Progress with UK e-Science

Cardiff University School of Computer Science

Welsh e-Science Centre

Malcolm AtkinsonDirector e-Science Institute

UK e-Science Envoy

www.nesc.ac.uk23rd May 2007

OverviewHistory of e-Science in UK > 6 years

Three Significant Strengths Established

Communities & Breadth

Science projects (70% of funding,Demanding drivers)

e-Infrastructure(hardware,

software & training)

Office of Cyberinfrastructure

D. E. Atkins

Office of Cyberinfrastructure

D. E. Atkins

Transformative Application - to

enhance discovery & learning

R&D to enhance technical and social dimensions of future CI

systems

Provisioning -Creation,

deployment and operation of advanced CI

Achieving the CI Vision requires

synergy between 3 types of Foundation wide activities

Defining e-Science

e-Science: Systematic Support for Collaborative Research using advanced ICT

Multi-disciplinary, Multi-Site & Multi-NationalAll disciplines contribute & benefitEnabling wider engagementBuilding on and demanding advances in Computing Science

Using advances in computing to support research, design, diagnosis

Dates back 50 yearsPrevalent in branches of biology >30 yearsPrevalent in Engineering for >40 years

New emphasis on systematic support for collaboration, sharing & interdisciplinarity

UK e-Science

e- Science and the Grid‘e- Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it.’

‘e- Science will change the dynamic of the way science is undertaken.’

J ohn TaylorDirector General of Research Councils

Offi ce of Science and Technology

From presentation by Tony Hey

GGF5 Edinburgh

UK e-Science Diversity

Thriving CommunityAll disciplines & all Research CouncilsIndustry & AcademiaMany universities & research institutesUK e-Science All Hands MeetingsProductive collaboration

e-Infrastructure

A shared resourceThat enables science, research, engineering, medicine, industry, …It will improve UK / European / … productivity

Lisbon Accord 2000 E-Science Vision SR2000 –

John Taylor

Commitment by UK government

Sections 2.23-2.25

Always there c.f. telephones, transport,

power

OSI report www.nesc.ac.uk/documents/

OSI/index.html

Slide from Carole Goble

Kyra Norman and Orchestra Cube; Photo: Rob Bristow, June 2006Slide: Angela Piccini

http://www.allhands.org.uk/index.html

EdinburghEdinburgh

ThemesInformation Services for Smart Decision making

Exploiting Diverse Data Sources

Usability - Barriers toUptake

Geospatial semantics

Distributed programming abstractions

Arts & Humanities requirements

Workshops

Summer Schools

Activity

Slide from Dr Anna Kenway

Theme 3: Adoption of e-Research Technologies

Theme 4: Spatial Semantics for Automating Geographic Information Processes

Theme 5: Distributed Programming Abstractions

Theme 6: e-Science in the Arts and Humanities

National Grid Service and partners

National Grid Service and partners

STFC HarwellSTFC Harwell

EdinburghEdinburgh

STFC DaresburySTFC DaresburyManchesterManchester

LancasterLancaster

LondonLondonCardiffCardiff BristolBristol

LeedsLeedsYorkYork

SheffieldSheffield

OxfordOxford

GlasgowGlasgow

UK e-Infrastructure

LHC

I SI S TS2

HPCx + HECtoR

Users get common access, tools, inf ormation, Nationally supported services, through NGS

I ntegratedinternationally

VRE, VLE, IE

Regional and Campus grids

Community Grids

Slide: Neil Geddes

e-Science Centres in the UKe-Science Centres in the UKe-Science Centres in the UKe-Science Centres in the UK

OxfordOxford

EdinburghEdinburgh

BelfastBelfast

CambridgeCambridgeSTFC DaresburySTFC Daresbury

ManchesterManchester

LeSCLeSC

NewcastleNewcastle

SouthamptonSouthampton

CardiffCardiff

STFC HarwellSTFC Harwell

GlasgowGlasgow

LeicesterLeicester

UCLUCL

BirminghamBirmingham

White RoseGrid

White RoseGrid

BristolBristol

LancasterLancaster

ReadingReading

Access GridSupport Centre

Access GridSupport Centre

Digital Curation CentreDigital Curation Centre

National GridService

National GridService

National Centrefor e-Social

Science

National Centrefor e-Social

Science

National Centre forText Mining

National Centre forText Mining

National Institutefor Environmental

e-Science

National Institutefor Environmental

e-Science

Open MiddlewareInfrastructure Institute

Open MiddlewareInfrastructure Institute

SheffieldSheffieldSheffieldSheffield

YorkYorkYorkYork

LeedsLeedsLeedsLeeds

Coordinated by:Directors’ Forum

& NeSC

Coordinated by:Directors’ Forum

& NeSC

OMII-UK nodes

EdinburghEdinburgh

EPCC & National e-Science CentreEPCC & National e-Science Centre

ManchesterManchester

School of Computer ScienceUniversity of Manchester

School of Computer ScienceUniversity of Manchester

SouthamptonSouthampton

School of Electronics andComputer Science

University of Southampton

School of Electronics andComputer Science

University of Southampton

OMII-UK Software

Software catalogue

Software repository

Special Product Lines

Community deposits

SE QA pipeline

Community software stacks

Commissionedprogramme

Software spotted on safarior by Product or Area Liaisons (PALs) Data

WorkflowPortal

Service registry

Infrastructure and Standards Community

User Community

ForeignDistributions

Open Source

OMII-BPEL

The NERC Success

Professor Robert GurneyDirector, Environmental Systems Science Centre, Reading

The NERC e-Science experience 11 papers in NatureEnthusiastic uptake of ensemble methods

climateprediction.net Users Worldwide>300,000 users total (90% MS Windows): >60,000 active~17 million model-years simulated (as of September '06)

~180,000 completed simulations

The world's largest climate modelling supercomputer!(NB: a black dot is one or more computers running climateprediction.net)

Slide: Robert Gurney

Impact:New ScienceUnderstanding of scienceEngaging schoolsBBC follow on

David De Roure

Slide: Dave De Roure & Jeremy Frey

TimelineTimeline

Today

BroadcastingBroadcasting100 years100 years

BroadcastingBroadcasting100 years100 years

TelecommunicationsTelecommunications170 years170 years

TelecommunicationsTelecommunications170 years170 years

PrintingPrinting600 years600 yearsPrintingPrinting

600 years600 years

WritingWriting5,000 years5,000 years

WritingWriting5,000 years5,000 years

Grunts andGrunts andbody languagebody language500,000 years500,000 years

Grunts andGrunts andbody languagebody language500,000 years500,000 years

SpeechSpeech300,000 years300,000 years

SpeechSpeech300,000 years300,000 years

Home ComputersHome ComputersInternet and WWWInternet and WWW

Mobile phonesMobile phonesGrid and Web 2.0Grid and Web 2.0

Web 3.0 and Ubiquitous connected devicesWeb 3.0 and Ubiquitous connected devices30 years30 years

Home ComputersHome ComputersInternet and WWWInternet and WWW

Mobile phonesMobile phonesGrid and Web 2.0Grid and Web 2.0

Web 3.0 and Ubiquitous connected devicesWeb 3.0 and Ubiquitous connected devices30 years30 years

“Wellbeing” the global-scale killer app., Sir Robin Saxby Oct. 2006

Foundations for Collaborative Behaviour

PatientHome-mobile-clinic

via TV-PDA-laptop-PC-Paper

Diabetes Specialist / Other Specialist Nurses

Home-mobile-clinicvia TV-PDA-laptop-PC-Paper

Dietitian

DiabeticianHome-mobile-clinic

via PDA-laptop-PC-Paper

Biochemist

GPHome-mobile-clinic

via PDA-laptop-PC-Paper

Various Clinical Specialists (Distributed)e.g. Ophthalmologist, Podiatrist, Vascular

Surgeons, Renal Specialists, Wound clinic, Foot care clinic, Neurologists, Cardiologists

ILLNESS

REFERRAL REFERRAL

REFERRAL

CASE

Community Nurses / Health Visitors

VARIABLESACCESSMATRIX

Healthcare @ Home

“Wellbeing” the global-scale killer app., Sir Robin Saxby Oct. 2006Slide from Alex Hardisty

DAME/BROADEN http://www.cs.york.ac.uk/dame/

• Aims to manage >1Tb per year of Aero Engine vibration and maintenance data.

• Interlinks with search and reasoning services.

• Defined and evaluated a distributed search system.

• GSI enabled secure engine performance simulation

• CBR advisor for diagnostic engineer• A data architecture defined based on

Globus and SRB.

• BROADEN DTI Project (£3.9M)• Spun out technology exploited

through Cybula Ltd., Oxford Biosignals and DS&S.

• Successful mid-term demonstrator well received by Rolls Royce

• White Rose Grid: experience of building & using production Grids

• In Grid Blue Print 2 edition 2

• Jim Austin (Comp Sci, York)• 4 Universities and institutes• 3 Companies

Aircraft healthcare diagnosis

Slide: Carole Goble, Jim Fleming & Jim Austin

resolving the ‘neural code’ from the timing of action potential activity

determining ion channel contribution to the timing of action potentials

examining integration within networks of differing dimensions

Understanding the brain may be the greatest

informatics challenge of the 21st century

Source: Colin Ingram

New EPSRC project. CARMENlate 2006 - 2009 http://bioinf.ncl.ac.uk/carmen/

MESSAGE – overview • Heterogeneous fixed and

mobile sensors on infrastructure, vehicles and people

• Sensors communicate via wireless networks

• Positioning via GPS + wireless & cellular ranging

• Integration of processing along the data path

• Multiple application studies in different local contexts Slide from John Polak

MESSAGE – multi-disciplinarity• MESSAGE involves integrating academic

expertise from several disciplines:

– 1Transport network modellers

– Air quality modellers

– Geomatricians

– Computer Scientists

– Electrical Engineers

– Sensor Developers (Physicists and Chemists)

• Together with industry experience and tangible real-world applications

Slide from John Polak

MESSAGE – research challenges

• Field units– Sensors

– Positioning

– Communications

• e-Science– Scalability

– Distributed data mining

– Online estimation of pollutant hotspots

• Transport and environment modelling – Traffic management and control

– Traveller information

Slide from John Polak

www.nanocmos.ac.uk

The ChallengeThe Challenge

6th September 2006

International Tech nology Roadmap for Semiconductors Year 2005 2010 2015 2020

MPU Half Pitch (nm) 90 45 25 14

MPU Gate Length (nm) 32 18 10 6

2005 edition Toshiba 04

Device diversification

90nm: HP, LOP, LSTP

45nm: UTB SOI

32nm: Double gate

25 nm

Bulk MOSFET

FD SOI

UTB SOI

FinFET

HP(MPU)

LOP

LSTP

Stat.Sets

230 nm

Bulk MOSFET

Standard

SingleSet

Slide from Asen Asenov

FireGrid

PiperPiperAlphaAlpha

Mont BlancMont Blanc

KobeKobe

Kings CrossKings Cross WTCWTC

Logging

FireGrid Architecture

Routine & Initial Workflows

sensor validation & calibration, building and

people status & event detection

Building data Pre-computedscenarios

Escalated Workflows

From PCs to teraflops

Displays from sensors and simulations

C&CView selected

status displays & user control panels

Personal & TeamPreference data

5 People

A C

D

E

B

A C

D

E

B

A C

D

E

B

Sensors & Actuators

Temp, CO, smoke,displacement/strain, vibration/acoustic,

systems status

Primary monitoring & gateways

between sensor nets & grid

Workflowselection& steering

Data-flowselection& actuation

SouthEasternEurope, 10%

SouthWesternEurope, 12% Italy, 16%

France, 18%

UKI, 29%NorthernEurope, 7%

CentralEurope, 4%

AsiaPacific, 2%

GermanySwitzerland, 1%

Russia, 1%

WISDOM deployment : wisdom.eu-egee.fr

Total amount of CPU provided by EGEE

federation

Countries with nodes contributing to the data challenge WISDOM

•10•UK•1•Poland•1•Germany

•1•Taiwan•2•Netherlands•9•France

•7•Spain•13•Italy•1•Cyprus

•2•Russia•1•Israel•1•Croatia

•1•Romania•3•Greece•3•Bulgaria

•sites•country•sites•country•sites•country

Discovery Net China SARS Virtual Lab

Relationship between SARS and other virus

Mutual regions identification

Homology search against viral genome DB

Annotation using Artemis and GenSense

Gene prediction

Phylogenetic analysis

Exon prediction

Splice site prediction

Immunogenetics

Multiple sequence alignment

Microarray analysis

Bibliographic databases

Key word search

GeneSenseOntology

D-Net:Integration,

interpretation, and

discovery

Epidemiological analysis

Predicted genes

SARS patients diagnosis

Homology search against protein DB

Homology search against motif DB

Protein localization site

prediction

Protein interaction prediction

Relationship between SARS

virus and human receptors prediction

Classification and secondary structure prediction

Bibliographic databases

Genbank

Annotation using Artemis and GenSense

Used now in Institute for Animal Health, UK

Source: Yike Guo and Moustafa Ghanem

Mouse models of trypanotolerance.

Survival of F6 and parental strains

010

2030

405060

7080

90100

1 21 41 61 81 101 121

Days Post Challenge%

Sur

viva

l

F6

AJ

C57BL

T brucei rhodesiense T gambiense

T. congolense, T. vivax

http://www.genomics.liv.ac.uk/tryps/trypsindex.html

Source: Andy Brass

Learning & Teaching workflows

Research & e-Science workflows

Aggregator services: national, commercial

Repositories : institutional, e-prints, subject, data, learning objects

Institutional presentation services: portals, Learning Management Systems, u/g, p/g courses, modules

Harvestingmetadata

Data creation / capture / gathering: laboratory experiments, Grids, fieldwork, surveys, media

Resource discovery, linking, embedding

Deposit / self-archiving

Peer-reviewed publications: journals, conference proceedings

Publication

Validation

Data analysis, transformation, mining, modelling

Resource discovery, linking, embedding

Deposit / self-archiving

Learning object creation, re-use

Searching , harvesting, embedding

Quality assurance bodies

Validation

Presentation services: subject, media-specific, data, commercial portals

Resource discovery, linking, embedding

The scholarly knowledge cycle.

Liz Lyon, Ariadne, July 2003.

This work is licensed under a Creative Commons LicenseAttribution-ShareAlike 2.0

© Liz Lyon (UKOLN, University of Bath), 2003

Data capture

Slide: Dave De Roure & Jeremy Frey

Slide: Dave De Roure & Jeremy Frey

Amazon Web Services

Web 2.0 APIshttp://www.programmableweb.com/apis currently (Jan 10 2007) 356 Web 2.0 APIs with GoogleMaps the most used in MashupsThis site acts as a “UDDI” for Web 2.0

Geoffrey Fox

Europe FP7http://cordis.europa.eu/fp7/

e-HealthThe Virtual Human

Challenges for e-Science

Understand what enables collaboration

InterdisciplinaryMulti-siteThrough timeWith realism about motives & competition

Find the best ways of supporting itIs this a one-size fits all opportunity?It requires an inter-disciplinary approachTechnology push or pull?

Abstract and communicate

Challenges for e-Science 2

Creating wider understandingIn researchersIn fundersIn the public

Find the best ways of creating understanding

Articulate the stories?Analyse the successesEducate the emerging generation?An interdisciplinary challenge

Abstract and communicate

INFSO-SSA-26637

Training & Education Spectrum

• Training– Targeted– Immediate goals– Specific skills– Building a workforce

• Education– Pervasive– Long term and sustained– Generic conceptual models– Developing a culture

• Both are needed

Society

Graduates

EducationInnovation

Invests

PreparesCreate

Enriches

Organisation

Skilled Workers

TrainingServices & Applications

Invests

PreparesDevelop

Strengthens

ICEAGE & Forum’s Primary Focus

Changing Culture

Three Educational Challenges

The Computing & Computational CoursesRecognition of the importance of scale and complexitySystems thinkingSupport for composition and orchestrationNumerical and Simulation skillsData intensive engineeringDistributed systemsComputational engineeringAbstraction skillsInsights into usabilityExperience working in multi-disciplinary applications

Three Educational Challenges

The Disciplines that may apply e-Science

Understanding potential & limits of modelsExploiting tools that capture methods and processesSuccess stories and exemplars in cognate disciplinesExperience working in multi-disciplinary collaborationsAppreciation of costs and responsibilities

Three Educational Challenges

A new engineering discipline - Designing, Building & Operating continuously available systems

Changing the engines on a 747 while flying passengers at 39,000 feet!Planning & designing systemsPlanning & designing operational proceduresUnderstanding risks and their managementUnderstanding workload dynamicsPredicting resource and system requirementsDeveloping abstractions that enable this to be done reliably in every deployed system

Take Home

UK e-Science investment has built three interdependent strengths:

Communities & collaborationProjects delivering & demandinge-Infrastructure: organisation, support & technology

Three success factors for projectsEngagement & value for all participantsCreativity & insight addressing a well-posed challengeTechnology adoption and innovation

Progress in research domains is the driverIntegrate whatever technology you needInvent new technology only if you have to