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Performing simulation based, real time decision making with cloud HPC Zack Smocha, April 2016
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Agenda
• Rescale overview • Evolution of simulation • Simulation in service • F1 simulations • Manor Racing case study
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HQ San Francisco, USA , Japan office rapid growth
Global simulation cloud HPC platform 30+ data centers, 120 simulation software
Over 100 leading enterprises - automotive, aerospace, energy and life sciences
Headquarters
Technology
Customers
Investors
Rescale - Company Overview
Peter Thiel Jeff Bezos Richard Branson
... and several other industry leaders, technology experts, and experienced executives
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Rescale Cloud HPC Enterprise Simulation Platform
So#ware
120+ software packages
Mul,-clouds
30 varied location and HW availability
Workflow
Administra,on
Security
Compliant, data and user
Manage usage access and cost
Experienced team seamless workflow
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Simulations in Industry
Aero
spac
e
Auto
mot
ive
Life
Scien
ces
Oil &
Gas
Indu
stria
ls
Sem
icond
ucto
r
• Complexturbine• Wingdesigns• Modelling
propulsion
• Crashsimula-on• Engine
computa-onalfluiddynamics
• Reservoirsimula-onworkflows
• Hydrocarbontraps
• Gene-cengineering
• Isola-onofgene-ctraits
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Evolution of Automotive Simulation Co
mpl
exity
1960 1970 1980 1990 2000 2010 Today
Vehicle Dynamics
Crash Analysis
FEA
Multiphysics High-fidelity Ensemble analysis
CFD & HPC
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Simulation in the Product Life Cycle
Predict behavior without actually testing it in real life
Validate and optimize the design of parts and manufacturing
Using real data to help make real time decision
Engineering Design Manufacturing In Service/Production
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Simulation In Service - Time Spectrum
Real-me“nextmove”gaminganalysis
Threads CPU GPU ELO
24 764 112 3,079
40 1,202 176 3,140
64 1,920 280 3,168
Maintenanceandabnormalbehavior
Usingreal-metracksidedataforracestrategy
Makesureyoudon’tcrashthebus
Days/hours Minutes MicrosecondsSeconds
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F1 Results Australia - Many Strategies
Superso# So# MediumHard Wet Intermedium Used=PitStop
• BasedinBanburyUK• PartnerswithMercedes-Benzenginetechnology• WilliamsAdvancedEngineeringfortransmissions
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Manor Racing
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Manor use case - Goals
• Best-metotakeapitstop• What-restofitforthenextstageoftherace.• Secondguessthecompe--ontotryandgainraceposi-onthroughbe^erpitstop-me
For Manor Racing it is about meticulous attention to detail, eking out every single opportunity to find every single gap. Car and driver, factory and team
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Manor use case - Users
DaveRyan-RacingDirector
JamesKnapton-HeadofVehicleScience
Strategyengineerswhoadvisetheraceengineersontheop-mumstrategyastheraceisdeveloping
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HPC Simulation in Service - Requirements
• Collect the data in real time? • Insert the data into the system? • Upload the data to the cloud HPC? • Best HW for fast simulation? • Download and access the data?
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Manor Cloud HPC Architecture
Cloud HPC Cluster
Head NodeHPC Scheduler
Compute NodesNodes are joined to the HPC Scheduler
Virtual Network LAN
IPSec VPN
Manor application GUI
• For optimization jobs directly interact with the HPC cluster • Clients running jobs join the head node domain and mount the shared file system
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Input Parameters and Live Data
• Parameters: lap time, tire degradation rate for each tire compound, expected car performance as fuel level reduces
Make a live decision based on the
simulation results and enter actual track
side results
Collect live track side data and run the
simulation
Make a live decision based on the
simulation results and enter actual track
side results
Collect live track side data and run the
simulation
• Example of live Input data: Actual lap time, tire degradation
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Input Parameters
• How do I collect the data input in real time – Data is available from the track side
• Insert the data to the system – User enters the data into the Manor
application interface, application generates input size files ~500kB
• Upload the data – Data is uploaded to the head cluster node
from the user laptop
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Simulation Benchmark - Best HW for Fast Simulation
• Simulations based on Monte-Carlo methods • Response < 45-50 sec • Run thousands of race simulations per minute, repeat
this process over and over throughout the race
#cars #cores Strategies Permuta-ons Itera-ons Running-meonthecluster
1Car 500 30 100 100 32.27
1Car 500 30 300 20 69.58
1Car 500 30 150 20 31.69
1Car 500 90 20 20 31.82
1Car 500 90 20 100 35.61
1Car 750 30 100 100 31.80
1Car 1500 30 100 100 30.75
2Cars 750(each) 30 100 100 35.03
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Running the Simulation
• Clusters are running the whole race
• 3000 tasks • Hardware:
• 1500 cores • 16 CPU per node • 98 nodes • Haswell CPU
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The Results
• Output: Optimum race time • Results size is 5MB • Results are download to the user PC • User views results in the Manor GUI App • Using the results in practice: Decide when would be the
best time for a pit stop
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The Results
• Each curve is a different tire choices • Each # represent a pit stop and the lap to stop • POA: Prime/Option/Alternate : Hardest to the softest
Op,mumrace,me–Sensi,vityto,restrategy
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Simulation defines our future, join us in helping build a better world.
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Simulations in F1
• Windtunnel,aerodynamic• CFDandFEA• 2014FIAregula-ons
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F1 the Art of Tire Change
• 15-19people• Stopbelow3sec• Dootheradjustments