11

CyberinfrastructureTechnologies and Applications

Summit on Cyberinfrastructure: Innovation At WorkBanff Springs Hotel

Banff Canada October 11 2007

Geoffrey FoxComputer Science, Informatics, Physics

Pervasive Technology LaboratoriesIndiana University Bloomington IN 47401

http://grids.ucs.indiana.edu/ptliupages/presentations/gcf@indiana.edu http://www.infomall.org

2222

e-moreorlessanything ‘e-Science is about global collaboration in key areas of science,

and the next generation of infrastructure that will enable it.’ fromits inventor John Taylor Director General of Research CouncilsUK, Office of Science and Technology

e-Science is about developing tools and technologies that allowscientists to do ‘faster, better or different’ research

Similarly e-Business captures an emerging view of corporations asdynamic virtual organizations linking employees, customers andstakeholders across the world.

This generalizes to e-moreorlessanything including presumably e-AlbertaEnterprise and e-oilandgas, e-geoscience ….

A deluge of data of unprecedented and inevitable size must bemanaged and understood.

People (see Web 2.0), computers, data (including sensors andinstruments) must be linked.

On demand assignment of experts, computers, networks andstorage resources must be supported

3333

What is Cyberinfrastructure Cyberinfrastructure is (from NSF) infrastructure that

supports distributed science (e-Science)– data, people,computers• Clearly core concept more general than Science

Exploits Internet technology (Web2.0) adding (via Gridtechnology) management, security, supercomputers etc.

It has two aspects: parallel – low latency (microseconds)between nodes and distributed – highish latency (milliseconds)between nodes

Parallel needed to get high performance on individual largesimulations, data analysis etc.; must decompose problem

Distributed aspect integrates already distinct components –especially natural for data

Cyberinfrastructure is in general a distributed collection ofparallel systems

Cyberinfrastructure is made of services (originally Webservices) that are “just” programs or data sources packagedfor distributed access

44

Underpinnings ofCyberinfrastructure

Distributed software systems are being “revolutionized” bydevelopments from e-commerce, e-Science and the consumerInternet. There is rapid progress in technology families termed“Web services”, “Grids” and “Web 2.0”

The emerging distributed system picture is of distributed serviceswith advertised interfaces but opaque implementationscommunicating by streams of messages over a variety of protocols• Complete systems are built by combining either services or

predefined/pre-existing collections of services together toachieve new capabilities

As well as Internet/Communication revolutions (distributedsystems), multicore chips will likely be hugely important (parallelsystems)

Industry not academia is leading innovation in these technologies

55

Service or Web Service Approach One uses GML, CML etc. to define the data structure in a

system and one uses services to capture “methods” or“programs”

In eScience, important services fall in three classes• Simulations• Data access, storage, federation, discovery• Filters for data mining and manipulation

Services could use something like WSDL (Web ServiceDefinition Language) to define interoperable interfaces but Web2.0 follows old library practice: one just specifies interface

Service Interface (WSDL) establishes a “contract” independentof implementation between two services or a service and a client

Services should be loosely coupled which normally means theyare coarse grain

Services will be composed (linked together) by mashups(typically scripts) or workflow (often XML – BPEL)

Software Engineering and Interoperability/Standards are closelyrelated

SDSC

TACC

UC/ANL

NCSA

ORNL

PU

IU

PSC

NCAR

Caltech

USC/ISI

UNC/RENCI

UW

Resource Provider (RP)

Software Integration Partner

Grid InfrastructureGroup (UChicago)

Computing and Cyberinfrastructure: TeraGrid

TeraGrid resources include more than 250 teraflops of computing capability and more than 30 petabytes ofonline and archival data storage, with rapid access and retrieval over high-performance networks. TeraGridis coordinated at the University of Chicago, working with the Resource Provider sites: Indiana University,Oak Ridge National Laboratory, National Center for Supercomputing Applications, PittsburghSupercomputing Center, Purdue University, San Diego Supercomputer Center, Texas Advanced ComputingCenter, University of Chicago/Argonne National Laboratory, and the National Center for AtmosphericResearch.

77

Data and Cyberinfrastructure DIKW: Data Information Knowledge Wisdom

transformation Applies to e-Science, Distributed Business Enterprise (including

outsourcing), Military Command and Control and generaldecision support

(SOAP or just RSS) messages transport information expressedin a semantically rich fashion between sources and services thatenhance and transform information so that complete systemprovides• Semantic Web technologies like RDF and OWL might help us

to have rich expressivity but they might be too complicated We are meant to build application specific information

management/transformation systems for each domain• Each domain has Specific Services/Standards (for API’s and Information

such as KML and GML for Geographical Information Systems)• and will use Generic Services (like R for datamining) and• Generic Standards (such as RDF, WSDL)

Standards made before consensus or not observant of technologyprogress are dubious

8Database

SS

SS

SS

SS

SS

SS

SS

SS

SS

SS

FS

FS

FS

FS

FS

FS

FS

FS FS

FS

FS

FS

FS

FS

FS

FS

FS FS

FS

FS

PortalFS

OS

OS

OSOS

OS

OS

OS

OS

OS

OS

OS

OS

MD

MD

MD

MD

MD

MD

MD

MD

MD

MetaDataFilter Service

Sensor Service

OtherService

AnotherGrid

Raw Data Data Information Knowledge Wisdom

Decisions

SS

SS

AnotherService

AnotherService

SSAnother

Grid SS

AnotherGrid

SS

SS

SS

SS

SS

SS

SS

SS

FS

Inter-Service Messages

Information and Cyberinfrastructure

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Information CyberinfrastructureArchitecture

The Party Line approach to Information Infrastructure is clear– one creates a Cyberinfrastructure consisting of distributedservices accessed by portals/gadgets/gateways/RSS feeds

Services include:• Computing• “original data”• Transformations or filters implementing DIKW (Data Information

Knowledge Wisdom) pipeline• Final “Decision Support” step converting wisdom into action• Generic services such as security, profiles etc.

Some filters could correspond to large simulations Infrastructure will be set up as a System of Systems (Grids of

Grids)• Services and/or Grids just accept some form of DIKW and produce

another form of DIKW• “Original data” has no explicit input; just output

1010

Virtual Observatory Astronomy GridIntegrate Experiments

Radio Far-Infrared Visible

Visible + X-ray

Dust Map

Galaxy Density Map

11

C Y B E R I N F R A S T R U C T U R E C E N T E R F O R P O L A R S C I E N C E ( C I C P S )

12

CReSIS PolarGrid• Important CReSIS-specific Cyberinfrastructure components include

– Managed data from sensors and satellites– Data analysis such as SAR processing – possibly with parallel

algorithms– Electromagnetic simulations (currently commercial codes) to design

instrument antennas– 3D simulations of ice-sheets (glaciers) with non-uniform meshes– GIS Geographical Information Systems

• Also need capabilities present in many Grids– Portal i.e. Science Gateway– Submitting multiple sequential or parallel jobs

• The need for three distinct types of components: Continental USA withmultiple base and field camps– Base and field camps must be power efficient– Terrible connectivity from base and field camps to Continental subGrid

CICC Chemical Informatics and CyberinfrastructureCollaboratory Web Service Infrastructure

Portal ServicesRSS FeedsUser ProfilesCollaboration as in Sakai

Core Grid ServicesService RegistryJob Submission and Management

Local ClustersIU Big Red, TeraGrid, Open Science Grid

Varuna.netQuantum Chemistry

Statistics Services Database Services

Core functionality Computation functionality 3D structures by

Fingerprints Regression CID

Similarity Classification SMARTS

Descriptors Clustering 3D Similarity

2D diagrams Sampling distributions

File format conversion

Docking scores/poses by

Applications Applications CID

Docking Predictive models SMARTS

Filtering Feature selection Protein

2D plots Docking scores

Toxicity predictions

Anti-cancer activity predictions

CID, SMARTS

Cheminformatics Services

Druglikeness

Arbitrary R code (PkCell)

Mutagenecity predictions

PubChem related data by

Pharmacokinetic parameters

OSCAR Document AnalysisInChI Generation/SearchComputational Chemistry (Gamess, Jaguar etc.)

14

Process Chemistry-Biology Interaction Datafrom HTS (High Throughput Screening)

Percent Inhibitionor IC50 data isretrieved from HTS

Question: Was thisscreen successful?

Question: What should theactive/inactive cutoffs be?

Question: What can welearn about the targetprotein or cell line from thisscreen?

Compound data submittedto PubChem

Workflows encodingdistribution analysis ofscreening results

Grids can link dataanalysis ( e.g imageprocessing developedin existing Grids),traditional Chem-informatics tools, aswell as annotationtools (Semantic Web,del.icio.us) andenhance lead ID andSAR analysis

A Grid of Grids linkingcollections of servicesatPubChemECCR centersMLSCN centers

Workflows encodingplate & control wellstatistics, distributionanalysis, etc

Workflows encodingstatistical comparison ofresults to similarscreens, docking ofcompounds into proteinsto correlate binding, withactivity, literature searchof active compounds,etc

CHEMINFORMATICSPROCESS GRIDS

Scientists at IU prefer Web 2.0 toGrid/Web Service for workflow

1515

People and Cyberinfrastructure: Web 2.0 Web 2.0 has tools (sites) and technologies

• Technologies (later) are “competition” for Grids and WebServices

• Sites (below) are the best way to integrate people intoCyberinfrastructure

Kazaa, Instant Messengers, Skype, Napster, BitTorrent for P2PCollaboration – text, audio-video conferencing, files

del.icio.us, Connotea, Citeulike, Bibsonomy, Biolicious manageshared bookmarks

MySpace, YouTube, Bebo, Hotornot, Facebook, or similar sitesallow you to create (upload) community resources and sharethem; Friendster, LinkedIn create networks• http://en.wikipedia.org/wiki/List_of_social_networking_websites

Writely, Wikis and Blogs are powerful specialized shareddocument systems

Google Scholar and Windows Live Academic Search tells you whohas cited your papers while publisher sites tell you about co-authors

16161616

“Best Web 2.0 Sites” -- 2006 Extracted from http://web2.wsj2.com/ Social Networking

Start Pages

Social Bookmarking

Peer Production News

Social Media Sharing

Online Storage(Computing)

1717

Web 2.0 Systems are Portals, Services, Resources Captures the incredible development of interactive

Web sites enabling people to create and collaborate

1818

Web 2.0 and Web Services I Web Services have clearly defined protocols (SOAP) and a well

defined mechanism (WSDL) to define service interfaces• There is good .NET and Java support• The so-called WS-* specifications provide a rich sophisticated but

complicated standard set of capabilities for security, fault tolerance, meta-data, discovery, notification etc.

“Narrow Grids” build on Web Services and provide a robustmanaged environment with growing adoption in Enterprisesystems and distributed science (so called e-Science)

Web 2.0 supports a similar architecture to Web services but hasdeveloped in a more chaotic but remarkably successful fashionwith a service architecture with a variety of protocols includingthose of Web and Grid services• Over 500 Interfaces defined at http://www.programmableweb.com/apis

Web 2.0 also has many well known capabilities with GoogleMaps and Amazon Compute/Storage services of clear generalrelevance

There are also Web 2.0 services supporting novel collaborationmodes and user interaction with the web as seen in socialnetworking sites, portals, MySpace, YouTube,

1919

Web 2.0 and Web Services II I once thought Web Services were inevitable but this is

no longer clear to me Web services are complicated, slow and non functional

• WS-Security is unnecessarily slow and pedantic(canonicalization of XML)

• WS-RM (Reliable Messaging) seems to have pooradoption and doesn’t work well in collaboration

• WSDM (distributed management) specifies a lot There are de facto standards like Google Maps and

powerful suppliers like Google which “define the rules” One can easily combine SOAP (Web Service) based

services/systems with HTTP messages but the “lowestcommon denominator” suggests additionalstructure/complexity of SOAP will not easily survive

2020

Applications, Infrastructure,Technologies

The discussion is confused by inconsistent use of terminology –this is what I mean

Multicore, Narrow and Broad Grids and Web 2.0 (Enterprise2.0) are technologies

These technologies combine and compete to build infrastructurestermed e-infrastructure or Cyberinfrastructure• Although multicore can and will support “standalone” clients probably

most important client and server applications of the future will be internetenhanced/enabled so key aspect of multicore is its role and integration ine-infrastructure

e-moreorlessanything is an emerging application area of broadimportance that is hosted on the infrastructures e-infrastructureor Cyberinfrastructure

2121

Some Web 2.0 Activities at IU Use of Blogs, RSS feeds, Wikis etc. Use of Mashups for Cheminformatics Grid workflows Moving from Portlets to Gadgets in portals (or at least

supporting both) Use of Connotea to produce tagged document

collections such as http://www.connotea.org/user/crmcfor parallel computing

Semantic Research Grid integrates multiple taggingand search systems and copes with overlappinginconsistent annotations

MSI-CIEC portal augments Connotea to tag a mix ofURL and URI’s e.g. NSF TeraGrid use, PI’s andProposals• Hopes to support collaboration (for Minority Serving

Institution faculty)

2222

Use blog tocreate posts.

Display blog RSSfeed in MediaWiki.

2323

Semantic Research Grid (SRG) Architecture

10/22/07 23

2424

MSI-CIEC Portal

MSI-CIECMinority Serving Institution CyberInfrastructure Empowerment Coalition

25252525

Mashups v Workflow? Mashup Tools are reviewed at

http://blogs.zdnet.com/Hinchcliffe/?p=63 Workflow Tools are reviewed by Gannon and Fox

http://grids.ucs.indiana.edu/ptliupages/publications/Workflow-overview.pdf Both include scripting

in PHP, Python, sh etc.as both implementdistributedprogramming at levelof services

Mashups use all typesof service interfacesand perhaps do nothave the potentialrobustness (security) ofGrid service approach

Mashups typically“pure” HTTP (REST)

26262626

Grid Workflow Datamining in Earth Science Work with Scripps Institute Grid services controlled by workflow process real time

data from ~70 GPS Sensors in Southern California

Streaming DataSupport

TransformationsData Checking

Hidden MarkovDatamining (JPL)

Display (GIS)

NASA GPS

Earthquake

Real Time

Archival

2727

Grid Workflow Data Assimilation in Earth Science Grid services triggered by abnormal events and controlled by workflow process real

time data from radar and high resolution simulations for tornado forecasts

Typicalgraphicalinterface toservicecomposition

28282828

Web 2.0 uses all types of Services Here a Gadget Mashup uses a 3 service workflow with

a JavaScript Gadget Client

2929

Web 2.0 Mashupsand APIs

http://www.programmableweb.com/apis has (Sept 122007) 2312 Mashups and511 Web 2.0 APIs and withGoogleMaps the most oftenused in Mashups

The Web 2.0 UDDI (serviceregistry)

3030

The List of Web2.0 API’s

Each site has API andits features

Divided into broadcategories

Only a few used a lot(49 API’s used in 10or more mashups)

RSS feed of new APIs Amazon S3 growing

in popularity

3131

Now to Portals3131

Grid-style portal as used in Earthquake GridThe Portal is built from portlets

– providing user interfacefragments for each servicethat are composed into thefull interface – uses OGCEtechnology as does planetaryscience VLAB portal withUniversity of Minnesota

32323232

Portlets v. Google Gadgets Portals for Grid Systems are built using portlets with

software like GridSphere integrating these on theserver-side into a single web-page

Google (at least) offers the Google sidebar and Googlehome page which support Web 2.0 services and do notuse a server side aggregator

Google is more user friendly! The many Web 2.0 competitions is an interesting model

for promoting development in the world-widedistributed collection of Web 2.0 developers

I guess Web 2.0 model will win!

Note the many competitions powering Web 2.0 Mashup Development

Typical Google Gadget Structure

… Lots of HTML and JavaScript </Content> </Module>Portlets build User Interfaces by combining fragments in a standalone Java ServerGoogle Gadgets build User Interfaces by combining fragments with JavaScript on the client

Google Gadgets are an example ofStart Page technologySee http://blogs.zdnet.com/Hinchcliffe/?p=8

3434

Web 2.0 v Narrow Grid I Web 2.0 and Grids are addressing a similar application class

although Web 2.0 has focused on user interactions• So technology has similar requirements

Web 2.0 chooses simplicity (REST rather than SOAP) to lowerbarrier to everyone participating

Web 2.0 and Parallel Computing tend to use traditional (possiblyvisual) (scripting) languages for equivalent of workflow whereasGrids use visual interface backend recorded in BPEL

Web 2.0 and Grids both use SOA Service Oriented Architectures “System of Systems”: Grids and Web 2.0 are likely to build

systems hierarchically out of smaller systems• We need to support Grids of Grids, Webs of Grids, Grids of

Services etc. i.e. systems of systems of all sorts

3434

Web 2.0 v Narrow Grid II Web 2.0 has a set of major services like GoogleMaps or Flickr

but the world is composing Mashups that make new compositeservices• End-point standards are set by end-point owners• Many different protocols covering a variety of de-facto standards

Narrow Grids have a set of major software systems like Condorand Globus and a different world is extending with customservices and linking with workflow

Popular Web 2.0 technologies are PHP, JavaScript, JSON,AJAX and REST with “Start Page” e.g. (Google Gadgets)interfaces

Popular Narrow Grid technologies are Apache Axis, BPELWSDL and SOAP with portlet interfaces

Robustness of Grids demanded by the Enterprise? Not so clear that Web 2.0 won’t eventually dominate other

application areas and with Enterprise 2.0 it’s invading GridsThe world does itself in large numbers!

3636

Web 2.0 v Narrow Grid III Narrow Grids have a strong emphasis on standards and

structure; Web 2.0 lets a 1000 flowers (protocols) and a milliondevelopers bloom and focuses on functionality, broad usabilityand simplicity• Semantic Web/Grid has structure to allow reasoning• Annotation in sites like del.icio.us and uploading to

MySpace/YouTube is unstructured and free text searchreplaces structured ontologies

Portals are likely to feature both Web and “desktop client” technologyalthough it is possible that Web approach will be adopted more or lessuniformly

Web 2.0 has a very active portal activity which has similar architecture toGrids• A page has multiple user interface fragments

Web 2.0 user interface integration is typically Client side using GadgetsAJAX and JavaScript while• Grids are in a special JSR168 portal server side using Portlets WSRP and

Java3636

3737

The Ten areas covered by the 60 core WS-*Specifications

WSRP (Remote Portlets)10: Portals and User Interfaces

WS-Policy, WS-Agreement9: Policy and Agreements

WSDM, WS-Management, WS-Transfer8: Management

WSRF, WS-MetadataExchange, WS-Context7: System Metadata and State

UDDI, WS-Discovery6: Service Discovery

WS-Security, WS-Trust, WS-Federation, SAML,WS-SecureConversation

5: Security

BPEL, WS-Choreography, WS-Coordination4: Workflow and Transactions

WS-Notification, WS-Eventing (Publish-Subscribe)

3: Notification

WS-Addressing, WS-MessageDelivery; ReliableMessaging WSRM; Efficient Messaging MOTM

2: Service Internet

XML, WSDL, SOAP1: Core Service ModelTypical Grid/Web Service ExamplesWS-* Specification Area

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WS-* Areas and Web 2.0

Start Pages, AJAX and Widgets(Netvibes) Gadgets10: Portals and User Interfaces

Service dependent. Processed by application9: Policy and Agreements

WS-Transfer style Protocols GET PUT etc.8: Management==Interaction

Processed by application – no system state –Microformats are a universal metadata approach

7: System Metadata and State

http://www.programmableweb.com6: Service Discovery

SSL, HTTP Authentication/Authorization,OpenID is Web 2.0 Single Sign on

5: Security

Mashups, Google MapReduceScripting with PHP JavaScript ….

4: Workflow and Transactions(no Transactions in Web 2.0)

Hard with HTTP without polling– JMS perhaps?3: NotificationNo special QoS. Use JMS or equivalent?2: Service Internet

XML becomes optional but still usefulSOAP becomes JSON RSS ATOMWSDL becomes REST with API as GET PUT etc.Axis becomes XmlHttpRequest

1: Core Service Model

Web 2.0 ApproachWS-* Specification Area

3939

Too much Computing? Historically one has tried to increase computing capabilities by

• Optimizing performance of codes• Exploiting all possible CPU’s such as Graphics co-processors and “idle

cycles”• Making central computers available such as NSF/DoE/DoD

supercomputer networks

Next Crisis in technology area will be the opposite problem –commodity chips will be 32-128way parallel in 5 years time andwe currently have no idea how to use them – especially on clients• Only 2 releases of standard software (e.g. Office) in this time span

Gaming and Generalized decision support (data mining) are twoobvious ways of using these cycles• Intel RMS analysis• Note even cell phones will be multicore

There is “Too much data” as well as “Too much computing” butunclear implications

4040

Intel’s Projection

41Pradeep K. Dubey, pradeep.dubey@intel.com

Tomorrow

What is …? What if …?Is it …?Recognition Mining Synthesis

Create a model instance

RMS: Recognition Mining Synthesis

Model-basedmultimodalrecognition

Find a modelinstanceModel

Real-time analytics ondynamic, unstructured,

multimodal datasets

Photo-realism andphysics-based

animation

TodayModel-less Real-time streaming and

transactions on static – structured datasets

Very limited realism

42Pradeep K. Dubey, pradeep.dubey@intel.com

What is a tumor? Is there a tumor here? What if the tumor progresses?

It is all about dealing efficiently with complex multimodal datasets

Recognition Mining Synthesis

Images courtesy: http://splweb.bwh.harvard.edu:8000/pages/images_movies.html

43Intel’s Application Stack

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Multicore SALSA at IU Service Aggregated Linked Sequential Activities

• http://www.infomall.org/multicore Aims to link parallel and distributed (Grid) computing

by developing parallel applications as services and notas programs or libraries• Improve traditionally poor parallel programming

development environments Can use messaging to link parallel and Grid services

but performance – functionality tradeoffs different• Parallelism needs few µs latency for message latency and

thread spawning• Network overheads in Grid 10-100’s µs

Developing Service (library) of multicore parallel datamining algorithms

4545

Microsoft CCR for Parallelism• Use Microsoft CCR/DSS where DSS is mash-up/workflow service

model built from CCR and CCR supports MPI or Dynamic threads• CCR Supports exchange of messages between threads using named

ports• FromHandler: Spawn threads without reading ports• Receive: Each handler reads one item from a single port• MultipleItemReceive: Each handler reads a prescribed number of

items of a given type from a given port. Note items in a port can begeneral structures but all must have same type.

• MultiplePortReceive: Each handler reads a one item of a given typefrom multiple ports.

• JoinedReceive: Each handler reads one item from each of two ports.The items can be of different type.

• Choice: Execute a choice of two or more port-handler pairings• Interleave: Consists of a set of arbiters (port -- handler pairs) of 3

types that are Concurrent, Exclusive or Teardown (called at end forclean up). Concurrent arbiters are run concurrently but exclusivehandlers are

• http://msdn.microsoft.com/robotics/

46464646

DSS "Get" (loop 1 to 10000; two services on one node)

0

50

100

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200

250

300

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1 10 100 1000 10000

Round trips

Avera

ge r

un

tim

e (

mic

roseco

nd

s)

Series1

Timing of HP Opteron Multicore as a function of number of simultaneous two-way service messages processed (November 2006 DSS Release)

Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better

DSS Service Measurements

474725.84ThreadCCRXPIntel4 (4 core 2.8 Ghz)

16.34ThreadCCRXP39.34ProcessMPICH299.44ProcessmpiJava1524ProcessMPJERedhat1854ProcessMPJEXPAMD4

(4 core 2.19 Ghz)

20.28ThreadCCR (C#)Vista1008ProcessmpiJavaFedora1428ProcessMPJEFedora

1708ProcessMPJEVistaIntel8b(8 core 2.66 Ghz)

64.28ProcessMPICH21118ProcessmpiJava1578ProcessMPJEFedoraIntel8c:gf20

(8 core 2.33 Ghz)

4.218ProcessNemesis39.38ProcessMPICH2: Fast40.08ProcessMPICH2 (C)

1818ProcessMPJE (Java)RedhatIntel8c:gf12(8 core 2.33 Ghz)(in 2 chips)

MPI ExchangeLatency

ParallelismGrainsRuntimeOSMachine

MPI Exchange Latency in µs (20-30 µs computation between messaging)

4848

Clustering algorithm annealing by decreasing distance scale and gradually finds moreclusters as resolution improvedHere we see 10 increasing to 30 as algorithm progresses

PC07Intro gcf@indiana.edu 49

Parallel Multicore Clustering(C# on Windows)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.5 1 1.5 2 2.5 3 3.5 4

Parallel Overheadon 8 Threads running on Intel 8 core

Speedup = 8/(1+Overhead)

10000/(Grain Size n = points per core)

Overhead = Constant1 + Constant2/n

Constant1 = 0.05 to 0.1 (Client Windows) due to threadruntime fluctuations

10 Clusters

20 Clusters

5050

We use DSS as Service Framework as Integratedwith CCR Supporting MPI/Threading

PC07Intro gcf@indiana.edu 51

Intel 8-core C# with 80 Clusters: Vista RunTime Fluctuations for Clustering Kernel

• 2 Quadcore Processors• This is average of standard deviation of run time of the 8 threads

between messaging synchronization points80 Cluster(ratio of std to time vs #thread)

0

0.05

0.1

0 1 2 3 4 5 6 7 8

thread

std

/ t

ime

10,000 Datpts

50,000 Datapts

500,000 Datapts

Number of Threads

Standard Deviation/Run Time

PC07Intro gcf@indiana.edu 52

80 Cluster(ratio of std to time vs #thread)

0

0.002

0.004

0.006

1 2 3 4 5 6 7 8

10,000 Datapts

50,000 Datapts

500,000 Datapts

Intel 8 core with 80 Clusters: Redhat RunTime Fluctuations for Clustering Kernel

• This is average of standard deviation of run time of the8 threads between messaging synchronization points

Number of Threads

Standard Deviation/Run Time

5353

What should one do? i.e. How does one Cyberinfrastructure enable a given area/application XYZ As computing free, focus on identifying data/information/knowledge/wisdom

needed (there is probably too much data but not so much wisdom in DIKWpipeline)• Should we care just about “original data” or also about the whole pipeline DIKW?

Scope out supercomputer/computer services needed and exploit OGFstandards

Identify services (filters, often data mining) needed by XYZ?• Will we need parallel implementations of filters – if so use multicore compatible

frameworks Identify standards for application XYZ Set up distributed XYZ Services Use Web 2.0 (as it makes things easier) not current Grids (which makes

things harder)• Build a “Programmable XYZ Web”’• Emphasize Simplicity• Is “Secrecy” important and in fact viable? Often important but hard

What are synergies of XYZ to pervasive capabilities such as Web 2.0 sites,National resources like TeraGrid, and “Personal aides in an information richworld” (future of PC) ?