through the Google Cloud Platform, and we have released a Python interactive notebook version of the simulation.