nikravesh australia long_versionkeynote2012

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Computational Science and Engineering (CSE) @ Berkeley The Emergence of Computation for Interdisciplinary Large Data inspired by Science Bounded by our imagination innovation through Technology Create Social impact Masoud Nikravesh @ CITRIS and LBNL CITRIS Director for CSE Executive Director, DE-CSE @ Berkeley http://cse.berkeley.edu/ http://cloud.citris-uc.org/ http://citris-uc.org/ http://www.lbl.gov/cs Health, Freshwater, Food Security, Ecosystems, and Urban Metabolism 1

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Page 1: Nikravesh australia long_versionkeynote2012

Computational Science and Engineering (CSE) @ BerkeleyThe Emergence of Computation for Interdisciplinary Large Data

inspired by Science Bounded by our imagination innovation through Technology Create Social impact

Masoud Nikravesh @ CITRIS and LBNL

CITRIS Director for CSE

Executive Director, DE-CSE @ Berkeley

http://cse.berkeley.edu/ http://cloud.citris-uc.org/

http://citris-uc.org/ http://www.lbl.gov/cs

Health, Freshwater, Food Security, Ecosystems, and Urban Metabolism

1

Page 2: Nikravesh australia long_versionkeynote2012

Outline of Talk

2

Drivers for Change: Computing and Big Data

Computational Science and Engineering

State Leadership

California – “The Golden State”

The State New Economy Model

“Sustainable California” –a return to “The Golden State”

Page 3: Nikravesh australia long_versionkeynote2012

Outline of Talk

3

Drivers for Change: Computing and Big Data

Computational Science and Engineering

State Leadership

California – “The Golden State”

The State New Economy Model

“Sustainable California” –a return to “The Golden State”

Page 4: Nikravesh australia long_versionkeynote2012

Drivers for Change

• Continued exponential increase in computational power simulation (Computing) is becoming third pillar of science, complementing theory (Analytic and Math ) and experiment (Applications)

Applications

HPC-Cloud

Computing

Analytics

Math

High performance computing

(HPC), large-scale simulations,

and scientific applications all

play a central role in CSE.

CSE

The HPC/cloud computing initiative

and next generation data center

Extreme simulation, visual-data analytics,

data-enabled scientific discovery

Applications/real‐world complex applications (scientific, engineering, social, economic,

policy) using the future multi-core parallel computing ((i.e. E-Informatics, Earthquake Early

Warning, NextGenMaps, Genome Atlas, Genetic Facebook, Genomics Browser)

HPC-Petascale and Exascale

systems are an indispensable

tool for exploring the frontiers of

science and technology for

social impact.

4

Page 5: Nikravesh australia long_versionkeynote2012

Revolution is Happening Now

Chip density is continuing increase ~2x every 2 years

Clock speed is not

Number of processor cores may double instead

There is little or no more hidden parallelism (ILP) to be found

Parallelism must be exposed to and managed by software

Source: Intel, Microsoft (Sutter) and

Stanford (Olukotun, Hammond)5

Page 6: Nikravesh australia long_versionkeynote2012

Computing Growth is Not Just an HPC Problem

10

100

1,000

10,000

100,000

1,000,000

1985 1990 1995 2000 2005 2010 2015 2020

Year of Introduction

The Expectation Gap

Microprocessor Performance “Expectation Gap” over Time

(1985-2020 projected)

6

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New Processors Means New Software

Exascale will have chips with thousands of tiny processor cores, and a few large ones

Architecture is an open question: sea of embedded cores with heavyweight “service” nodes

Lightweight cores are accelerators to CPUs

Autotuning eases code generation for new architectures

Interconnect

Memory

Processors

Server Processors Manycore processors

130 Megawatts 75 Megawatts

Source: Kathy Yelick,7

Page 8: Nikravesh australia long_versionkeynote2012

Interconnect

Memory

Processors

New Memory and Network Technology to Lower Energy

Memory as important as processors in energy

Latency is physics, bandwidth is money

Software managed memory or cache hybrids

Autotuning has helped with that management

Need to raise level of autotuning to higher level kernels

Usual memory + network New memory + network

25 Megawatts75 Megawatts

Source: Kathy Yelick,8

Page 9: Nikravesh australia long_versionkeynote2012

TOP500 Sites – June 2011

Today, HPC-Petascale and soon Exascale systems- is not just a tool of

choice, but it becomes an indispensable tool for frontiers of science and

technology for social impact.

Petaflop with ~1M Cores in your PC by 2025?

9

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TOP10 Sites - June 2010

10

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TOP10 Sites - November 2010

11

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TOP10 Sites – June 2011

12

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TOP500 Sites – June 2011

Today, HPC-Petascale and soon Exascale systems- is not just a tool of

choice, but it becomes an indispensable tool for frontiers of science and

technology for social impact.

Petaflop with ~1M Cores in your PC by 2025?

8-10 years

6-8 years

13

Page 14: Nikravesh australia long_versionkeynote2012

goal

usual

scaling

2005 2010 2015

2020

Energy Cost Challenge for Computing Facilities

At ~$1M per MW, energy costs are substantial

1 petaflop in 2010 will use 3 MW

1 exaflop in 2018 possible in 200 MW with “usual” scaling

1 exaflop in 2018 at 20 MW is DOE target

14

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New Processor Designs are Needed to Save Energy

Server processors have been designed for performance, not energy

Graphics processors are 10-100x more efficient

Embedded processors are 100-1000x (1.25 rather than 100 watt)

Need manycore chips with thousands of cores

Cell phone processor

(0.1 Watt, 4 Gflop/s)

Server processor

(100 Watts, 50 Gflop/s)

Source: Kathy Yelick, HPC-SEG July 2011 15

Page 16: Nikravesh australia long_versionkeynote2012

Motif/Dwarf: Common Computational Methods

(Red Hot Blue Cool)

Em

be

d

SP

EC

DB

Ga

me

s

ML

HP

C

Health Image Speech Music Browser

1 Finite State Mach.

2 Combinational

3 Graph Traversal

4 Structured Grid

5 Dense Matrix

6 Sparse Matrix

7 Spectral (FFT)

8 Dynamic Prog

9 N-Body

10 MapReduce

11 Backtrack/ B&B

12 Graphical Models

13 Unstructured Grid

What do commercial and CSE applications have in common?

Source: Jim Demmel, Berkeley Parlab16

Page 17: Nikravesh australia long_versionkeynote2012

Source: Oliver Pell, HPC-SEG July 2011, Berkeley

CPU, GPU, Hybrid, FPGA?

17

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x86 Multicores GPU FPGA

Numbers -Current generation: 4–6 cores/CPU x 2

CPUs/node = 8–12 cores/node

-Future generation: 16–20 cores/CPU x 4

CPUs/node = 64–80 cores/node

-512 cores/GPU (Nvidia)

-1600 cores/GPU (AMD)

-No more cores but BRAM,

--Look Up Tables, FlipFlops,

etc..

-Clock frequency is in the

order of hundreds of MHz

-Memory per card is in the

order of tens of GB

What is the

easy part?

-Well known and mature technology

-Well established development environments

-Parallelism between core and nodes

-Well known technology (for

gaming purposes)

-It is becoming reliable also

for HPC computation

-High performance-per-watt

ratio

What is

difficult to do?

-Linear speedup with increasing core numbers -CUDA: good tool but

proprietary

-OpenCL: open technology

but not yet standard and more

complex to use

-Development tools (+

profiling, debugging, etc) not

yet fully available

-Non standard development

tools (VHDL is not for

Geophysicists… but we

have MaxCompiler!)

-Data streaming technology is

different from standard

approaches

(grid/matrix)

Main

problems

-Slow memory access

-Legacy codes need to be re-engineered in

order to get the best performance

(e.g. SSE vectorization, cache blocking)

-Network connections have to be optimized for

the architecture

-Limited amount of memory

(4–6 GB) per card

-Slow communication with the

host CPU (due to PCI

Express)

-Internal bandwidth is not

always enough

-The technology is not yet

standard for HPC

-Slow communication with the

host CPU (due to PCI

Express)

Source: Carlo Tomas, HPC-SEG, July 2011, Berkeley18

Page 19: Nikravesh australia long_versionkeynote2012

A Likely Trajectory - Collision or

Convergence?

CPU

GPU

multi-threading multi-core many-core

fixed function

partially programmable

fully programmable

future

processor

by 2012

?

pro

gra

mm

abili

ty

parallelismafter Justin Rattner, Intel, ISC 2008

19

Page 20: Nikravesh australia long_versionkeynote2012

Drivers for Change

• Continued exponential increase in experimental, simulation, sensors, and social data techniques and technology in data analysis, visualization, analytics, networking, and collaboration tools are becoming essential in all data rich applications

Big

DataModel

Human

Experts- Citizen Cyber Science

Crowdsourceing

Analytic ToolsFirst Principles Hybrid Models

Google

IBM-Watson

IBM- Cognitive Model

Boeing 747 Simulation

Protein Folding

Amazon AI-ImageIn

crea

sed

clim

ate/

envi

ronm

enta

l de

tail

Increased socio-economic detail

Tera

Peta

Peta

Exa

Socio-Economic Modeling

for Large-scale Quantitative

Climate/Environmental

Change Analysis

En Informatics

Environment-Genetic

20

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World Population: Today-~6B, 2050-~9B, 2100-~10B%70 will live in Cities by 2050

By 2020: 35 trillion Gigabytes Data (Cyber-Physical world is connected through

billions to even trillions of sensors and devices)

Petaflop with ~1M Cores in your PC by 2025?

Health, Freshwater, Food Security, Ecosystems, and Urban Metabolism

21

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Why BIG Data is a Big Deal?

Size of Data:

• 2010: 1.2 million Petabytes, or 1.2 Zettabytes

• 2020: 35 trillion Gigabytes (Cyber-Physical World is connected through

billions to even trillions of sensors and devices)

Type of data:

• from homogenous data to heterogeneous and multi-scale

• from physical sensor data to social-economical data

• from complete to incomplete, imprecise and uncertain

• from implementing on single-simple hardware-software

architecture to scalable parallel complex

hardware-software architectures

22

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Why BIG Data is a Big Deal?

Crisis: Data storage/transfer/communication and security-

privacy doomsday forecast

Opportunities: Information gold mine

Needs: better, faster, cheaper, and scalable technologies

for storage, manipulation, communication and analysis

23

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Why BIG Data is a Big Deal?

Challenge: Combine our current and to be developed

advanced-scalable* analytical tools with first principle

models and human capabilities at scale with anticipatory

capabilities to discover the un-seen phenomena and

insights and to make and deliver securely right decisions

and at the right time based on incomplete, imprecision,

and uncertain public/private data dealing with multi and

conflicting objectives and criteria.

24

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Why BIG Data is a Big Deal? Crowdsourcing

Big

DataModel

Human

Experts- Citizen Cyber Science

Crowdsourceing

Analytic ToolsFirst Principles Hybrid Models

Google

IBM-Watson

IBM- Cognitive Model

Boeing 747 Simulation

Protein Folding

Amazon AI-Image

Incre

as

ed

clim

ate

/en

vir

on

men

tal

deta

il

Increased socio-economic detail

Tera

Peta

Peta

Exa

Socio-Economic Modeling

for Large-scale Quantitative

Climate/Environmental

Change Analysis

En Informatics

Environment-Genetic

25

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Distributed thinking / Human computing

Physical participation coordinated via Internet

BIG Data and Citizen Cyber Science?

What can be aggregated?

Aggregate perception, knowledge, reasoning

Visual pattern recognition

Real-world knowledge

3D spatial manipulation

Language skills

Where to get Volunteers

Tell a good story about your research

Give recognition

Make it a game

Add a social dimension26

Page 27: Nikravesh australia long_versionkeynote2012

BIG Data and Citizen Cyber science?

27

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AMP: Algorithms, Machines, People

Adaptive/Active Machine Learning

and Analytics

Cloud ComputingCrowdSourcing

Massive

and

Diverse

Data

Source: M. Franklin

28

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Cloud Initiative at Berkeley

~120 Faculty (CSE), ~120 Researchers (Cloud-HPC) , 22 Departments

Data Structure

Analytics

Service Delivery

29

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CSE Cloud Computing Initiative

Cloud Computing are being used by a broad array of Computational Science and Engineering faculty investigators, researchers and graduate students from social scientists and economists to astrophysicist and Bioengineers. Our list of faculty includes experts from both computational science and engineering, and the cloud and HPC community. It includes ~120 faculty and over ~120 researchers/students from over 22 departments (http://cse.berkeley.edu and http://cloud.citris-uc.org/).

30

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Cloud Infrastructure

Applications (scientific, engineering, social, economic/business/finance, policy)

Delivery of Services

Mobile Devices Mobile CloudSoftware and Appliances

Cluster Scheduling &

Reliability

Network Research and

Security

Supercomputer

Public Cloud

Private Cloud

Volunteering Computing

Mobile Cloud

Streaming Data

Massive Data

Extreme Simulation

Large Scale Visualization

Machine Learning

Analytics

Intelligent Dynamic Maps

Early Warning

Social Networking

Second Life

Cyber Citizen

Personalized Services

Crowd Sourcing

Cloud Initiative at Berkeley~120 Faculty (CSE), ~120 Researchers (HPC-Cloud) , 22 Departments

31

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Cloud Initiative at Berkeley~120 Faculty (CSE), ~120 Researchers (HPC-Cloud) , 22 Departments

Infrastructure – Cloud Cluster and Data Centers

Delivery of Services – Mobile Cloud

Applications Scientific

Social

Economics/Business

Software and Appliances

Cluster Scheduling & Reliability

Network Research and Security

Mobile devices, Mobile Cloud, and Cloud Infrastructure

will be the device/tools of choice for delivery of services.

32

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Cloud Computing Initiative

We will focus on three main areas:

Machine Learning: Provide the general public with machine learning analytics tools and algorithm runs in cloud infrastructure.

Streaming Data Analytics and Visualization: Analyses and visualization of large-scale real time data sets such as traffic information, online news sources, economics data, and scientific data such as astrophysical and Genomics data.

Scientific Applications: Benchmarking and cataloging the suitability of cloud computing for science and engineering applications, including HPC applications.

33

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BIG Data and Sensors/Cyber-Physical Infrastructure

Water

Air

Energy

Earthquake

Marvell

Lab

μSensors

TinyOS

Prototyping

Devices

and

Sensors

G/H

FEEDBACK

California Independent System (Cal ISO)

Department of Water ResourcesCalifornia Department of Health and Social Services and FCC

Cyberspace

Handhelds

Laptop/PC

Clusters

IBM/ room143

Cloud

+

+

+

Analytics

Algorithms

M/C Learning/A.I.

Statistical Analysis

Social Comp

Knowledge

Insight

Large-Scale

Information

Extraction

Delivery and

Service

Back to

Handhelds

Distributed

Systems

Visualization, Analytics and Insight

Physical

World

Big Data

Streams

34

Page 35: Nikravesh australia long_versionkeynote2012

Incr

ease

d

clim

ate/

envi

ronm

enta

l de

tail

Increased socio-economic detail

Tera

Peta

Peta

Exa

Socio-Economic Modeling

for Large-scale Quantitative

Climate/Environmental

Change Analysis

En Informatics

Environment-Genetic

BIG Data and Exa-Scale Computing

35

Page 36: Nikravesh australia long_versionkeynote2012

Courtesy of U.S. Department of Energy Human Genome Program , http://www.ornl.gov/hgmis

BIG Data and DNA Computing

36

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BIG Data and DNA Computing

37

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BIG Data and DNA Computing

38

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BIG Data and Visualization –Scientific

39

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BIG Data and Visualization

40

Page 41: Nikravesh australia long_versionkeynote2012

Outline of Talk

41

Drivers for Change: Computing and Big Data

Computational Science and Engineering

State Leadership

California – “The Golden State”

The State New Economy Model

“Sustainable California” –a return to “The Golden State”

Page 42: Nikravesh australia long_versionkeynote2012

Computational Science

Nature, March 23, 2006

“An important development in

sciences is occurring at the

intersection of computer science and

the sciences that has the potential to

have a profound impact on science. It

is a leap from the application of

computing … to the integration of

computer science concepts, tools,

and theorems into the very fabric of

science.” -Science 2020 Report, March 2006

42

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Nature of Work, Education and Future Society

“Creative Creators” or “Creative Servers”: Do complex task, and Enhance, Refine, and Reinvent. “T. Friedman and M. Mandelbaum” That Used to be Us”

20th Century 21th Century

Number of Jobs1-2 Jobs 10-15 Jobs

Job Requirement

Mastery of

one Field

(Single Deep Expertise)

Breadth;

Depth in several Fields

(Multiple Deep Expertise)

(Broad Knowledge)

Alternative sources of Natural Resources: Energy and Water

Technology: Nano-technology, Quantum Computers, Genetic and Biometrics, and Robotics

Services: Online Education and Services on Demand

Resources: Sensors and Devices, Big Data, Computing Power, Social Network and Computing

Charles Fadel

43

Page 44: Nikravesh australia long_versionkeynote2012

TmT m

Tm-shaped Individual and not just T or m-shaped

Single Expertise Multiple Deep Expertise

Single Deep + Multiple Expertise Hybrid (CSE)

Broad Knowledge

21st century skills: problem-solving, critical thinking,

entrepreneurship and creativity

44

Page 45: Nikravesh australia long_versionkeynote2012

Computational Science and Engineering (CSE) @ Berkeley

45

Page 46: Nikravesh australia long_versionkeynote2012

What is CSE?

CSE is a rapidly growing multidisciplinary field that encompasses real-world complex applications (scientific, engineering, social, economic, policy), computational mathematics, and computer science and engineering. High performance computing (HPC), large-scale simulations and modeling (physical, biological, economic, social, and policy processes), and scientific applications all play a central role in CSE.

Petaflop with ~1M Cores in your PC by 2025?

46

Page 47: Nikravesh australia long_versionkeynote2012

What is CSE?

Simulation of complex problems is sometimes the only feasible way to make progress if the theory is intractable and experiments are too difficult, too expensive, too dangerous, or too slow.

Through modeling and simulation of multiscale systems of systems, and through scientific discovery from large-scale heterogeneous data, CSE aims to advance solutions for a wide range of problems in the areas of nanoscience and nanotechnology, energy, climate change, engineering design, neuroscience, cognitive computing and intelligent systems, plasma physics, transportation, bioinformatics and computational biology, earthquake engineering, geophysical modeling, astrophysics, materials science, national defense, information technology for health care, engineering better search engines, socio-economic-policy modeling, and other fields that are critical to scientific, economic, and social progress.

47

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CSE: Vision

To support the work of scientists and engineers as they pursue complex –simulation/modeling, as well as computational, data and visualization- intensive research to enhance scientific, technological, and economic leadership while improving our quality of life.

inspired by Science Bounded by our imagination innovation through Technology Create Social impact

Today, HPC-Petascale and soon Exascale systems- is not just a tool of

choice, but it becomes an indispensable tool for frontiers of science and

technology for social impact.

48

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CSE: Mission

Conduct world-leading research in applied mathematics and computer science to provide leadership in such areas as energy, environment, health-information technology, climate, bioscience and neuroscience, and intelligent cyber-physical infrastructure to name a few.

Be at the forefront of the development and use of ultra-efficient largest-scale computer systems, focusing on discoveries and solutions that link to the evolution of the commercial market for high-performance and cloud computing and services.

Allow industry collaborators to gain experience with computational modeling / simulation and the effective use of HPC and Cloud facilities and carrying back new expertise to their institutions. This would enable the Industry partners to be “first to market” with important scientific and technological capabilities, breakthrough ideas, and new hardware-software.

Educate the next generation of interdisciplinary students and industry leaders (DE-CSE program and a new Professional Master Program (PMS) to be developed)

inspired by Science Bounded by our imagination innovation through Technology Create Social impact

Petaflop with ~1M Cores in your PC by 2025?

49

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High performance computing

(HPC), large-scale simulations,

and scientific applications all

play a central role in CSE.

Applications

HPC-Cloud

Computing

Analytics

MathCSE

The HPC/cloud computing initiative

and next generation data center

Extreme simulation, visual-data analytics,

data-enabled scientific discovery

Applications/real‐world complex applications (scientific, engineering, social, economic,

policy) using the future multi-core parallel computing ((i.e. E-Informatics, Earthquake Early

Warning, NextGenMaps, Genome Atlas, Genetic Facebook, Genomics Browser)

CSEBerkeley and LBNL Partnership

HPC-Petascale and Exascale

systems are an indispensable

tool for exploring the frontiers of

science and technology for

social impact.

50

Page 51: Nikravesh australia long_versionkeynote2012

Computational Research Division

Applied Mathematics Computer Science

Computational Science

HPC architecture,

OS, and compilers

512256128

643216

842

1024

1/16 1 2 4 8 16321/81/4

1/21/32

RTM/wave eqn.

NVIDIA C2050 (Fermi)

SpMV

7pt Stencil

27pt Stencil

DGEMM

GTC/chargei

GTC/pushi

Performance

& AutotuningVisualization

and Data

Management

Cloud, grid &

distributed

computing

Mathematical

Models Adaptive Mesh

Refinement

Linear

Algebra

Libraries and

Frameworks

Interface

Methods

NanoscienceCombustion Climate Cosmology &

AstrophysicsGenomicsEnergy &

Environment

Source- LBNL & CSE51

Page 52: Nikravesh australia long_versionkeynote2012

Computational Science and Engineering (CSE) @ Berkeley

Designated Emphasis (DE) in CSE Participants

~120 Faculty (CSE), ~120 Researchers (HPC-Cloud), ~22 Departments,

, ~33 Students and growing, ~60 Courses, more being developed

http://cse.berkeley.edu/ http://cloud.citris-uc.org/

http://citris-uc.org/ http://www.lbl.gov/cs

52

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Designated Emphasis (DE) in CSE

• New “graduate minor” – approved, starting July 1, 2008

• Motivation

– Widespread need to train PhD students in large scale simulation, or analysis of large data sets

– Opportunities for collaboration, across campus and at LBNL

• Graduate students participate by

– Getting accepted into existing department/program

– Taking CSE course requirements

– Qualifying examination with CSE component

– Need to sign up before quals!

– Thesis with CSE component

– Receive “PhD in X with a DE in CSE”

53

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CSE Participating Departments (1/2) ( # faculty by “primary affiliation”, # courses, # Students )

•Astronomy (7,3,1)

•Bioengineering (3,1,0)

•Biostatistics (2,0,1)

•Chemical Engineering (6,0,0)

•Chemistry (8,1,0)

•Civil and Environmental Engineering (7,8,2)

•Earth and Planetary Science (6,3,4)

•EECS (19,14,4)

•IEOR (5,5,0)

•School of Information (1,0,0)

54

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CSE Participating Departments (2/2) ( # faculty by “primary affiliation”, # courses, # Students )

• Integrative Biology (1,0,0)

•Materials Science and Engineering (2,1,0)

•Mathematics (15,4,0)

•Mechanical Engineering (9,6,8)

•Neuroscience (7,1,4)

•Nuclear Engineering (2,1,3)

•Physics (1,1,0)

•Political Science (2,0,1)

•Statistics (5, 11,0)

•New: Biostatistics (1), Public Health (0), Vision

Science(1), Biopyhsics(1), Business School (1)

55

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Course Structure

3 kinds of students, course requirements

Applications, CS, Math

Each kind of student has 3 course requirements in other two fields

Goal: enforce cross-disciplinary training

Ex: Applications students takes courses from EECS, Math, Statistics, IEOR

We support new course development

5 courses recently created/updated

56

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Educating the Workforce of the FutureChina & India:

300M Skilled worker by 2025

Eng. Ph.D Median Salary:

India: $39,200

China: $53,700

Germany: $99,400

US(CA): $125,200

Science and Engineering Graduate

US 420000, EU 470000,

China 530000 , India 690000,

Japan 350000

McKinsey report concluded that only

10% of Chinese engineers and 25%

of Indian engineers can compete in

the global outsourcing arena.

Revised by: Nikarvesh57

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Annualized Job Openings vs. Annual Degrees Granted (2008-2018)

CSE educates the next generation of

interdisciplinary students and industry

leaders.

CSE Revised by: Nikarvesh58

Page 59: Nikravesh australia long_versionkeynote2012

Degree Production vs. Job Openings

Sources: Adapted from a presentation by John Sargent, Senior Policy Analyst, Department of Commerce,at the CRA Computing Research Summit, February 23, 2004. Original sources listed asNational Science Foundation/Division of Science Resources Statistics; degree data fromDepartment of Education/National Center for Education Statistics: Integrated PostsecondaryEducation Data System Completions Survey; and NSF/SRS; Survey of Earned Doctorates; andProjected Annual Average Job Openings derived from Department of Commerce (Office ofTechnology Policy) analysis of Bureau of Labor Statistics 2002-2012 projections. Seehttp://www.cra.org/govaffairs/content.php?cid=22.

160,000

140,000

120,000

100,000

80,000

60,000

40,000

20,000

Engineering Physical Sciences Biological Sciences Computer Science

Ph.D.

Master’s

Bachelor’s

Projected job openings

CSE educates the next generation of

interdisciplinary students and industry

leaders.

CSE Revised by: Nikarvesh59

Page 60: Nikravesh australia long_versionkeynote2012

CSECenter Concepts

CDISC

ACCESSInsight

60

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Open Big Data ScienceComputational Foundations and Driving Applications

CDISC – Center Concept

Open Big Data Science

APPS

CORE

LIBRARIES

ANALYTICS

MACHINE LEARNING

TRANINING &

EDUCATION

OUTREACH

Devices and Computing Environment

61

Page 62: Nikravesh australia long_versionkeynote2012

Our Center will develop a wide array of computational tools to tackle the

challenges of data-intensive scientific research across multiple scientific

disciplines.

These tools will encapsulate state of the art machine learning and statistical

modeling algorithms into broadly applicable, high-level interfaces that can

be easily used by application scientists.

Our goal is to dramatically reduce the time needed to extract knowledge

from the floods of data science is facing, thanks to workflows that permit

exploratory and collaborative research to evolve into robustly reproducible

outcomes.

CDISC: Center ConceptCenter for Data-Driven Scientific Computing

62

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Our development will be driven by a collection of scientific problems that

share a common theme.

They all present major data-intensive challenges requiring significant

algorithmic breakthroughs and represent key questions within their field,

from rapid astronomical discovery of rare events to early warning

systems for natural hazards such as earthquakes or tsunamis.

Moving beyond the traditional domain of scientific computing, we will

tackle a collection of problems in social sciences and the digital

humanities, pushing the boundaries of quantitative scholarship in these

disciplines.

CDISC: Center ConceptCenter for Data-Driven Scientific Computing

63

Page 64: Nikravesh australia long_versionkeynote2012

CDISC: Center for Data-Driven Scientific Computing

Center Concept

Date-Driven Scientific Computing

APPS

CORE

LIBRARIES

ANALYTICS

MACHINE LEARNING

TRANINING &

EDUCATION

OUTREACH

Devices and Computing Environment

64

Page 65: Nikravesh australia long_versionkeynote2012

Center for Accelerating Environmental Synthesis and Solutions (ACCESS)

& Environment Quality and Security

To enable synthesis, En Informatics(En= Environmental, Ecological, Epidemiological, Economic,

Engineering, Equitable, Ethical,… )

Center Concept

Health, Freshwater, Food Security, Ecosystems, and Urban Metabolism

World Population: Today-~6B, 2050-~9B, 2100-~10B%70 will live in Cities by 2050

65

Page 66: Nikravesh australia long_versionkeynote2012

ACCESS Focus

ACCESS will focus on five major domains critical for human welfare and environmental quality: freshwater, health, ecosystems, urban metabolism, and food security; and will create and implement a synthesis process that makes research tools and understanding rapidly accessible across disciplines, and foster new ways of thinking across disciplines about critical environmental problems.

Source: Inez Fung

Center for Accelerating Environmental Synthesis and Solutions (ACCESS)

66

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Berkeley ACCESS Themes

Ecosystem trajectories over the past million years and in the future -rate and nature - result principally 8000 generations of human population growth and aspirations.

Underlying ecosystem trajectories are the changing supply and demand of water and the need to harness energy to advance civilization.

Urban metabolism: Theoretical models of cities as complex socio-ecological systems with particular metabolic dynamics. Urban policy is increasingly critical to building a more sustainable future.

The increasing ease of utilizing existing resources leads to their rapid and unsustainable depletion, with many resulting intolerable impacts, including those on

Human and animal health

Food security

Source: Inez Fung

Center for Accelerating Environmental Synthesis and Solutions (ACCESS)

67

Page 68: Nikravesh australia long_versionkeynote2012

Urban Metabolism

Conceptual Frameworks for Urban Metabolism: Theoretical models of cities as complex socio-ecological systems with particular metabolic dynamics include approaches based in political economy, sociology, urban ecology and biogeochemistry, and industrial ecology – many of which remain disconnected from each other. In addition, because the inputs to urban life are globalized, the geography of consumption and production networks must be integrated into conceptual frameworks.

Data Integration: A rapidly expanding volume of geospatial data on urban stocks and flows – about people, animals, vegetation, consumer products, energy, waste, etc. – is available for synthesis and building models of the complex metabolic cycles of cities.

Policy and Activism: Urban policy is increasingly critical to building a more sustainable future, but the policy interventions and activist campaigns are piecemeal remedies rather than solutions based on an understanding of cities as complex socio-ecological systems.

Visualization and Decision-Support: Decision makers and stakeholders of many types need to visuzlize model results quickly and effectively. Generating sophisticated and insightful visualizations of urban systems is an emergent and critical field.

Source: Inez Fung68

Page 69: Nikravesh australia long_versionkeynote2012

Insight Lab

Applications

Machine Learning

Massive Scale Data

Analytics and Visualization

69

Page 70: Nikravesh australia long_versionkeynote2012

Strategic Projects/

Shared Facilities,

Resources, Expertise

TechnologyStreaming Data and

Visual Analytics

Core Group*

Core Scientific

Group*

Shared Facilities

VisLab+ Computing

Infrastructures

Delivery of Service

Mobile Devices,

Internet, and Cloud

Scie

nce

/Ap

plic

atio

ns

scie

ntific

, en

gin

ee

ring, s

ocia

l, eco

no

mic

/bu

sin

ess/fin

an

ce

ACCESS- E-informatics

Earthquake Early

Warning

Next Generation

Dynamic Maps

Genome Atlas, Genetic

Facebook, Genomics

Browser, bioinformatics,

Immune System, …

Computational

Bioscience,

Neuroscience,

Nanoscience ,

Astrophysics , …

*core group of enabling computational scientists would stand at the heart of the center, and that they would both cross-

pollinate expertise among projects and provide great leverage in winning large federally-supported projects*.

Educational, Research, and Social Impacts; IT-Enabled Disaster Resilience

Insight LabIntensive Computing, Immersive Visualization and Human Interaction

Data and Visual-enabled Scientific Discovery and Insight Accelerator

(~120 CSE Faculty, ~120 HPC-Cloud Researchers, and 22 Departments)

70

Page 71: Nikravesh australia long_versionkeynote2012

Earthquake early warning

400 seismic stations

across California

Use existing seismic stations to

• detect the beginning of earthquakes

• estimate the location and magnitude

• predict damaging ground shaking

• issue a warning to those in harms way

Seconds to tens

of seconds warning,

up to 1 minute

• people move to safe zone (under table)

• slow and stop trains (BART)

• isolate hazards (equipment, chemicals)

new science + modern communications

Allen Richard

71

Page 72: Nikravesh australia long_versionkeynote2012

Opinion Space: Crowdsourcing Insights

Scalability: N Participants, N Viewpoints

Each Viewpoint is n-Dimensional

Dim. Reduction: 2D Map of Affinity/Similarity

Insight vs. Agreement: Nonlinear Scoring

N2 Peer to Peer Reviews

Source: Ken Goldberg and Alec Ross

72

Page 73: Nikravesh australia long_versionkeynote2012

CISN

ShakeMap

Crowdsourcing + physical modeling + sensing + data assimilation

Physical modeling-based live maps, which contain real-time assessments of

situation integrating streaming dataSource: Alex Bayen

NextGenMap: The Value of Multi-disciplinary Research:

Invention, Societal-pull, Products, New Legislation

73

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Real-time (machine-learned) classification of astronomical event data

data deluge requires abstracting traditional roles of scientist in discovery

working with real data now, towards a scalable framework for the Large

Synoptic Survey (LSST) era

new statistical analytics

on sparse datamachine learning with noisy

& spurious feature sets

cloud-based ML with

massive databases

Source: Josh Bloom

Berkeley Time-Series Center

74

Page 75: Nikravesh australia long_versionkeynote2012

Innovative visualizations for a topic’s

summary in news across time

Real-time summaries of topics across many news sources

Global image of news landscape

Interpretable results obtained via sparse machine learning techniques

Massive data sets requires cloud computing

Real-time image of news sources or topics

Source: Laurent El Ghaoui

StatNews: Analytics and Visualization of News Data

75

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Lawrence Berkeley National Laboratory

76

Page 77: Nikravesh australia long_versionkeynote2012

Berkeley Lab’s Major Scientific FacilitiesComplex Tools to Address Scientific Challenges

Advanced Light Source

Molecular

FoundryNational Center for Electron

Microscopy

National Energy

Research Scientific

Computing Center

88-Inch

Cyclotron

Joint Genome

Institute

Energy Sciences

Network (ESnet)

77

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Computational Research Division

Applied Mathematics Computer Science

Computational Science

HPC architecture,

OS, and compilers

512256128

643216

842

1024

1/16 1 2 4 8 16321/81/4

1/21/32

RTM/wave eqn.

NVIDIA C2050 (Fermi)

SpMV

7pt Stencil

27pt Stencil

DGEMM

GTC/chargei

GTC/pushi

Performance

& AutotuningVisualization

and Data

Management

Cloud, grid &

distributed

computing

Mathematical

Models Adaptive Mesh

Refinement

Linear

Algebra

Libraries and

Frameworks

Interface

Methods

NanoscienceCombustion Climate Cosmology &

AstrophysicsGenomicsEnergy &

Environment

78

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National Energy Research Scientific Computing Facility

Department of Energy Office of Science

(unclassified) Facility

• 4000 users, 500 projects

• From 48 states; 65% from universities

• 1400 refereed publications per year

Systems designed for science

• 1.3 PF Hopper system (Cray XE6)

- 4th Fastest computer in US, 8th in world

• .5 PF in Franklin (Cray XT4), Carver (IBM

iDataplex) and other clusters

79

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NERSC Systems

Large-Scale Computing Systems

Franklin (NERSC-5): Cray XT4

• 9,532 compute nodes; 38,128 cores

• ~25 Tflop/s on applications; 356 Tflop/s peak

Hopper (NERSC-6): Cray XE6

• 6,384 compute nodes, 153,216 cores

• 120 Tflop/s on applications; 1.3 Pflop/s peak

HPSS Archival Storage

• 40 PB capacity

• 4 Tape libraries

• 150 TB disk cache

NERSC Global

Filesystem (NGF)

Uses IBM’s GPFS

• 1.5 PB capacity

• 5.5 GB/s of bandwidth

Clusters

140 Tflops total

Carver

• IBM iDataplex cluster

PDSF (HEP/NP)

• ~1K core cluster

Magellan Cloud testbed

• IBM iDataplex cluster

GenePool (JGI)

• ~5K core cluster

Analytics

Euclid

(512 GB shared memory)

Dirac GPU testbed (48 nodes)

80

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The TOP10 of the TOP500

Rank Site Manufacturer Computer Country CoresRmax

[Pflops] [MW]

1RIKEN Advanced Institute

for Computational Science Fujitsu

K Computer

SPARC64 VIIIfx 2.0GHz,

Tofu Interconnect

Japan 548,352 8.162 9.90

2National SuperComputer

Center in TianjinNUDT

Tianhe-1A

NUDT TH MPP,

Xeon 6C, NVidia, FT-1000 8C

China 186,368 2.566 4.04

3Oak Ridge National

LaboratoryCray

Jaguar

Cray XT5, HC 2.6 GHzUSA 224,162 1.759 6.95

4National Supercomputing

Centre in ShenzhenDawning

Nebulae

TC3600 Blade, Intel X5650, NVidia

Tesla C2050 GPU

China 120,640 1.271 2.58

5GSIC, Tokyo Institute of

TechnologyNEC/HP

TSUBAME-2

HP ProLiant, Xeon 6C, NVidia,

Linux/Windows

Japan 73,278 1.192 1.40

6 DOE/NNSA/LANL/SNL CrayCielo

Cray XE6, 8C 2.4 GHzUSA 142,272 1.110 3.98

7NASA/Ames Research

Center/NASSGI

Pleiades

SGI Altix ICE 8200EX/8400EXUSA 111,104 1.088 4.10

8DOE/SC/

LBNL/NERSCCray

Hopper

Cray XE6, 6C 2.1 GHzUSA 153,408 1.054 2.91

9Commissariat a l'Energie

Atomique (CEA)Bull

Tera 100

Bull bullx super-node S6010/S6030France 138.368 1.050 4.59

10 DOE/NNSA/LANL IBMRoadrunner

BladeCenter QS22/LS21USA 122,400 1.042 2.3481

Page 82: Nikravesh australia long_versionkeynote2012

Exascale: Who Needs It?

Fusion: Simulations

of plasma properties

to ITER scale model

Combustion:

complete predictive

engine simulation

Astronomy: origins

of the universe

Sequestration:

Understanding fluid

flow & chemistry

Materials: solar panels

to database of

materials-by-design.

Climate: Resolve

clouds (1km scale) &

model mitigations

Protein structures:

From Biofuels to

Alzheimers

Every field needs more computing!

1) To quantify and reduce uncertainty in simulations

2) Analyze data from experiments and simulations

82

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ESnet provides the critical network infrastructure that supports the Department of Energy’s Office of Science missions.

• ESnet directly supports the research of some 15,000 scientists, postdocsand graduate students at DOE laboratories, universities, other federal agencies, and industry worldwide

• Science is increasingly collaborative and globally distributed

• ESnet provides the reliable connection, science-driven innovation and user focus that enables scientists to collaborate, manage, and exchange data

The Energy Sciences Network

83

Page 84: Nikravesh australia long_versionkeynote2012

Prototype 100G Topology

Magellan

Magellan

Supporting Advanced Scientific Computing Research • Basic Energy Sciences •

Biological and Environmental Research • Fusion Energy Sciences • High Energy

Physics • Nuclear Physics

84

Page 85: Nikravesh australia long_versionkeynote2012

Outline of Talk

85

Drivers for Change: Computing and Big Data

Computational Science and Engineering

State Leadership

California – “The Golden State”

The State New Economy Model

“Sustainable California” –a return to “The Golden State”

Page 86: Nikravesh australia long_versionkeynote2012

“Sustainable California” –a Return to the Golden State

Health, Freshwater, Food Security, Ecosystems, and Urban Metabolism

86

“California”-The Golden State

“Silicon Valley” –The Golden High Tech Region

Page 87: Nikravesh australia long_versionkeynote2012

Top 10 Countries by GDP 2009 & 2010

Overall

Rank

Country or

U.S. State

GDP

(millions of USD)

— World 62,220,000

1 United States 14,620,000

2 People's Republic of China 5,879,100

[2]

3 Japan 5,391,000

4 Germany 3,306,000

5 France 2,555,000

6 United Kingdom 2,259,000

7 Italy 2,037,000

8 Brazil 2,024,000

California

1,911,822

9 Canada 1,564,000

10 Russia 1,477,000

Overall

Rank

Country or

U.S. State

GDP

(millions of USD)

— World 58,133,309

1 United States 14,119,000

2 Japan 5,068,996

3 People's Republic of China 4,985,461[2][3]

4 Germany 3,330,032

5 France 2,649,390[4]

6 United Kingdom 2,174,530

7 Italy 2,112,780

California 1,911,822

8 Brazil 1,573,409

9 Spain 1,460,250

10 Canada 1,336,068

2010 2009

Source: Wikipedia 87

Page 88: Nikravesh australia long_versionkeynote2012

State

Rank Company

Fortune 500

rank City

Revenues

($ millions)

1 Chevron 3 San Ramon 196,337.0

2 Hewlett-Packard 11 Palo Alto 126,033.0

3 McKesson 15 San Francisco 108,702.0

4 Wells Fargo 23 San Francisco 93,249.0

5 Apple 35 Cupertino 65,225.0

6 Intel 56 Santa Clara 43,623.0

7 Safeway 60 Pleasanton 41,050.0

8 Cisco Systems 62 San Jose 40,040.0

9 Walt Disney 65 Burbank 38,063.0

10 Northrop Grumman 72 Los Angeles 34,757.0

Top publicly traded companies in California for 2011 (over 50) according to revenues with State and U.S. rankingsA Total of $1,218,340.30 ($Millions)

Source: Fortune 500 88

Page 89: Nikravesh australia long_versionkeynote2012

“California” – The Golden StateCalifornia's economy is the ninth (eighth in 2010) largest economy in the world,

if the states of the U.S. were compared with other countries.

• California is house to top publicly traded companies in California (over 50

Fortune 500 in 2011 ) according to revenues with State and U.S. rankings

A Total of $1,218,340.30 ($Millions) in Revenue

• California is not only the house to the largest High Technology companies

but also house to the largest company in the world. Apple with Market Cap

of over $420B ranked 1st with Exxon ranked 2nd .

• California is the house to the leaders of the Internet and ICT and super-

computers

• California is the house to the largest and leading Bioscience, Life Sciences

and Biomedicine

• California is the house to the leading Nano and Sensor Technology

• California is the house to the many leading Universities and DoE Leading

National Lab in Science and Technology

The University of California is well known for developing and operating

academic research centers in cooperation with partners world-wide. UC

Berkeley has a proud reputation of solving problems of interest not only in the

state of California, but for the people of world.

89

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California’s commitment to the Leadership in Science and Technology (UCOP)

In the last part of the 20th century, California created the high-tech and biotechnology innovations that formed the backbone of today's "New Economy." As we begin the 21st century, the state of California, the University of California and hundreds of the state's leading-edge businesses have joined together in an unprecedented partnership to lay the foundation for the "next New Economy.“

The Governor Gray Davis Institutes for Science and Innovation –now named for the former governor in recognition of his instrumental role in their creation – include:

Source- UCOP90

Page 91: Nikravesh australia long_versionkeynote2012

California’s commitment to the Leadership in Science and Technology (UCOP)

Taken together, these four institutes represent a billion-dollar, multidisciplinary effort that focuses public/private resources and expertise simultaneously on research areas critical to sustaining California's economic growth and its competitiveness in the global marketplace.

The new ideas and technologies developed by researchers at the institutes help expand our economy into new industries and markets - and bring the benefits of innovation more quickly into the lives of people everywhere. These institutes open the doors to new understanding, new applications and new products through essential research in biomedicine, bioengineering, nanosystems, telecommunications and information technology.

Source- UCOP91

Page 92: Nikravesh australia long_versionkeynote2012

Silicon Valley and Stanford University

“Stanford University, its affiliates, and graduates played a major

role in the development of California's electronics and high-tech

industry.[16] From the 1890s, Stanford University's leaders saw its

mission as service to the West and shaped the school accordingly.

Regionalism helped align Stanford's interests with those of the

area's high-tech firms for the first fifty years of Silicon Valley's

development.[17] “

“During the 1940s and 1950s, Frederick Terman, as Stanford's

dean of engineering and provost, encouraged faculty and

graduates to start their own companies. He is credited with

nurturing Hewlett-Packard, Varian Associates, and other high-tech

firms, until what would become Silicon Valley grew up around the

Stanford campus.”

Source: Wikipedia 92

Page 94: Nikravesh australia long_versionkeynote2012

The World University Rankings 2011-2012

94

World

Rank

Institution Country /

Region

Overall

score

Teaching International

mix

Industry

income

Research Citations

1 California Institute of

Technology

United States 94.8 95.7 56 97 98.2 99.9

2 Harvard University United States 93.9 95.8 67.5 35.9 97.4 99.8

2 Stanford University United States 93.9 94.8 57.2 63.8 98.9 99.8

4 University of Oxford United

Kingdom

93.6 89.5 91.9 62.1 96.6 97.9

5 Princeton University United States 92.9 91.5 49.6 81 99.1 100

6 University of Cambridge United

Kingdom

92.4 90.5 85.3 55.5 94.2 97.3

7 Massachusetts Institute of

Technology

United States 92.3 92.7 79.2 94.4 87.4 100

8 Imperial College London United

Kingdom

90.7 88.8 92.2 93.1 88.7 93.9

9 University of Chicago United States 90.2 89.4 58.8 Data

withheld

by THE

90.8 99.4

10 University of California

Berkeley

United States 89.8

Page 95: Nikravesh australia long_versionkeynote2012

List of U.S. States by Unemployment Rate

State or DistrictUnemployment rate

(seasonally adjusted)

Monthly percent change

(=drop in unemployment)

Nevada 12.6 0.4%

California 11.1 0.2%

Rhode Island 10.8 0.3%

Mississippi 10.4 0.1%

District of Columbia 10.4 0.2%

North Carolina 9.9 0.1%

Florida 9.9 0.1%

Illinois 9.8 0.2%

Georgia 9.7 0.1%

South Carolina 9.5 0.4%

Michigan 9.3 0.5%

Kentucky 9.1 0.3%

Indiana 9.0 0.0%

New Jersey 9.0 0.1%

Oregon 8.9 0.2%

Arizona 8.7 0.0%

Tennessee 8.7 0.4%

Washington 8.5 0.2%

Idaho 8.4 0.1%

United States (mean)[5] 8.3 0.2%

Connecticut 8.2 0.2%

Alabama 8.1 0.6%

Ohio 8.1 0.4%

New York 8.0 0.0%

Missouri 8.0 0.2%

Colorado 7.9 0.1%

West Virginia 7.9 0.0%

State or DistrictUnemployment rate

(seasonally adjusted)

Monthly percent change

(=drop in unemployment)

United States (mean)[5] 8.3 0.2%

Texas 7.8 0.3%

Arkansas 7.7 0.2%

Pennsylvania 7.6 0.3%

Delaware 7.4 0.2%

Alaska 7.3 0.0%

Wisconsin 7.1 0.2%

Maine 7.0 0.0%

Massachusetts 6.8 0.2%

Louisiana 6.8 0.1%

Montana 6.8 0.3%

Maryland 6.7 0.2%

New Mexico 6.6 0.1%

Hawaii 6.6 0.1%

Kansas 6.3 0.2%

Virginia 6.2 0.0%

Oklahoma 6.1 0.0%

Utah 6.0 0.4%

Wyoming 5.8 0.0%

Minnesota 5.7 0.2%

Iowa 5.6 0.1%

Vermont 5.1 0.2%

New Hampshire 5.1 0.1%

South Dakota 4.2 0.1%

Nebraska 4.1 0.0%

North Dakota 3.3 0.1%

January 24, 2012 for December 2011

Source: Wikipedia 95

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Outline of Talk

96

Drivers for Change: Computing and Big Data

Computational Science and Engineering

State Leadership

California – “The Golden State”

The State New Economy Model

“Sustainable California” –a return to “The Golden State”

Page 97: Nikravesh australia long_versionkeynote2012

The State New Economy Index*

Methodology

The State New Economy Index uses 26 indicators. These

Indicators are divided into five categories. These categories

best capture what is new about the New Economy:

1) Knowledge Jobs (5)

2) Globalization (2)

3) Economic Dynamism (3.5)

4) Transformation to a Digital Economy (3)

5) Technological Innovation Capacity (5)

97*Source: ITIF-Kauffman

Page 98: Nikravesh australia long_versionkeynote2012

Top 10 US States ranked based on “The New Economy Index”

2010

1. Massachusetts (92.6)

2. Washington (77.5)

3. Maryland (76.9)

4. New Jersey (76.9)

5. Connecticut(76.6)

6. Delaware (75.0)

7. California (74.3)

8. Virginia (73.7)

9. Colorado (72.8)

10. New York (71.3)

2008

1. Massachusetts (97)

2. Washington (81.9)

3. Maryland (80)

4. Delaware (79.3)

5. New Jersey (77)

6. Connecticut (76.1)

7. Virginia (75.6)

8. California (75)

9. New York (74.4)

10. Colorado (70.4)

2007

1. Massachusetts (96.1)

2. New Jersey (86.4)

3. Maryland (85.0)

4. Washington (84.6)

5. California (82.9)

6. Connecticut (81.8)

7. Delaware (79.6)

8. Virginia (79.5)

9. Colorado (78.3)

10. New York (77.4)

2002

1. Massachusetts (90.0)

2. Washington (86.2)

3. California (85.5)

4. Colorado (84.3)

5. Maryland (75.6)

6. New Jersey (75.1)

7. Connecticut (74.2)

8. Virginia (72.1)

9. Delaware (70.5)

10. New York (69.3)

1999

1. Massachusetts (82.3)

2. California (74.3)

3. Colorado (72.3)

4. Washington (69.0)

5. Connecticut (64.9)

6. Utah (64.0)

7. New Hampshire (62.5)

8. New Jersey (60.9)

9. Delaware (59.9)

10. Arizona (59.2)98

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Page 99: Nikravesh australia long_versionkeynote2012

ITIF-Kauffman

Ranking

26 Attributes PCA

(MNIK2012)

5 Categories PCA

(MNIK2012)

Massachusetts Massachusetts Massachusetts

Washington Washington New Jersey

Maryland Connecticut Connecticut

New Jersey Maryland Washington

Connecticut New Jersey Maryland

Delaware Virginia Delaware

California California California

Virginia Colorado Virginia

Colorado Delaware New York

New York New Hampshire Colorado

New Hampshire Minnesota New Hampshire

Utah Utah Minnesota

Minnesota New York Utah

Oregon Oregon Oregon

Illinois Illinois Illinois

Rhode Island Michigan Rhode Island

Michigan Rhode Island Texas

Texas Pennsylvania Michigan

Georgia Texas Georgia

Arizona Vermont Florida

Florida Arizona Pennsylvania

Pennsylvania Georgia Arizona

Vermont North Carolina Vermont

North Carolina Ohio North Carolina

ITIF-Kauffman

Ranking

26 Attributes PCA

(MNIK2012)

5 Categories PCA

(MNIK2012)

Ohio Idaho Kansas

Kansas Kansas Ohio

Idaho Wisconsin Nevada

Maine Florida Maine

Wisconsin Missouri Idaho

Nevada Nebraska Wisconsin

Alaska New Mexico Alaska

New Mexico Maine Missouri

Missouri Iowa Nebraska

Nebraska Alaska Hawaii

Indiana North Dakota Indiana

Montana Hawaii Iowa

North Dakota Indiana North Dakota

Iowa South Carolina New Mexico

South Carolina Nevada Tennessee

Hawaii South Dakota South Carolina

Tennessee Tennessee Montana

Oklahoma Montana Louisiana

Kentucky Oklahoma Oklahoma

Louisiana Wyoming Kentucky

South Dakota Alabama South Dakota

Wyoming Kentucky Wyoming

Alabama Louisiana Alabama

Arkansas Arkansas Arkansas

West Virginia West Virginia West Virginia

Mississippi Mississippi Mississippi

US States ranked based on “The New Economy Index”and two new PCA ranking models!??

99

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Page 100: Nikravesh australia long_versionkeynote2012

KNOWLEDGE JOBS Weight

IT Professionals

Professional and Managerial Jobs

Workforce Education

Immigration of Knowledge Workers

U.S. Migration of Knowledge Workers

Manufacturing Value-Added

Traded-Services Employment

GLOBALIZATION

Export Focus on Manufacturing and Services

Foreign Direct Investment (FDI)

ECONOMIC DYNAMISM

Job Churning

Initial Public Offerings (IPOs)

Entrepreneurial Activity

Inventor Patents

Fastest-Growing Firms

The State New Economy Index*

DIGITAL ECONOMY

Online Population

Digital Government

Farms and Technology

Broadband

Health IT

INNOVATION CAPACITY

High-Tech Employment

Scientists and Engineers

Patents

Industry R&D

Non-industry R&D

Green Economy

Venture Capital

100Ref.*: ITIF and Kauffman Foundation

Page 101: Nikravesh australia long_versionkeynote2012

Knowledge Job (5)

1 Massachusetts (17.39)

2 Connecticut (16.78)

3 Maryland (15.40)

4 Virginia (15.37)

5 Delaware (13.94)

6 Minnesota (13.94)

7 New Jersey (13.85)

8 Washington (13.80)

9 New York (13.66)

10 New Hampshire (12.96)

13 California (10.70)

Top 10 US States ranked based on “The New Economy Index”

Globalization (2)

1 Delaware (18.05)

2 Texas (16.39)

3 South Carolina (15.31)

4 New Jersey (14.73)

5 Connecticut (14.68)

6 Massachusetts (14.59)

7 Kentucky (14.24)

8 New York (14.21)

9 Washington (13.73)

10 North Carolina (13.61)

17 California (13.17)

Economic Dynamism (3.5)

1 Utah (14.94)

2 Colorado (13.74)

3 Georgia (13.38)

4 Massachusetts (13.30)

5 Florida (13.09)

6 Montana (12.87)

7 Arizona (12.64)

8 Nevada (12.56)

9 California (12.01)

10 Idaho (11.86)

Digital Economy (3)

1 Massachusetts (16.40)

2 Rhode Island (15.53)

3 New Jersey (15.13)

4 Maryland (14.29)

5 Connecticut (14.09)

6 California (14.07)

7 New York (14.03)

8 Oregon (13.58)

9 Washington (13.41)

10 Virginia (12.82)

Innovation Capacity (5)

1 Massachusetts (19.0)

2 Washington (17.5)

3 California (15.0)

4 Maryland (13.4)

5 Delaware (13.1)

6 Colorado (13.0)

7 New Hampshire (12.2)

8 New Jersey (12.2)

9 Virginia (12.0)

10 New Mexico (11.8)

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Page 102: Nikravesh australia long_versionkeynote2012

Projection of the cases on the factor-plane ( 1 x 3)

Cases with sum of cosine square >= 0.00

Active

AL

AK

AZ

AR

CA

CO

CT

DE

FL

GA

HI

ID

IL

IN

IAKS

KY

LA

ME

MD

MA

MI

MN MS

MO

MT

NE

NV

NH

NJ

NM

NY

NC ND

OH

OKOR

PA

RI

SCSD

TN

TX

US

UT

VT

VA

WA WVWI

WY

-8 -6 -4 -2 0 2 4 6

Factor 1: 34.46%

-2

-1

0

1

2

3

4

Fa

cto

r 3

: 10

.00

%

AL

AK

AZ

AR

CA

CO

CT

DE

FL

GA

HI

ID

IL

IN

IAKS

KY

LA

ME

MD

MA

MI

MN MS

MO

MT

NE

NV

NH

NJ

NM

NY

NC ND

OH

OKOR

PA

RI

SCSD

TN

TX

US

UT

VT

VA

WA WVWI

WY

Top 25 States Bottom 25 States

PCA Analysis of US States Ranking: The New Economy Index (26 Indicators)

102

Page 103: Nikravesh australia long_versionkeynote2012

Outline of Talk

103

Drivers for Change: Computing and Big Data

Computational Science and Engineering

State Leadership

California – “The Golden State”

The State New Economy Model

“Sustainable California” –a return to “The Golden State”

Page 104: Nikravesh australia long_versionkeynote2012

CDISCACCESS

Insight

Incr

ease

d

clim

ate/

envi

ron

men

tal

det

ail

Increased socio-economic detail

Tera

Peta

Peta

Exa

Socio-Economic Modeling

for Large-scale Quantitative

Climate/Environmental

Change Analysis

En Informatics

Environment-Genetic

World Population: Today-~6B, 2050-~9B, 2100-~10B%70 will live in Cities by 2050

By 2020: 35 trillion Gigabytes Data (Cyber-Physical world is connected throughbillions to even trillions of sensors and devices)

Petaflop with ~1M Cores in your PC by 2025? Health, Freshwater, Food Security, Ecosystems, and Urban Metabolism

104

Page 105: Nikravesh australia long_versionkeynote2012

“Sustainable California” –a return to the Golden State

building upon massive scale datasets –

streaming and static (sensors/social-economic)

employing sophisticated analytics, with an

emphasis on modeling, simulation, and

crowdsourcing

focus on major domains critical for human

welfare and environmental quality (Environment

and Security); urban metabolism and smart

cities, food security, fresh water resources,

public health, natural disasters, energy

conservation, and ecosystem.

educating the next generation of

interdisciplinary students and industry leaders

A statewide initiative to create integrated

systems and advanced analytic tools

using advanced computational science

and engineering

105

Page 106: Nikravesh australia long_versionkeynote2012

California can improve the standard of living by applying predictive simulation systems and integrated advanced analytic tools using advanced computational science and engineering to critical problems facing the state

How can California respond to rapidly

changing environment, climate change,

socio-economic forces and

demographics?

water resources, public health, natural

disasters, energy conservation,

environment and security

Predictive simulation and advanced

analytic can be used to

understand the impacts of policy choices

understand social and economical impacts

create new technologies and industries

find more efficient solutions to California’s

pressing infrastructure problems 7

TURING’s TEST

Turing: A computer can be said to be intelligent if its

answers are indistinguishable from the answers of a

human being

??

Computer

Health, Freshwater, Food, Energy, Environment Security, Ecosystems, and Urban Metabolism

106