Accelerating physics simulations with tensor processing units: An inundation modeling example. (July 2022)

• Record Type: Journal Article
• Authors: Hu, R Lily; Pierce, Damien; Shafi, Yusef; Boral, Anudhyan; Anisimov, Vladimir; Nevo, Sella; Chen, Yi-fan
• Is Part Of: International journal of high performance computing applications. Volume 36:Number 4(2022)
• Page Start: 510
• Page End: 523
• Publication Date: 2022-07
• Subjects: CFD -- computational fluid dynamics -- distributed computing -- flood forecasting -- flood modeling -- inundation modeling -- large scale simulations -- parallel computing -- tensor processing units; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/10943420221102873
• Languages: English
• ISSNs: 1094-3420

Abstract

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.