Cloud CAE...On-Demand Scalable Cloud CAE Private Hybrid Public Actionable Data HPC AI Stanford...
Transcript of Cloud CAE...On-Demand Scalable Cloud CAE Private Hybrid Public Actionable Data HPC AI Stanford...
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Cloud CAE A Game Changer for Engineers
Ian Campbell
CEO, OnScale
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HPC AI Stanford Conference 2
1940’s 1960’s 1980’s 2020’s
Engineers: Making Science Fiction Reality Since 10,000 B.C.
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Design Simulatefor Days/Weeks/Months…
Computer Aided Engineering
3
CAE Should Answer: “How can I engineer the BEST design?”
Analyze ResultsIdea
(Repeat over and over and over and over…)
100B Core-Hours/Year, 99% On-Premise
HPC AI Stanford Conference
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The Solution:
4
Faster!Results
FREE CAE SOFTWARE
NO L ICENSES, NO IT, NO HW
Engineering Problems
Engineering Results
than Legacy
CAE Solutions
Source: 1000’s of Customer Benchmarks
SAAS, ON-DEMAND, SCALABLE
WORLD-CLASS SOLVERS
On-Demand Scalable Cloud CAE
Private
Hybrid
Public
Actionable Data
HPC AI Stanford Conference
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OnScale at a Glance
Founded in 2017 by world-class engineers/entrepreneurs
Venture backed and growing quickly
Focusing on Future of Engineering:
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Work-
flow
Cloud
HPC
Solvers
AI
Disruptive Cloud CAE Platform
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BioMed Devices ADAS Sensors/Systems
IoT Sensors 5G RF Components
Advanced Therapies Consumer Ultrasound 3D Ultrasound Driver Monitoring
Initial Applications: High Tech
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Multi-Billion Dollar Markets, Big, Bursty CAE Workloads
MEMS Mics Gesture Sensors RF Filters MEMS RF Switches
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The Multi-Billion Dollar Pain
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Month 1 Month 2 Month 3 Month 4 Month 5 Month 6
Risk
Fab Device
Design
Freeze
Device
Verification
Go or
No-Go
MPSystem Verification
If “No-Go”
then Reiterate
(and spend another $1M …)
Traditional Design Cycle = Too Much Risk, Time, and Cost
CONFIDENTIAL
CAE
<10 S
ims
Device-Only CAE:
No System Insights
Up-front
CAE
Bottleneck
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Cloud CAE → Minimize Risk, Time, and Cost
e
The Solution
8
Month 1 Month 2 Month 3 Month 4 Month 5 Month 6
Device
Verification Go!
>1M
Sim
s
CONFIDENTIAL
CAE
Cloud CAE Breaks
Bottleneck
Fab Device
Minimize Protos
& Short Loops
System Verification
Head Start on
Algos, FW/SW
Reduce
Time
MP
More
Design Wins!
Design
Freeze
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Use Case: 5G RF Front-Ends
5
Full 3D Simulations: Impossible with On-premise CAE
Optimize Next Gen 5G
Filters in Full 3D
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Use Case: Fingerprint Sensors
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Massive Sim Studies Reduce Risk, Cost and Time to Market
Simulates Sensor AND System
Effects in Full 3D
Simulation Data Enhances AI Algos
for Fingerprint Recognition
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Enhanced Engineering Workflows
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Example: IoT Sensor Design Plugins
EDA for ASIC Design
MCAD for Sensor Design
Iterate
Design
Iterate
Design
EDA Plugin
CAD Plugin
Problems
Results
ProblemsResults
Data
• On-Demand HPC
• Data Storage
• Version Control
• Central Data Repo
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Disruptive SaaS Sales Model
Other Benefits: No IT, No Licenses, No Hardware, One-hour Setup, Free GUI
for Model Setup & Post Processing, Free Training/Examples
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Cloud CAE = No SW LicensesCloud CAE breaks licensing constraints
– Run massive numbers of sims in parallel
– Multi-tenant, supports large engineering teams
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Legacy CAE
1 Engineer 1 License1 PC
1 Sim
Limited Data
Cloud CAE
Many
Engineers
“Infinite”
Hardware
Many, Many
Simulations
Wealth of Data
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Unified Pricing Model
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Why Solver Efficiency Matters
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Low Med High Very High
Solv
e T
ime
Min
Hrs
Days
Problem Size (DoF)
OnScale Scales with Problem Size – Legacy Competition Does Not:
100k 10M 100M1M
Wks
Advantages:
• Solve Time
• Cloud Compute Cost
Limited
Practicality
(takes weeks)
High Cloud
Compute
Cost Barrier
Impossiblewith Legacy(too large for any system)
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Fully Coupled Physics“Fully Coupled”: all physics algos on same finite element mesh
– Reduces CPU and RAM requirements
– Reduces overall solve time and Cloud Compute cost
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Legacy CAE OnScale
Mechanical Electrical EM
Decoupled solvers, serial solve,
separate FEM matrices
FEM1 FEM2 FEM3
Mechanical
Electrical
EM
Single
FEM
Fully coupled solvers, parallel
solve, single FEM matrix
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OnScale Cloud MPI Acceleration• MPI splits model across 10,000’s of cores
• Unparalleled acceleration on Cloud HPC
• Previously impossible problems now Solvable
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Legacy CAE
• Not possible, too large
• Even 1M DoF problems
take hours/days
OnScale
Cores 900
Runtime 47 min
3D BAW Model, 100 MDoF
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OnScale MPI Acceleration
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With MPI, simulation acceleration is linear with additional cores
212
210
28
26
24
22
22 24 26 28 210 212
CPU Cores
Accele
rati
on M
ult
iple
Competition
Diminishing
Returns
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Why the Time is Right
Cloud CAE is Enabled by New Cloud tech:– Kubernetes clusters (2015)
– Docker container system (2016)
– True Cloud “Bare Metal HPCs” (2017)
– Advanced token “licensing” (2017)
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Big-Compute is waiting for
the Perfect Cloud CAE App– Ground-up physics coupling
– Parallelism built-in from day 1
– Move 100B core-hours to Cloud
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Questions?
CAE is a massive HPC workload world wide
99% of of CAE is on-premise, Legacy model
In 3-5 years, majority will be on Cloud HPC
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