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.
翻译:最近硬件加速器的进展,如Tensor处理器(TPUs)等硬件加速器加快了相对于中央处理器(CPUs)的计算时间,不仅用于机器学习,而且如这里所示,用于科学建模和计算机模拟。为了研究用于分布式科学计算的TPU硬件,我们解决了用于模拟河流洪水的液体物理模拟的局部差分方程式(PDEs)。我们证明TPU在CPU上达到两个级的加速速度。在TPU上运行物理模拟可以通过Google云平台公开查阅,我们发行了模拟的Python交互式笔记本版本。