Nikravesh big datafeb2013bt

The Emergence of Computation for Interdisciplinary Large Data
Frontiers in HPC and Data Analytics
inspired by Science Bounded by our imagination innovation through Technology Create Social impact
Masoud Nikravesh @ LBNL and Maxeler
Mnikravesh@lbl.gov
Nikravesh@Maxeler.com
Visiting Scientist- Lawrence Berkeley National Lab
Vice President- Maxeler Technologies
Health, Freshwater, Food Security, Ecosystems, and Urban Metabolism
1

Outline of Talk
Drivers for Change: Computing and Big Data
Computational Science and Engineering
Big Data and State Economic Model
The State New Economy Model
State-wide Initiative
Maxeler Technologies
2

Outline of Talk
Big Data and Economic Model
3

Drivers for Change- Computation
• 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

Moore’s Law is Alive and Well
2X transistors/Chip Every 1.5 years
Called “Moore’s Law”
Moore’s Law
Microprocessors have become
smaller, denser, and more
powerful.
Gordon Moore (co-founder of
Intel) predicted in 1965 that the
transistor density of
semiconductor chips would
double roughly every 18
months.
Slide source: Jack Dongarra
5

But Clock Scaling Bonanza Has Ended
Processor designers forced to go “multicore”:
 Heat density: faster clock means hotter chips
 more cores with lower clock rates burn less power
 Declining benefits of “hidden” Instruction Level Parallelism
(ILP)
 Last generation of single core chips probably over-engineered
 Lots of logic/power to find ILP parallelism, but it wasn’t in the
apps
 Yield problems
 Parallelism can also be used for redundancy
 IBM Cell processor has 8 small cores; a blade system with all 8
sells for $20K, whereas a PS3 is about $600 and only uses 7
6

Clock Scaling Hits Power Density Wall
4004
8008
8080
8085
8086
286
386
486
Pentium®
P6
1
10
100
1000
10000
1970 1980 1990 2000 2010
Year
PowerDensity(W/cm2)
Hot Plate
Nuclear
Reactor
Rocket
Nozzle
Sun’s
Surface
Source: Patrick
Gelsinger, Intel
Scaling clock speed (business as usual) will not work
7

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)
8

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)
9

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,
10

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
11

Source: Oliver Pell, HPC-SEG July 2011, Berkeley
CPU, GPU, Hybrid, FPGA?
12

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, Berkeley
13

A Likely Trajectory - Collision or
Convergence?
CPU
GPU
multi-threading multi-core many-core
fixed function
partially programmable
fully programmable
future
processor
by 2012
?
programmability
parallelism
after Justin Rattner, Intel, ISC 2008
14

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,
15

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
16

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
17

TOP10 Sites – Nov 2012
Rank Site System Cores Rmax (TFlop/s) Rpeak (TFlop/s) Power (kW)
1
DOE/SC/Oak Ridge National
Laboratory
United States
Titan - Cray XK7 , Opteron 6274 16C 2.200GHz, Cray Gemini
interconnect, NVIDIA K20x
Cray Inc.
560640 17590.0 27112.5 8209
2
DOE/NNSA/LLNL
United States
Sequoia - BlueGene/Q, Power BQC 16C 1.60 GHz, Custom
IBM
1572864 16324.8 20132.7 7890
3
RIKEN Advanced Institute for
Computational Science (AICS)
Japan
K computer, SPARC64 VIIIfx 2.0GHz, Tofu interconnect
Fujitsu
705024 10510.0 11280.4 12660
4
DOE/SC/Argonne National
Laboratory
United States
Mira - BlueGene/Q, Power BQC 16C 1.60GHz, Custom
IBM
786432 8162.4 10066.3 3945
5
Forschungszentrum Juelich (FZJ)
Germany
JUQUEEN - BlueGene/Q, Power BQC 16C 1.600GHz, Custom
Interconnect
IBM
393216 4141.2 5033.2 1970
6
Leibniz Rechenzentrum
Germany
SuperMUC - iDataPlex DX360M4, Xeon E5-2680 8C 2.70GHz,
Infiniband FDR
IBM
147456 2897.0 3185.1 3423
7
Texas Advanced Computing
Center/Univ. of Texas
United States
Stampede - PowerEdge C8220, Xeon E5-2680 8C 2.700GHz,
Infiniband FDR, Intel Xeon Phi
Dell
204900 2660.3 3959.0
8
National Supercomputing Center in
Tianjin
China
Tianhe-1A - NUDT YH MPP, Xeon X5670 6C 2.93 GHz, NVIDIA
2050
NUDT
186368 2566.0 4701.0 4040
9
CINECA
Italy
Fermi - BlueGene/Q, Power BQC 16C 1.60GHz, Custom
IBM
163840 1725.5 2097.2 822
10
IBM Development Engineering
United States
DARPA Trial Subset - Power 775, POWER7 8C 3.836GHz,
Custom Interconnect
IBM
63360 1515.0 1944.4 3576
18

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
21

Drivers for Change – Big Data
• 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
Data
Model
Human
Experts- Citizen Cyber Science
Crowdsourceing
Analytic ToolsFirst Principles Hybrid Models
Google
IBM-Watson
IBM- Cognitive Model
Boeing 747 Simulation
Protein Folding
Amazon AI-ImageIncreased
climate/environmentaldetail
Increased socio-economic detail
Tera
Peta
Peta
Exa
Socio-Economic Modeling
for Large-scale Quantitative
Climate/Environmental
Change Analysis
En Informatics
Environment-Genetic
22

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)
23

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
24

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
25

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.
26

Crowdsourcing
Big
Data
Model
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
Increased
Tera
Peta
Peta
Exa
Change Analysis
En Informatics
Environment-Genetic
27

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 dimension
28

Cloud Computing
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.
29

What is a Cloud? Definition-NIST
30
According to the National Institute of Standards &
Technology (NIST)…
Resource pooling. Computing resources are pooled
to serve multiple consumers.
Broad network access. Capabilities are available over
the network.
Measured Service. Resource usage is monitored and
reported for transparency.
Rapid elasticity. Capabilities can be rapidly scaled
out and in (pay-as-you-go)
On-demand self-service. Consumers can provision
capabilities automatically.

What is a cloud? Cloud Models
31

Map Reduce
 Map:
 Accepts input key/value
pair
 Emits intermediate
key/value pair
 Reduce :
 Accepts intermediate
key/value* pair
 Emits output key/value
pair
Very
big
data
Result
M
A
P
R
E
D
U
C
E
Partitioning
Function

MapReduce: The Map Step
vk
k v
k v
map
vk
vk
…
k v
map
Input
key-value pairs
Intermediate
key-value pairs
…
k v

MapReduce: The Reduce Step
k v
…
k v
k v
k v
Intermediate
key-value pairs
group
reduce
reduce
k v
k v
k v
…
k v
…
k v
k v v
v v
Key-value groups
Output
key-value pairs

Distributed Execution
User
Program
Worker
Worker
Master
Worker
Worker
Worker
fork fork fork
assign
map
assign
reduce
read
local
write
remote
read,
sort
Output
File 0
Output
File 1
write
Split 0
Split 1
Split 2
Input Data

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 Computing
39

Cloud Computing
 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.
40

Cloud Computing Initiative
The focus will be 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.
41

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 Resources
California 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
Nano Lab
Clusters
42

Increased
Tera
Peta
Peta
Exa
Change Analysis
En Informatics
Environment-Genetic
BIG Data and Exa-Scale Computing
43

Courtesy of U.S. Department of Energy Human Genome Program , http://www.ornl.gov/hgmis
BIG Data and DNA Computing
44

BIG Data and Visualization –Scientific
47

BIG Data and Visualization - Business
48

Outline of Talk
49

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
50

51

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.
52

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.
53

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.
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.
54

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)
55

High performance computing
(HPC), large-scale simulations,
and scientific applications all
play a central role in CSE.
Applications
HPC-Cloud
Computing
Analytics
Math
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)
CSE
HPC-Petascale and Exascale
systems are an indispensable
tool for exploring the frontiers of
science and technology for
social impact.
56

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 Jobs
1-2 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
57

Tm
T 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
58

Educating the Workforce of the Future
China & 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: Nikarvesh
59

Annualized Job Openings vs. Annual Degrees Granted (2008-2018)
CSE educates the next generation of
interdisciplinary students and industry
leaders.
CSE Revised by: Nikarvesh
60

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 as
National Science Foundation/Division of Science Resources Statistics; degree data from
Department of Education/National Center for Education Statistics: Integrated Postsecondary
Education Data System Completions Survey; and NSF/SRS; Survey of Earned Doctorates; and
Projected Annual Average Job Openings derived from Department of Commerce (Office of
Technology Policy) analysis of Bureau of Labor Statistics 2002-2012 projections. See
http://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: Nikarvesh
61

Open Big Data Science
Computational Foundations and Driving Applications
Open
Big Data
Science
APPS
CORE
LIBRARIES
ANALYTICS
MACHINE
LEARNING
TRANINING &
EDUCATION
OUTREACH
Devices and Computing Environment
62

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.
The 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.
Data-Driven Scientific Computing
63

The 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.
Center for Data-Driven Scientific Computing
64

Accelerating Environmental Synthesis and Solutions (ACCESS)
& Environment Quality and Security
To enable synthesis, En Informatics
(En= Environmental, Ecological, Epidemiological, Economic,
Engineering, Equitable, Ethical,… )
World Population: Today-~6B, 2050-~9B, 2100-~10B
%70 will live in Cities by 2050
65

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.
Accelerating Environmental Synthesis and Solutions (ACCESS)
66

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
Center for Accelerating Environmental Synthesis and Solutions (ACCESS)
67

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.
68

Insight Lab
Applications
Machine
Learning
Massive
Scale Data
Analytics and
Visualization
Data Structure
Analytics
Service
Delivery
69

Strategic Projects/
Shared Facilities,
Resources, Expertise
Technology
Streaming Data and
Visual Analytics
Core Group*
Core Scientific
Group*
Shared Facilities
VisLab+ Computing
Infrastructures
Delivery of Service
Mobile Devices,
Internet, and Cloud
Science/Applications
scientific,engineering,social,economic/business/finance
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 Lab
Intensive Computing, Immersive Visualization and Human Interaction
Data and Visual-enabled Scientific Discovery and Insight Accelerator
70

Outline of Talk
71

List of U.S. States by Unemployment Rate
State or District
Unemployment 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 District
Unemployment 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 72

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)
*Source: ITIF-Kauffman 73

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)
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
3. Maryland (85.0)
7. Delaware (79.6)
8. Virginia (79.5)
9. Colorado (78.3)
10. New York (77.4)
2002
4. Colorado (84.3)
5. Maryland (75.6)
8. Virginia (72.1)
9. Delaware (70.5)
10. New York (69.3)
1999
3. Colorado (72.3)
6. Utah (64.0)
7. New Hampshire (62.5)
9. Delaware (59.9)
10. Arizona (59.2)
74

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!??
75

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
Ref.*: ITIF and Kauffman Foundation 76

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)
7 Kentucky (14.24)
8 New York (14.21)
10 North Carolina (13.61)
Economic Dynamism (3.5)
1 Utah (14.94)
2 Colorado (13.74)
3 Georgia (13.38)
5 Florida (13.09)
6 Montana (12.87)
7 Arizona (12.64)
8 Nevada (12.56)
10 Idaho (11.86)
Digital Economy (3)
2 Rhode Island (15.53)
4 Maryland (14.29)
7 New York (14.03)
8 Oregon (13.58)
10 Virginia (12.82)
Innovation Capacity (5)
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)
77

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
OK
OR
PA
RI
SC
SD
TN
TX
US
UT
VT
VA
WA WV
WI
WY
-8 -6 -4 -2 0 2 4 6
Factor 1: 34.46%
-2
-1
0
1
2
3
4
Factor3:10.00%
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
OK
OR
PA
RI
SC
SD
TN
TX
US
UT
VT
VA
WA WV
WI
WY
Top 25 States Bottom 25 States
PCA Analysis of US States Ranking:
The New Economy Index (26 Indicators)
78

Outline of Talk
79

State-Wide Initiative
 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
80

States 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 states
How can States 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
Health, Freshwater, Food, Energy, Environment Security, Ecosystems, and Urban Metabolism
81

Outline of Talk
82

Roots at Stanford University, Bell Labs,
and Imperial College London
Founded in 2003,
incorporated in Delaware and England
2006: signs long term R&D contract with
Chevron in San Ramon CA
2010: ENI (Italy) buys largest Maxeler
Supercomputer for Imaging
2011: sold 20% stake to JP Morgan’s
strategic investments group
2012: Partnership for Sequence Assembly and Analysis with EU Genomics Center
84

 Oskar Mencer, CEO, previously at Technion, Stanford,
DIGITAL, Hitachi and Bell Labs.
 Prof Michael J Flynn, Chairman, Professor Emeritus,
Stanford, previously VC partner, Founder of American
Supercomputers, and manager at IBM.
 Stephen Weston, Chief Development Officer, previously
Managing Director at JP Morgan, Deutsche Bank, UBS,
and CS.
 Over 50 employees, over 10 PhDs and scientists
 Main office is in London, UK.
 see www.maxeler.com
Maxeler Technologies – The Team
85

86
Maxeler Technologies - Divisions

The Challenges- HPC
We are approaching the end of easy scaling as predicted by
Moore’s Law, reaching limits in multiple dimensions: Power,
Space, Time, and Cost.
The Power Gap: As CPU transistors shrink, the net effect
is an increase in density but consequently power
consumption is on the rise too
The Space Gap: The space required to perform
computation continues to expand as our appetite for
solving complex problems marches on
The Time Gap: As we explore new science and exploit Big
Data, we also increase application complexity and as a
direct result runtime
The Cost Gap: Each server node added to the data center
increases operational costs in the form of utility rates
87

The Maxeler Solution: How We Deliver
Maximum Performance Computing
Maxeler has bridged these gaps to deliver maximum
performance by designing systems to meet the needs of
the application rather than forcing applications to conform
to a generic machine
This approach optimises for performance whilst minimising
on space, cost, and power
Power Gap 
Time Gap
Space Gap 
Cost Gap 
88

What does Maxeler do?
Maxeler’s Dataflow
Technology combines
computation, data and connectivity
to transform data intensive tasks
from long overnight computations in
a data center to real-time delivery of results
at the source of data.
The Maxeler Dataflow computing appliance model enables
the next generation of algorithms and applications.
89

Maximum Performance Computing ProcessStart
Original
Application
Identify code
for
acceleration
and analyze
bottlenecks
Write
MaxCompiler
code
Simulate
Functions
correctly?
Build for
Hardware
Integrate with
Host code
Meets
performance
goals?
Accelerated
Application
NO
YESYES
NO
Transform
app, architect
and model
performance
Accelerate
remaining
code
on CPU
90

Traditional (CPU) Computing
Maxeler computes 30-200x faster, with
10-50x smaller physical footprint and
10-50x power efficiency because 100%
of the chip is used for computation.
Multiscale Dataflow Computing
Pushing physical limits of computation.
Only a small proportion of chip is actually
used for computation, time is wasted
talking to levels of cache.
Solution Scalability – how it works…
Maxeler Dataflow Technology (DFT)
Maxeler computes in space not in time by maximising use of chip surface area. Our
specialist tools shorten development and maintenance cycles. Our Dataflow Engines
(DFEs) maximise data through-put - computation happens as a side effect.
CPUs compute
in time
DFEs compute in
space
91

for (int i =0; i < DATA_SIZE; i++)
y[i]= x[i] * x[i] + 30;
PCI
Express
Manager
Chip
Memory
Manager (.java)
x
x
+
30
x
Manager m = new
Manager(“Calc”);
Kernel k =
new MyKernel();
m.setKernel(k);
m.setIO(
link(“x", PCIE),
m.addMode(modeDefault());
m.build();
link(“y", PCIE));
#include “MaxSLiCInterface.h”
#include “Calc.max”
Calc(x, y, DATA_SIZE)
Main
Memory
CPU
CPU
Code
CPU Code (.c)
Maxeler Dataflow Compiler
SLiC
MaxelerOS
DFEvar x = io.input("x", hwInt(32));
DFEvar result = x * x + 30;
io.output("y", result, hwInt(32));
MyKernel (.java)
int *x, *y;
y
x
x
+
30
y
x
92

Maxeler Speed Advantage
for High Performance Computing (HPC)
Modelling 25x Finite Difference
60x
Data Correlation 22x
Smith-Waterman 16-
32x
Fluid Flow
30x
Imaging 29x
93

1200m
1200m
1200m
1200m
1200m
Generates >1GB every 10
The Oil Exploration Problem
Image Courtesy of 94

Risk Solution Architecture
Consistent, real-time, valuation and risk management
calculations across all major asset classes
Maxeler’s dataflow
accelerated finance library
provides ultra high speed
computation of PV and risk
Client provides trade, market
and static data in own
format
Finance appliance
covers 10 asset classes
Risk summarizations in
hardware avoid use of
complex databases95

 Trade capture
 Deal entry, storage and
retrieval
 Trade lifecycle
management
 Deal modification – new,
amends and deletes
 Pricing and risk
management
 Deal valuation
 Portfolio valuation and risk
 Enterprise level regulatory
risk
Trade Capture
Java GUI
Trade
Lifecycle
Risk
engine
Maxeler finance
library & OS
Dataflow
Engines
Risk and P&L
results database
Results of scenarios stored
into client database and
retrieved using client tools
Results transmitted to down-
stream reporting systems
Results
viewable in
any format –
e.g. Excel,
Python,
Matlab etc
Flexible Python
scripting
enables rapid
scenario
development
and deployment
Trading system infrastructure - overview
96

Maxeler Dataflow Engines (DFEs)
High Density DFEs
Intel Xeon CPU cores and up to 6
DFEs with 288GB of RAM
The Dataflow Appliance
Dense compute with 8 DFEs,
384GB of RAM and dynamic
allocation of DFEs to CPU servers
with zero-copy RDMA access
The Low Latency Appliance
Intel Xeon CPUs and 1-2 DFEs with
direct links to up to six 10Gbit
Ethernet connections
MaxWorkstation
Desktop
dataflow
development system
Dataflow Engines
48GB DDR3, high-speed
connectivity and dense
configurable logic
MaxRack
10, 20 or 40 node rack systems integrating
compute, networking & storage
MaxCloud
Hosted, on-demand, scalable accelerated
compute
97

Maxeler University Program Members
98

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