TICER Summer School, August 24th 20061 Ticer Summer School Thursday 24 th August 2006 Dave Berry &...

TICER Summer School, August 24th 2006 1

Ticer Summer School

Thursday 24th August 2006

Dave Berry & Malcolm AtkinsonNational e-Science Centre, Edinburghwww.nesc.ac.uk


Digital Libraries, Grids & E-ScienceDigital Libraries, Grids & E-Science

What is E-Science?

What is Grid Computing?

Data Grids Requirements

Examples

Technologies

Data Virtualisation

The Open Grid Services Architecture

Challenges



What is e-Science?What is e-Science?

• Goal: to enable better research in all disciplines• Method: Develop collaboration supported by

advanced distributed computation– to generate, curate and analyse rich data resources

• From experiments, observations, simulations & publications• Quality management, preservation and reliable evidence

– to develop and explore models and simulations• Computation and data at all scales• Trustworthy, economic, timely and relevant results

– to enable dynamic distributed collaboration• Facilitating collaboration with information and resource sharing• Security, trust, reliability, accountability, manageability and agility

climateprediction.net and GENIE

• Largest climate model ensemble

• >45,000 users, >1,000,000 model years

10K2K

Response of Atlantic circulation to freshwater forcing

6Courtesy of David Gavaghan & IB Team

Integrative Biology

Tackling two Grand Challenge research questions:

• What causes heart disease?• How does a cancer form and grow?

Together these diseases cause 61% of all UK deaths

Building a powerful, fault-tolerant Grid infrastructure for biomedical science

Enabling biomedical researchers to use distributed resources such as high-performance computers, databases and visualisation tools to develop coupled multi-scale models of how these killer diseases develop.

BBiomedical iomedical RResearch esearch IInformatics nformatics DDelivered by elivered by GGrid rid EEnabled nabled SServiceservices

Glasgow Edinburgh

Leicester Oxford

London

Netherlands

Publically Curated Data

Private data

Private data

Private data

Private data

Private data

Private data

CFG Virtual Organisation Ensembl

MGI

HUGO

OMIM

SWISS-PROT

… DATA HUB

RGD

SyntenyGrid

Service

blast

+

Portal

http://www.brc.dcs.gla.ac.uk/projects/bridges/


eDiaMoND: Screening for Breast CancereDiaMoND: Screening for Breast Cancer

1 Trust Many TrustsCollaborative WorkingAudit capabilityEpidemiology

Other Modalities-MRI-PET-Ultrasound

Better access toCase informationAnd digital tools

Supplement MentoringWith access to digitalTraining cases and sharingOf information acrossclinics

LettersRadiology reportingsystems

eDiaMoNDGrid

2ndary CaptureOr FFD

Case Information

X-Rays andCase Information

DigitalReading

SMF

Case andReading Information

CAD Temporal Comparison

Screening

ElectronicPatient Records

Assessment/ SymptomaticBiopsy

Case andReading Information

Symptomatic/AssessmentInformation

Training

Manage Training Cases

Perform Training

SMF CAD 3D Images

Patients

Provided by eDiamond project: Prof. Sir Mike Brady et al.


E-Science Data ResourcesE-Science Data Resources

• Curated databases– Public, institutional, group, personal

• Online journals and preprints

• Text mining and indexing services

• Raw storage (disk & tape)

• Replicated files

• Persistent archives

• Registries

• …

TICER Summ

er School

, August

24th 2006©

10

EBank

Slide from Jeremy Frey

TICER Summ

er School

, August

24th 2006©

11

Biomedical data – making connections

12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct tttttagctt 12301 gaccatccta atagatacac agtggtgtct cactgtgatt ttaatttgca ttttcctgct 12361 gactaattat gttgagcttg ttaccattta gacaacttca ttagagaagt gtctaatatt 12421 taggtgactt gcctgttttt ttttaattgg

Slide provided by Carole Goble: University of Manchester


Using Workflows to Link ServicesUsing Workflows to Link Services

• Describe the steps in a Scripting Language• Steps performed by Workflow Enactment Engine• Many languages in use

– Trade off: familiarity & availability– Trade off: detailed control versus abstraction

• Incrementally develop correct process– Sharable & Editable– Basis for scientific communication & validation– Valuable IPR asset

• Repetition is now easy– Parameterised explicitly & implicitly


Workflow SystemsWorkflow Systems

Language WF Enact. Comments

Shell scripts

Shell + OS Common but not often thought of as WF. Depend on context, e.g. NFS across all sites

Perl Perl runtime

Popular in bioinformatics. Similar context dependence – distribution has to be coded

Java JVM Popular target because JVM ubiquity – similar dependence – distribution has to be coded

BPEL BPEL Enactment

OASIS standard for industry – coordinating use of multiple Web Services – low level detail - tools

Taverna Scufl EBI, OMII-UK & MyGrid http://taverna.sourceforge.net/index.php

VDT / Pegasus

Chimera & DAGman

High-level abstract formulation of workflows, automated mapping towards executable forms, cached result re-use

Kepler Kepler BIRN, GEON & SEEKhttp://kepler-project.org/

TICER Summ

er School

, August

24th 2006©

14

Workflow example

Taverna in MyGrid http://www.mygrid.org.uk/ “allows the e-Scientist to describe and enact their

experimental processes in a structured, repeatable and verifiable way”

GUI Workflow

language Enactment

engine

TICER Summ

er School

, August

24th 2006©

15

Pub/Sub for Laboratory data using a broker and ultimately delivered over GPRS

Notification

Comb-e-chem: Jeremy Frey


Relevance to Digital LibrariesRelevance to Digital Libraries

• Similar concerns– Data curation & management– Metadata, discovery– Secure access (AAA +)– Provenance & data quality– Local autonomy– Availability, resilience

• Common technology– Grid as an implementation technology



What is a Grid?

LicenseLicense

PrinterPrinter

A grid is a system consisting of− Distributed but connected resources and − Software and/or hardware that provides and manages logically

seamless access to those resources to meet desired objectives

A grid is a system consisting of− Distributed but connected resources and − Software and/or hardware that provides and manages logically

seamless access to those resources to meet desired objectives

R2AD

DatabaseDatabase

Webserver

Webserver

Data CenterCluster

Handheld Supercomputer

Workstation

Server

Source: Hiro Kishimoto GGF17 Keynote May 2006


Virtualizing Resources

Resources

Webservices

AccessAccess

StorageStorage SensorsSensors ApplicationsApplications InformationInformationComputersComputers

Resource-specific InterfacesResource-specific Interfaces

Common Interfaces

Type-specific interfaces

Hiro Kishimoto: Keynote GGF17


Ideas and FormsIdeas and Forms

• Key ideas– Virtualised resources– Secure access– Local autonomy

• Many forms– Cycle stealing– Linked supercomputers– Distributed file systems– Federated databases– Commercial data centres– Utility computing


Grid Middleware

Virtualized resources

Grid middleware

services

Brokering Service

Brokering Service

Registry Service

Registry Service

DataService

DataService

CPU ResourceCPU ResourcePrinter Service

Printer Service

Job-Submit Service

Job-Submit Service

ComputeService

ComputeService

No

tify

Ad

vertise

ApplicationService

ApplicationService

Hiro Kishimoto: Keynote GGF17


Key Drivers for GridsKey Drivers for Grids

• Collaboration– Expertise is distributed– Resources (data, software licences) are location-specific– Necessary to achieve critical mass of effort– Necessary to raise sufficient resources

• Computational Power– Rapid growth in number of processors– Powered by Moore’s law + device roadmap– Challenge to transform models to exploit this

• Deluge of Data– Growth in scale: Number and Size of resources– Growth in complexity– Policy drives greater data availability


Minimum Grid FunctionalitiesMinimum Grid Functionalities

• Supports distributed computation– Data and computation– Over a variety of

• hardware components (servers, data stores, …)• Software components (services: resource managers,

computation and data services)

– With regularity that can be exploited• By applications• By other middleware & tools• By providers and operations

– It will normally have security mechanisms • To develop and sustain trust regimes

TICER Summer School, August 24th 2006 24Source: Hiro Kishimoto GGF17 Keynote May 2006

Grid & Related Paradigms

Utility Computing• Computing “services”• No knowledge of provider• Enabled by grid technology

Utility Computing• Computing “services”• No knowledge of provider• Enabled by grid technology

Distributed Computing• Loosely coupled• Heterogeneous• Single Administration

Distributed Computing• Loosely coupled• Heterogeneous• Single Administration

Cluster• Tightly coupled• Homogeneous• Cooperative working

Cluster• Tightly coupled• Homogeneous• Cooperative working

Grid Computing• Large scale• Cross-organizational• Geographical distribution• Distributed Management

Grid Computing• Large scale• Cross-organizational• Geographical distribution• Distributed Management



Why use / build Grids?Why use / build Grids?

• Research Arguments– Enables new ways of working– New distributed & collaborative research– Unprecedented scale and resources

• Economic Arguments– Reduced system management costs– Shared resources better utilisation– Pooled resources increased capacity– Load sharing & utility computing – Cheaper disaster recovery


Why use / build Grids?Why use / build Grids?

• Operational Arguments– Enable autonomous organisations to

• Write complementary software components• Set up run & use complementary services• Share operational responsibility• General & consistent environment for

Abstraction, Automation, Optimisation & Tools

• Political & Management Arguments– Stimulate innovation– Promote intra-organisation collaboration– Promote inter-enterprise collaboration


Grids In Use: E-Science Examples

• Data sharing and integration− Life sciences, sharing standard data-sets,

combining collaborative data-sets− Medical informatics, integrating hospital information

systems for better care and better science− Sciences, high-energy physics

• Data sharing and integration− Life sciences, sharing standard data-sets,

combining collaborative data-sets− Medical informatics, integrating hospital information

systems for better care and better science− Sciences, high-energy physics

• Capability computing− Life sciences, molecular modeling, tomography− Engineering, materials science− Sciences, astronomy, physics

• Capability computing− Life sciences, molecular modeling, tomography− Engineering, materials science− Sciences, astronomy, physics

• High-throughput, capacity computing for − Life sciences: BLAST, CHARMM, drug screening− Engineering: aircraft design, materials, biomedical− Sciences: high-energy physics, economic modeling

• High-throughput, capacity computing for − Life sciences: BLAST, CHARMM, drug screening− Engineering: aircraft design, materials, biomedical− Sciences: high-energy physics, economic modeling

• Simulation-based science and engineering− Earthquake simulation

• Simulation-based science and engineering− Earthquake simulation

Source: Hiro Kishimoto GGF17 Keynote May 2006


PDB 33,367 Protein structuresEMBL DB 111,416,302,701 nucleotides

Database GrowthDatabase Growth

Slide provided by Richard Baldock: MRC HGU Edinburgh


Requirements: User’s viewpointRequirements: User’s viewpoint

• Find Data– Registries & Human communication

• Understand data– Metadata description, Standard / familiar formats &

representations, Standard value systems & ontologies

• Data Access– Find how to interact with data resource– Obtain permission (authority)– Make connection– Make selection

• Move Data– In bulk or streamed (in increments)


Requirements: User’s viewpoint 2Requirements: User’s viewpoint 2

• Transform Data– To format, organisation & representation

required for computation or integration

• Combine data– Standard database operations + operations relevant to

the application model

• Present results– To humans: data movement + transform for viewing– To application code: data movement + transform to the

required format– To standard analysis tools, e.g. R– To standard visualisation tools, e.g. Spitfire


Requirements: Owner’s viewpointRequirements: Owner’s viewpoint

• Create Data– Automated generation, Accession Policies, Metadata

generation– Storage Resources

• Preserve Data– Archiving– Replication– Metadata– Protection

• Provide Services with available resources– Definition & implementation: costs & stability– Resources: storage, compute & bandwidth


Requirements: Owner’s viewpoint 2Requirements: Owner’s viewpoint 2

• Protect Services– Authentication, Authorisation, Accounting, Audit– Reputation

• Protect data– Comply with owner requirements – encryption for privacy,

…

• Monitor and Control use– Detect and handle failures, attacks, misbehaving users– Plan for future loads and services

• Establish case for Continuation– Usage statistics– Discoveries enabled



Large Hadron ColliderLarge Hadron Collider

• The most powerful instrument ever built to investigate elementary particle physics

• Data Challenge:– 10 Petabytes/year of data– 20 million CDs each year!

• Simulation, reconstruction, analysis:– LHC data handling requires

computing power equivalent to ~100,000 of today's fastest PC processors


Composing Observations in AstronomyComposing Observations in Astronomy

Data and images courtesy Alex Szalay, John Hopkins

No. & sizes of data sets as of mid-2002, grouped by wavelength• 12 waveband coverage of large areas of the sky• Total about 200 TB data• Doubling every 12 months• Largest catalogues near 1B objects

GODIVA Data Portal• Grid for Ocean Diagnostics, Interactive

Visualisation and Analysis

• Daily Met Office Marine Forecasts and gridded research datasets

• National Centre for Ocean Forecasting

• ~3Tb climate model datastore via Web Services

• Interactive Visualisations inc. Movies

• ~ 30 accesses a day worldwide

• Other GODIVA software produces 3D/4D Visualisations reading data remotely via Web Services

Online Movies

www.nerc-essc.ac.uk/godiva

GODIVA Visualisations• Unstructured Meshes

• Grid Rotation/Interpolation

• GeoSpatial Databases v. Files (Postgres, IBM, Oracle)• Perspective 3D Visualisation

• Google maps viewer

NERC Data Grid

• The DataGrid focuses on federation of NERC Data Centres

• Grid for data discovery, delivery and use across sites

• Data can be stored in many different ways (flat files, databases…)

• Strong focus on Metadata and Ontologies

• Clear separation between discovery and use of data.

• Prototype focussing on Atmospheric and Oceanographic data

www.ndg.nerc.ac.uk

Global In-flight Engine DiagnosticsGlobal In-flight Engine Diagnostics

in-flight data

airline

maintenance centre

ground station

global networkeg SITA

internet, e-mail, pager

DS&S Engine Health Center

data centre

Distributed Aircraft Maintenance Environment: Leeds, Oxford, Sheffield &York, Jim Austin

100,000 aircraft

0.5 GB/flight

4 flights/day

200 TB/day

Now BROADEN

Significant ingetting Boeing 787 engine contract



Storage Resource Manager (SRM)Storage Resource Manager (SRM)

• http://sdm.lbl.gov/srm-wg/• de facto & written standard in physics, …• Collaborative effort

– CERN, FNAL, JLAB, LBNL and RAL

• Essential bulk file storage– (pre) allocation of storage

• abstraction over storage systems

– File delivery / registration / access– Data movement interfaces

• E.g. gridFTP

• Rich function set– Space management, permissions, directory, data transfer

& discovery


Storage Resource Broker (SRB)Storage Resource Broker (SRB)

• http://www.sdsc.edu/srb/index.php/Main_Page• SDSC developed• Widely used

– Archival document storage– Scientific data: bio-sciences, medicine, geo-sciences, …

• Manages – Storage resource allocation

• abstraction over storage systems

– File storage– Collections of files– Metadata describing files, collections, etc. – Data transfer services


Condor Data ManagementCondor Data Management

• Stork– Manages File Transfers– May manage reservations

• Nest– Manages Data Storage– C.f. GridFTP with reservations

• Over multiple protocols


Globus Tools and Services for Data Management

GridFTP A secure, robust, efficient data transfer protocol

The Reliable File Transfer Service (RFT) Web services-based, stores state about transfers

The Data Access and Integration Service (OGSA-DAI) Service to access to data resources, particularly relational and

XML databases

The Replica Location Service (RLS) Distributed registry that records locations of data copies

The Data Replication Service Web services-based, combines data replication and

registration functionality

Slides from Ann Chervenak


RLS in Production Use: LIGO

Laser Interferometer Gravitational Wave Observatory Currently use RLS servers at 10 sites

Contain mappings from 6 million logical files to over 40 million physical replicas

Used in customized data management system: the LIGO Lightweight Data Replicator System (LDR)

Includes RLS, GridFTP, custom metadata catalog, tools for storage management and data validation

Slides from Ann Chervenak

Date post:	28-Dec-2015
Category:	Documents
Upload:	garry-hodge
View:	213 times
Download:	1 times

TICER Summer School, August 24th 20061 Ticer Summer School Thursday 24 th August 2006 Dave Berry &...

Documents