Présentation "Know the wind" - Big Data Analytics - Smarter Analytics Live - 5 juin 2012 - par Anders Rhod Gregersen - Vestas

1. Know the wind
Big data analytics at Vestas Wind Systems A/S
Anders Rhod Gregersen
agreg@vestas.com
Senior specialist, Vestas Wind Systems A/S
5th of June 2012, Anders Rhod Gregersen, Vestas Wind Systems A/S

2. Presentation Outline
Introduction
• Introduction to Vestas
What are we trying to achieve?
• Finding a good site and HPC
How do we do it?
Q&A
• Forecasting
• Usability
• Building HPC capability
• Road ahead
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3. Marked leader in Wind turbines
Wind only company
Install base approx. 50GW / 50.000 wind turbines
22.000 employees
World wide
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4. Why high performance data heavy computing?
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5. Why HPC and Big data?
Renewable vs base production
• Predictability
• Integration
Significant investment
What matters to the customer?
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6. The complexity of business case certainty
Cost of Energy
Wind Resource
Service Cost
Turbulence
Complexity
Height contours
Site picture
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7. Finding a good site
Traditional process:
Point measurements (met mast)
Point estimate of wind resource
Point estimate of turbulence
Drawbacks:
Costly
Time consuming
Point measurement of flow
No weather context
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8. Understand wind ressource
Hindcasting the weather
Very high area resolution
Very high time resolution
Unlike weather services, the model is
sensitive to wind
Time series from turn of millenium
onwards
Approx. 200 parameters
Wind measurements in context
Extreme events
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9. Understand wind turbulence
Complex vs simple terrain
Turbulence and fattigue
Point measurement of turbulence
Modeling via Computational Fluid Dynamics
Moving from point to flow
Wind shear
In-flow angles
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10. Flow over terrain
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11. Architecture
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12. Architecture overview
Compute:
1.306 compute nodes
2.612 Intel X5670 CPUs
15.672 X5670 cores
>34TiB RAM
>161TFLOPS
10.752 M2070Q cores
Interconnects:
4xQDR Infiniband (40Gb)
2:1 over-subscribed
Ethernet
Storage:
1.680 spindles
14 I/O servers
>20GB/s
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13. Primary memory
Modeling fluid flow means
Solving Navier-Stokes equeations which means
Memory bound code
On die memory controller
One bank, three ranks/CPU
Remote memory via Message passing interface (MPI)
MPI via Infiniband
Low latency / high bandwidth
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14. Secondary memory
GPFS – general parallel filesystem
Freedom in both bandwidth and capacity
Singular name space
I/O moves via Infiniband
Exposed to the OS as POSIX
Backup vs snapshots
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15. Analytics at Vestas
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16. Building database technology that scales beyond petabytes
Use cases:
Point queries: user wants the full time series for the four adjacent points
Data exploration: meteorologist queries region or world for existence or frequency of
phenomenon
Forensics: Engineer is doing a post-mortem on high frequency data from a wind
turbine
Regular database/warehouse has problems scaling (conceptually/economically)
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17. Business challenge
What we have
200 parameters
Hourly measurements
Time series since 2000
What we need:
Relational database functionality
Fast point queries
Scalable queries
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18. Facts:
Nature of data:
Volume, Velocity
Assumption, all data time series
Assumption, common query full time series
Optimization: partition elimination
Optimzation: parameter elimination
Solution: a column based
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19. Enter IBM InfoSphere BigInsights
Partnership with IBM
Joint development with
IBM Almaden (research) and
IBM Silicon Valley Lab (productization)
Weekly meetings
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20. Advantages of a partnership:
Software developed that matches our need
Software lifecycle in handled by IBM
Software productization
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21. Thank you for your attention
Copyright Notice
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22. Usability challenge
HPC grew out of academia
What to expect from users?
Users range from PhDs
….to saleforce
One common tool for common users
….enables tracability
Point and click HPC for regular users
Terminal based interaction for the
power user
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23. Building up HPC capability
Q1/2003
First commercial license for CFD
Q4/2006
First cluster (40 cores)
Q3/2007
CFD model validated.
Q4/2008
Second cluster (15 TFLOPS, TiBs)
Q2/2011
Third cluster (3rd largest industrial HPC, PiBs)
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