The document discusses the concept of simulation informatics as a data-driven methodology to enhance scientific analysis by using inexpensive data computations rather than costly high-performance computing (HPC) simulations. It outlines the integration of MapReduce technology to manage and analyze large volumes of simulation data, enabling engineers and analysts to extract meaningful insights efficiently. The presentation emphasizes the importance of data in scientific inquiry and the potential for developing surrogate models that enhance simulation fidelity.

1. SIMULATION INFORMATICS
A data-driven methodology for
synthesis from simulation results
David F. Gleich
John von Neumann Postdoctoral Fellow
Sandia National Laboratories
Livermore, CA
David Gleich (Sandia) 5/5/2011 1/18

2. A bit more background on me
Before
Undergrad Harvey Mudd College (Joint CS/Math)
Internships Yahoo! and Microsoft Internships
Graduate Stanford (Computation and Mathematical Engineering)
Thesis: Models and Algorithm’s for PageRank Sensitivity
Internships Intel, Joint Library of Congress/Stanford, Microsoft
Post-doc 1 Univ. of British Columbia
Now
Post-doc 2 2010 John von Neumann fellow @ Sandia
In August
Tenure-Track position at Purdue’s CS department
David Gleich (Sandia) 5/5/2011 2/18

3. It’s time to ask:
What can science
learn from Google?
-- Wired Magazine (2008)
http://www.wired.com/science/discoveries/magazine/16-07/pb_theory
David Gleich (Sandia) 5/5/2011 3/18

4. A tale of two computers …
Cielo (#10 in top 500) SNL/CA Nebula Cluster
Separate storage system 2TB/core storage
This platform is awesome This platform is awesome
for simulating physical systems for working with data
Cost ~$50 million Cost $150k
David Gleich (Sandia) 5/5/2011 4/18

5. Simulation:The Third Pillar of Science
21st Century Science in a nutshell
Experiments are not practical / feasible.
Simulate things instead.
But do we trust the simulations?
We’re trying!
Model Fidelity
Verification & Validation (V&V)
Uncertainty Quantification (UQ)
Insight and confidence requires multiple runs.
David Gleich (Sandia) 5/5/2011 5/18

6. The problem Simulation ain’t cheap!
We can throw the numbers into
21.1st Century Science
the biggest computing clusters the in a nutshell?
world has ever seen and let Simulations are
statistical algorithms find patterns too expensive.
where science cannot. Let data provide a
--Wired (again) surrogate.
The Database
Input Time history s1 -> f1
Parameters of simulation
s2 -> f2
s f
sk -> fk
David Gleich (Sandia) 5/5/2011 6/18

7. Our CSRF project Simulation Informatics
The People
Myself
Paul G. Constantine
(2009 von Neumann)
To enable analysts and
engineers to hypothesize from
inexpensive data computations
instead of expensive HPC
computations.
Stefan Domino
Jeremy Templeton
Joe Ruthruff
David Gleich (Sandia) 5/5/2011 7/18

8. The idea and vision: Store the runs!
Supercomputer MapReduce cluster Engineer
Each multi-day HPC A MapReduce cluster can … enabling engineers to query
simulation generates hold hundreds or thousands and analyze months of simulation
gigabytes of data. of old simulations … data for statistical studies and
uncertainty quantification.
David Gleich (Sandia) 5/5/2011 8/18

9. MapReduce
Originated at Google for Data scalable
indexing web pages and Maps M M
1 2
1 M
computing PageRank 2 M
Reduce
M M
R 3 4
3 M
M R
4 M
The idea 5
5 M Shuffle
Bring the computations to the data.
Express algorithms in Fault tolerance by design
data-local operations. Input stored in triplicate
Reduce input/
Implement one type of M
output on disk
communication: shuffle. M R
M
Shuffle moves all data with R
M
the same key to the same M Map output
reducer persisted to disk
before shuffle
David Gleich (Sandia) 5/5/2011 9/18

10. Mesh point variance in MapReduce
Three simulation runs for three time steps each.
Run 1 Run 2 Run 3
T=1 T=2 T=3 T=1 T=2 T=3 T=1 T=2 T=3
David Gleich (Sandia) 5/5/2011 10/18

11. Mesh point variance in MapReduce
Three simulation runs for three time steps each.
Run 1 Run 2 Run 3
T=1 T=2 T=3 T=1 T=2 T=3 T=1 T=2 T=3
M M M
1. Each mapper out- 2. Shuffle moves all
puts the mesh points values from the same
with the same key. mesh point to the same
R R reducer.
3. Reducers just compute
a numerical variance.
Bring the computations
to the data.!
David Gleich (Sandia) 5/5/2011 11/18

12. Hadoop???
Hadoop: an open-source
MapReduce system
supported by Yahoo!
Hadoop was the name
of Doug Cutting’s
son’s stuffed elephant.
Photo credit, New York Times (2009)
http://www.nytimes.com/2009/03/17/tech
nology/business-computing/17cloud.html
David Gleich (Sandia) 5/5/2011 12/18

13. MapReduce and Surrogate Models
A surrogate model
is a function that
reproduces the
f1 Surrogate
output of a simul-
Sample
ation and predicts
its output at new f2
parameter values.
f5
The Database New Samples
The Surrogate
s1 -> f1 Extraction Interpolation sa -> fa
s2 -> f2 sb -> fb
sk -> fk Just one machine
sc -> fc
On the MapReduce cluster On the MapReduce cluster
David Gleich (Sandia) 5/5/2011 13/18

14. MapReduce + Simulations
Thermal Race problem
Joe Ruthruff and Jeremy Templeton
Heating an unclassified NW model to simulate a fire
Goal make sure the weak link fails
before the strong link
Objective determine where
parameters are interesting 30 min per run
Parameters material properties 700GB of Raw Data
~64GB of data for
1000 sample MapReduce
parameter
study using Our results
Extraction 30 min
Interpolation 5 min
David Gleich (Sandia) 5/5/2011 14/18

15. Data driven Green’s functions
The simulation varies most in the corners of the parameter space.
These analyses will help understand which parameters matter,
and where to continue running the simulations.
David Gleich (Sandia) 5/5/2011 15/18

16. What can science
learn from Google?
The importance of data.
For more about this, see a talk
and paper by Peter Norvig
“Internet-Scale Data Analysis”
http://www.stanford.edu/group/
mmds/slides2010/Norvig.pdf
Halevy et al. “The unreasonable
effectiveness of data.” IEEE
Intelligent systems, 2009.
David Gleich (Sandia) 5/5/2011 16/18

17. The future?
MapReduce computers
embedded within super
computers as the parallel file
system and data analysis
platform.
The best of both worlds.
Our work
Laying the foundation for useful
surrogate models that will
make data based simulation a
compelling addition to the
simulation revolution.
Any questions?
David Gleich (Sandia) 5/5/2011 17/18