Cloud hpc-bigdata-challenges

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Microsoft Azure for Research

presentation for keynote at NSF Cloud Workshop for Chameleon and Cloudlab

Data & Analytics

gannon@Indiana.edu
Dennis.gannon@outlook.com

IT PAC

Melbourne
Sydney
Brazil
Beijing

Programming tools: Scala, IPython, Azure ML, …
Frameworks: Spark, Hadoop, Yarn, HDInsight, Reef, Twister, Brisk
Software Defined Storage
Software Defined Networks
Hardware Abstraction/Virtualization

http://tce.technion.ac.il/files/2012/06/Scott-shenker.pdfwww.opennetsummit.org/pdf/2013/presentations/albert_greenberg.pdfhttp://www.cs.princeton.edu/~jrex/papers/pyretic-login13.pdf

The Science Perspective

Every research field is now a data science field

Last
few decades
Thousand
years ago
Last few Today and the Future
hundred years
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Simulation of
complex phenomena
Newton’s laws,
Maxwell’s equations…
Description of natural
phenomena
Unify theory, experiment and
simulation with large
multidisciplinary Data
Using data exploration and
data mining
(from instruments, sensors,
humans…)
Distributed Communities

Video Link

Inputs (training data)
Labels
Hidden layers
Input data
Detected features
Mona Lisa

•The Genetic Causes of Disease (David Heckerman)
•WellcomeTrust for a GWAS for a large population
•Looking for causes for seven common diseases (bipolar, r. arthritis, coronary, hypertension, ….)
•Confounding is a problem. Needed a new algorithm.
•Ran on Azure cloud using 35,000 cores in 3 weeks.

Chameleon Cloud
SDN
NIH data commons

Mesos
Tachyon
Docker
Spark
Data Analytics and ML programming tools
Reef
Twister

•Many Examples
•The Challenge: sustainability
Data
Acquisition & modelling
Collaboration and visualisation
Analysis & data mining
Dissemination & sharing
Archiving and preserving

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Cloud hpc-bigdata-challenges

1. gannon@Indiana.edu Dennis.gannon@outlook.com

2.

3.

5.

6. Melbourne Sydney Brazil Beijing

7. Programming tools: Scala, IPython, Azure ML, … Frameworks: Spark, Hadoop, Yarn, HDInsight, Reef, Twister, Brisk Software Defined Storage Software Defined Networks Hardware Abstraction/Virtualization

8. http://tce.technion.ac.il/files/2012/06/Scott-shenker.pdfwww.opennetsummit.org/pdf/2013/presentations/albert_greenberg.pdfhttp://www.cs.princeton.edu/~jrex/papers/pyretic-login13.pdf

9.

10. The Science Perspective

11. Every research field is now a data science field

12. Last few decades Thousand years ago Last few Today and the Future hundred years 2 2 2 . 3 4 a G c a a               Simulation of complex phenomena Newton’s laws, Maxwell’s equations… Description of natural phenomena Unify theory, experiment and simulation with large multidisciplinary Data Using data exploration and data mining (from instruments, sensors, humans…) Distributed Communities

14.

15.

16.

17.

18.

19. Inputs (training data) Labels Hidden layers Input data Detected features Mona Lisa

20.

21. •The Genetic Causes of Disease (David Heckerman) •WellcomeTrust for a GWAS for a large population •Looking for causes for seven common diseases (bipolar, r. arthritis, coronary, hypertension, ….) •Confounding is a problem. Needed a new algorithm. •Ran on Azure cloud using 35,000 cores in 3 weeks.

22.

23.

24. Chameleon Cloud SDN NIH data commons

25. Mesos Tachyon Docker Spark Data Analytics and ML programming tools Reef Twister

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27.

28.

29. •Many Examples •The Challenge: sustainability Data Acquisition & modelling Collaboration and visualisation Analysis & data mining Dissemination & sharing Archiving and preserving