A School inComputational Science &

EngineeringEngineering

Richard FujimotoChair, Computational Science andChair, Computational Science and

Engineering Division

Planned ChangesGeorgia Tech

CSE Division• Focus on computer

b d d l fArchitecture

Georgia TechColleges

based models of natural & engineered systems9 f lt ith i

School of Computer ScienceComputing

• 9 faculty with primary appointment in CSE

• 4 with secondary i t t i CSEDivision of

School of Interactive Computing

Management

Ivan Allen

School appointment in CSE• 9 adjunct faculty• 15 faculty with joint

Division ofComputational Science

and EngineeringEngineering

Sciences

School

Operational Changes

appointmentsSciences

• None: CSE already operating like a school in terms of education programs, administration, processes (e.g., RPT), …

Rationale• Create a home for faculty and students focusing on core

topics such as modeling and simulation, computational data analytics, high performance computingdata analytics, high performance computing– CSE discipline recognized internationally (e.g., SIAM)– Need for CSE departments recognized by (for example) NSF-OCI

S G• Such a home gives Georgia Tech a competitive advantage (e.g., faculty, student recruiting) relative to universities such as Stanford, Cal Tech, UC-Berkeley, UT-Austin, Univ. , , y, ,of Illinois, Purdue, …

• Creates an administrative structure to manage resources ( l ) d f t i d ll b ti i ll(e.g., people) and foster increased collaboration especially with units in the Colleges of Engineering and Sciences

Georgia Tech is taking a leadership position by establishing Computational Science & Engineering as a school

Education Programs• Multidisciplinary MS and PhD degree programs

in Computational Science and Engineering (b F ll 2008)(began Fall 2008)– Jointly offered by three colleges: Computing,

Sciences, and Engineering– 20 PhD, 20 MS students enrolled (Nov 2009)– First distance learning degree offered by CoC

• Computer Science undergraduate programComputer Science undergraduate program (revised 2007)– Jointly offer with Schools of Computer Science,

Interacti e Comp ting and CSEInteractive Computing, and CSE– Thread in modeling and simulation

• CRUISE: Computing Research Undergraduate p g gIntern Summer experience (began 2008)– Women and minority outreach

High Performance Computing

David Bader

Source: Austin American Statesman

Jeff Vetter (‘98 GT)

(’96 Univ. Maryland)

Jeff Vetter ( 98 GT)joint with ORNL

George Biros (’00 CMU), joint Biomed. Eng.

Massive Data: NSF/DHS FODAVA Program(Foundations of Data and Visual Analytics)( y )

UIUCDuke

CornellCMU Virginia Tech FODAVA partner

universities

UC–Santa Cruz

FODAVA Lead:G i T h

UI-ChicagoGeorgetown

Maryland CruzGeorgia Tech

Stanford

UC-DavisMichigan

Michigan State

Northwestern

Michigan State

Penn State Princeton

PurdueHaesun Park(’87 Cornell)

• Extracting knowledge from massive data critically important

G i T h d l d i tit ti i FODAVA• Georgia Tech won award as lead institution in FODAVA program

• Interdisciplinary team spanning CoC, CoE, CoS

“High Performance” Junior Faculty

Alex GrayMachine Learning

Data Analytics

Guy LebanonMachine Learning

Data Analytics

Rich VuducHigh Performance

Computing(’04 UC B k l )(’03 CMU)

• NSF Career A d

(’05 CMU)

• NSF Career A d

(’04 UC-Berkeley)

• NSF Career AwardDARPA CS St dAward Award• DARPA CS Study Group

Current Research Funding(Value of Active Grants & Contracts including CSE faculty(Value of Active Grants & Contracts including CSE faculty

as a PI or Co-PI - Nov. 2009)

• Over $29 Million total in active grants (10 CSE faculty)

$ Millions

• Many multidisciplinary awards• Four grants of $1M or more

Concluding Remarks• CSE Division founded on the vision that

Computational Science & Engineering is a discipline that merits a home on university campusesTh CSE Di i i h d t d b• The CSE Division has grown and matured by– Creating new educational programs

Establishing strong multidisciplinary research– Establishing strong, multidisciplinary research programs in HPC, Massive Data, and Simulation

– Providing leadership in the broader communityg p y• The School of Computational Science and

Engineering is a natural next step that will continue Georgia Tech’s leadership role in defining and establishing the CSE discipline

“The best way to predict the future is to i t it ”invent it.”

- Alan Kay, 1971y,

Backup Slides

About the NamePro• “Computational Science &

Engineering” the recognized, well established name in the

it

Chttp://www.siam.org/students/resources/report.php

community• CSE faculty strongly favor this

name for the schoolCon• Potential confusion “Computer Science” vs. “Computer Engineering” vs.

“Computational Science & Engineering”

p g p p p

• Potential ABET issues• Use of “Engineering” outside College of Engineering

SolutionSolution• Retain name “Computational Science & Engineering” for school• Avoid use of “CSE” name in undergraduate programs

D t & bli i di ti ti / l ti hi C t S i• Document & publicize distinctions/relationships among Computer Science, Computer Engineering, Computational Science & Engineering

• Continued communication among CoC and CoE, CSE and ECE

CSE FacultyyAlberto Apostolico

Bioinformatics, Pattern Matching

David BaderHigh Performance

Computing

Guy LebanonMachine Learning

Data Analyticsg(‘76 Univ. Salerno)joint Inter. Comp.

Computing(’96 Univ. Maryland)

y(’05 CMU)

George Biros

Ken BrownQuantum Computing

(’04 UC-Berkeley)joint Chemistry

Jeff VetterHigh Performance

Computing(‘98 GT)

joint with ORNL Haesun ParkScientific Computing

Data Analytics(’87 Cornell)

George BirosHigh Performance Computing

(’00 CMU)joint Biomed. Eng.

Mark BorodovskyBioinformatics

(‘76, Moscow Inst. Phs&Tech)joint Biomed. Eng.

joint with ORNL

Richard FujimotoRich Vuduc jParallel/Distributed

Simulation(’83 UC-Berkeley)

Alex GrayMachine Learning

Data Analytics(’03 CMU)

Hongyuan ZhaScientific Computing

Data Analytics(’93 Stanford)

High Performance Computing

(’04 UC-Berkeley)

David SherrillHigh Performance Computing

(’96 UGa)joint Chemistry

CSE is a discipline devoted to the systematic study of computer-

Computational Science & Engineering (CSE)p y y p

based models of natural and engineered systems.Nanomaterials

TransportationAstrophysics

Aerospace

Weather and climate

CSE

Computation

Aerospace

Biology(drug design,

Mathematics

Biomedical

( g g ,cancer treatment,

phylogeny, …) Manufacturing

14

Biomedical

Interdisciplinary collaboration with science and engineering

CSE Strategy(our hedgehog concept*)(our hedgehog concept )

• Disciplinary StrengthE t bli h ll i f th CSE– Establish excellence in core areas of the CSE discipline: high performance computing, data and visual analytics, embedded modeling and simulation

• Interdisciplinary Collaboration– NOT by simply providing a service (e.g., programming)– NOT by simply applying existing techniques to

applications (even important ones)BY d i th t t f th t i t ti l– BY advancing the state-of-the-art in computational methods to enable solution of real-world problems

– BY defining and shaping new problems in the context of g p g pimportant application domains

*J. Collins, Good To Great, Harper Collins, 2001

High Performance ComputingThe multicore challenge/crisis

HPC algorithms, computational The multicore challenge/crisis• Single processor performance

improvements essentially stopped ~2005• From handhelds to supercomputers, parallel

Advances in Medical imaging

Understanding large-scale networks (transportation

methods, software to enable

p p , pcomputing has become necessary to exploit new hardware capabilities

imaging(transportation, energy, water, Internet, …)

Blue Gene/L Roadrunner

Fluid simulation(e.g., heart valve design)ASCI Red

ASCI White ASCI Q

Earth Simulator

ASCI Red

Storm

NASA Columbia

ASC Purple

Challenge: Scalable algorithms and software for million core machines.

CSE Faculty: Bader, Biros, Fujimoto, Vuduc

Massive-Scale Data Analytics• Widespread deployment of sensors, camera, wireless networks, Internet…A tsunami of data

Widespread deployment of sensors, camera, wireless networks, Internet…• 2002: 22 exabytes (1018) electronic information and recorded media• 2006: 161 EB digital information created, captured, replicated• 2010: About 988 EB (almost 1 ZB) new information

Li it i bilit t t t k l d d i i ht f th f d t• Limit is our ability to extract knowledge and insight from the many sources of data• Challenge: algorithms that scale to massive, complex data sets

CSE Faculty:Apostolico, Gray, Lebanon, Park, Zha,

Bioinformatics

Epidemiology

Astrophysics

Borodovsky

Social NetworksBiometric RecognitionText AnalysisHomeland Security

Modeling & Simulation Interacting with the Real World Emerging Technologies

- Sensors and computers have become ubiquitous – cell phones, ipods, video cameras, …- Wireless communication widely deployed (e.g., cellular, WiFi, sensor networks)

Embedded modeling and simulationAdvance from capturing the state of the world to predicting future states- Advance from capturing the state of the world to predicting future states

- Computation models interacting with the real-world- On-line management and optimization of systems

I t t dRoadside‐to‐vehicle

Ad Hoc Distributed Simulation

Instrumentedtraffic signalcontroller

communicationVehicle‐to‐vehiclecommunication

exampleSophisticated distributed computing systems on the road

In‐Vehicle Simulations p g y

for transportation system management• Distributed simulation• Wireless network protocols

Computers are incredibly fast, accurate, and stupid. p y , , pHuman beings are incredibly slow, inaccurate, and brilliant. Together they are powerful beyond imagination.

Albert Einstein