Windsurfing with APPA: Automating the Computational Fluid Dynamics Simulations of Wind Flow using the Cloud Computing

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Windsurfing with APPA: Automating Computational
Fluid Dynamics Simulations of Wind Flow using
Cloud Computing*
Anshul Jindal, Benedikt Strahm, Vladimir Podolskiy, Michael Gerndt
Chair of Computer Architecture and Parallel Systems
Technical University of Munich (TUM), Germany
Session: CISA
Parallel, Distributed, and Network-Based Processing 2020
13th March 2020
*Supported by Google Cloud Platform research
credits and AWS cloud credits.

Cloud and IoT RG @ Chair of Computer Architecture and Parallel Systems
Technical University of Munich
 Research Areas:
• Self-adaptive Cloud (predictive autoscaling for VMs).
• AI for smart Cloud operations (anomaly detection and failure predictions).
• Modelling of microservices applications and many more…
 Key Publications:
• [2019, ICPE ] A. Jindal, V. Podolskiy, M. Gerndt "Performance Modeling for Cloud Microservice Applications”.
• [2018, CLOUD] Vladimir Podolskiy, Anshul Jindal and Michael Gerndt. IaaS Reactive Autoscaling Performance
Challenges. The 2018 IEEE International Conference on Cloud Computing (CLOUD 2018).
• [2017, SC2 ] Anshul Jindal, Vladimir Podolskiy and Michael Gerndt. 2017. Multilayered Cloud Applications
Autoscaling Performance Estimation.
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We are…
Anshul Jindal |Windsurfing with APPA : Automating Computational Fluid Dynamics Simulations of Wind Flow using Cloud Computing | PDP 2020 | 13th March 2020
Prof. Dr.
Michael Gerndt
Ph.D Student
Vladimir Podolskiy
Ph.D. Student
Anshul Jindal
+ Other Students

 Introduction
 Motivation
 Goals
 Related Work
 Developed Solution
 Evaluation and Results
 Conclusion and future work
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Outline

Introduction
Anshul Jindal |Windsurfing with APPA : Automating Computational Fluid Dynamics Simulations of Wind Flow using Cloud Computing | PDP 2020 | 13th March 2020 4

 Wind loading on buildings is a potential cause for structural damages and collapses.
 Wind Engineering investigates the impact of wind flow on buildings in different
environments.
 Wind tunnel-based physical testing was established as the standard to evaluate the
impact of wind.
 Computational fluid dynamics (CFD) uses numerical analysis to conduct simulations of
turbulent wind flow around structures.
 It can serve as an alternative approach to conventional wind tunnel testing to assess the
wind flow around tall buildings.
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Introduction

 Computational fluid dynamics (CFD) is a branch of fluid mechanics which deals with
numerical solutions of various fluid problems.
 The flow of air around tall buildings is classified as an unsteady turbulent Newtonian
fluid nearly always in subsonic range and therefore incompressible Navier-Stokes
equation is used to model it.
 To perform numerical simulations for buildings,
a so-called virtual wind tunnel is used.
 It is a three-dimensional space that encloses the
geometry of the building and are discretized into
computational cells.
 Process of converting the flow problem from a physical statement:
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CFD in wind engineering on tall buildings
Physical
System
Mathematical
Model
Discrete
Model
Numerical
Approximation
Computed
Solution

Motivation
Anshul Jindal |Windsurfing with APPA : Automating Computational Fluid Dynamics Simulations of Wind Flow using Cloud Computing | PDP 2020 | 13th March 2020 7

 CFD can serve as a complementary approach to conventional wind tunnel testing to
assess the wind flow around tall buildings.
 CFD simulations conventionally run on supercomputers and compute clusters using
specialized software such as OpenFOAM.
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Motivation
Limited availability and high maintenance
costs of supercomputers and clusters.
Use of on-demand offer of compute capacity by Cloud
Require extensive expertise and experience in working
with cloud computing.
Problem

Goals
9Anshul Jindal |Windsurfing with APPA : Automating Computational Fluid Dynamics Simulations of Wind Flow using Cloud Computing | PDP 2020 | 13th March 2020

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Goals
Use compute capacity of the Cloud for
running simulations.
Automatic deployment and
execution of CFD simulations.
Estimate the optimal number of vCPUs
based on running time and cost.
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2
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Related Work

 J. Slawinski, T. Passerini, U. Villa, A. Veneziani, and V. Sunderam,“Experiences with
target-platform heterogeneity in clouds, grids, and on-premises resources,” in2012 IEEE
26th International Parallel and Distributed Processing Symposium Workshops PhD
Forum, May 2012,pp. 41–52
• Ported two production CFD based simulations on four different platforms (one of which was
Amazon EC2)
• Claimed Cloud laaS resources can be utilized for scientific CFD simulations possibly at lower cost.
• Deployed manually due to cumbersome nature of the requirements and it took them up to a day
for the deployment only.
R. Ledyayev and H. Richter, “High performance computing in a cloud using
openstack,”Cloud Computing, pp. 108–113, 2014.
• Benchmarked OpenFOAM on OpenStack platform.
• Found that OpenStack platform might not be well-suited for running OpenFOAM based
simulations due to the performance degradation.
• Claimed certain measures like changing the hardware and running multiple tests can improve the
performance.
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Related Work

 O'Leary, Patrick et al. “HPCCloud: A Cloud/Web-Based Simulation Environment.” 2015
IEEE 7th International Conference on Cloud Computing Technology and Science
(CloudCom) (2015): 25-33
• Allows to create or define a simulation using the given input decks and then it submits to a cluster
in the Cloud for processing
• We have left the simulation creation to the domain expert and other stuff like deployment,
running of simulation, monitoring etc. are off-loaded from the user to the developed system.
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Related Work Cont..

Developed Solution

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Overall System Architecture
Mongo
DB
Test data
saved
Resources Monitoring
data saved
Influx
DB
Output
UserInterface/APIInterface
Visualizationand
tables
Application
Configuratio
n
DeploymentConfiguration
Application
Source
Application
Deployment
AWS Cloud Interface
Google Cloud Interface
Application Deployer
Application Container
Monitoring Agent
Virtual Machine
Cloud Bucket
Test
Start
APPA
Cloud Service Provider

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Automated Parallel Processing Application (APPA) Workflow
OpenFOAM simulation source code of the
building
Configuration of simulation parameters
VM “1”
Running of simulations on the Cloud
VM “2” VM “n-1” VM “n”
Simulation results storage
Analysis, visualization and download
Monitoring data storage
…

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Dashboard screenshot for starting the simulation
Input Configuration ParametersChoose Cloud Service
Provider

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Dashboard screenshot for viewing simulation‘s status
Type of
Instance
Download
Simulation
results
Monitor VM in real
time
Current Status of
Simulation (number of
timesteps completed)

Evaluation

 Instances equipped with 150GB of storage.
 OS used was Ubuntu 18.04-LTS-bionic
 Docker version 18.03 CE and Docker-Compose version 1.23.1 was used.
 OpenFOAM Docker image version v1812 was used.
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Experimental Configurations (System)
Cloud
Provider
Instance Type vCPU Memory (GiB)
AWS c4.8xlarge 36 60
GCP
n1-highcpu-32 32 28.8
n1-highcpu-64 64 57.6
n1-highcpu-96 96 86.4

 The simulated building is the Commonwealth Advisory Aeronautical Council (CAARC)
standard tall building model.
 The building geometry consists of a prismatic shape with a rectangular cross-section.
 The domain itself was decomposed into four zones with gradually refined grid sizes.
 Two different grid resolutions, both with hexahedralcells, were created:
554,542 cells for Mesh A and 1,476,020 cells for Mesh B
 Number of time steps was equal to 6,250 for each simulation.
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Experimental Configurations (Simulation)

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Performance analysis for Mesh A with different objectives
16 Cores AWS32 Cores GCP (n1-32)36 Cores GCP (n1-64) 10 Cores AWS10 Cores GCP (n1-64)12 Cores GCP (n1-32)
Objective: Reducing Average runtime per timestep Objective: Reducing Total Cost
Overall Best: 36 cores of n1-highcpu-64 (1.17s) Overall Best: 10 cores of n1-highcpu-64 (78.55 USD)

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Performance analysis for Mesh B with different objectives
42 Cores GCP (n1-96) 28 Cores GCP (n1-96)
Objective: Reducing Average runtime per timestep Objective: Reducing Total Cost
Overall Best: 42 cores of n1-highcpu-96 (2.55s) Overall Best: 28 cores of n1-highcpu-96 (298.48 USD)

Conclusion

 Cloud computing provides a valuable alternative for running CFD-simulations on-
demand.
 The introduced APPA tool provides a user-friendly interface for conducting CFD-
simulations and deployment, running of simulation, monitoring etc. tasks are off-loaded
from the user.
 Results show that:
• use of GCE instance types can result both in low total cost (78.55USD) and in low
execution time per timestep (0.77seconds).
• lowering the number of cores results in the reduction of the total cost.
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Conclusion

 The monitoring of the simulation time and of the resources usage to enable the
optimization of resources by applying early stopping.
 Deriving the model that relates domain-specific parameters used for CFD-simulations to
the total cost and the execution time.
 Porting of this platform on a serverless computing framework so that individual
simulations can be run as a function.
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Future Work

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Contact
anshul.jindal@tum.de
/ansjin
/Anshul_Jindal4
Thank you for your attentention!
Questions ?
Scan for checking the
tool on GitHub
https://github.com/Cloud-Pie/APPA

Windsurfing with APPA: Automating the Computational Fluid Dynamics Simulations of Wind Flow using the Cloud Computing

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