This is not an official Google product -moded
Swirl-LM is a computational fluid dynamics (CFD) simulation framework that is accelerated by the Tensor Processing Unit (TPU). It solves the three dimensional variable-density Navier-Stokes equation using a low-Mach approximation, and the governing equations are discretized by a finite-difference method on a collocated structured mesh. It is implemented in TensorFlow.
To use Swirl-LM, you will need access to TPUs on Google Cloud. For small simulations, the easiest way to access TPUs is to use Google Colab. To see a demo, you can open one of the example notebooks and follow the notebook's instructions.
To run large simulations, you will need to create TPU Nodes or VMs in your Google Cloud project. See the instructions for the stand-alone demo on how to set up TPU Nodes and the docs about Cloud TPUs to set up TPM VMs.
If you extend or use this package in your work (except the components in the
ext subpackage, in which case please reference the information within that
subpackage), please cite the
paper
as
@ARTICLE{Wang2022-ln,
title = "A {TensorFlow} simulation framework for scientific computing of
fluid flows on tensor processing units",
author = "Wang, Qing and Ihme, Matthias and Chen, Yi-Fan and Anderson,
John",
abstract = "A computational fluid dynamics (CFD) simulation framework for
fluid-flow prediction is developed on the Tensor Processing Unit
(TPU) platform. The TPU architecture is featured with
accelerated dense matrix multiplication, large high bandwidth
memory, and a fast inter-chip interconnect, making it attractive
for high-performance scientific computing. The CFD framework
solves the variable-density Navier-Stokes equation using a
low-Mach approximation, and the governing equations are
discretized by a finite-difference method on a collocated
structured mesh. It uses the graph-based TensorFlow as the
programming paradigm. The accuracy and performance of this
framework is studied both numerically and analytically,
specifically focusing on effects of TPU-native single precision
floating point arithmetic. The algorithm and implementation are
validated with canonical 2D and 3D Taylor-Green vortex
simulations. To demonstrate the capability for simulating
turbulent flows, simulations are conducted for two
configurations, namely decaying homogeneous isotropic turbulence
and a turbulent planar jet. Both simulations show good
statistical agreement with reference solutions. The performance
analysis shows a linear weak scaling and a superlinear strong
scaling up to a full TPU v3 pod with 2048 cores.",
journal = "Comput. Phys. Commun.",
publisher = "North-Holland",
volume = 274,
pages = "108292",
month = may,
year = 2022,
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2022.108292},
url = {https://www.sciencedirect.com/science/article/pii/S0010465522000108},
keywords = "Tensor processing unit; TensorFlow; Computational fluid
dynamics; High performance computing"
}