11

Cyberinfrastructure Technologies and Applications

Summit on Cyberinfrastructure: Innovation At Work Banff 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.’ from its inventor John Taylor Director General of Research Councils UK, Office of Science and Technology

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

Similarly e-Business captures an emerging view of corporations as dynamic virtual organizations linking employees, customers and stakeholders 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 be managed and understood.

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

On demand assignment of experts, computers, networks and storage 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 Grid technology) 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 large simulations, data analysis etc.; must decompose problem

Distributed aspect integrates already distinct components – especially natural for data

Cyberinfrastructure is in general a distributed collection of parallel systems

Cyberinfrastructure is made of services (originally Web services) that are “just” programs or data sources packaged for distributed access

44

Underpinnings of Cyberinfrastructure

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

The emerging distributed system picture is of distributed services with advertised interfaces but opaque implementations communicating 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 to achieve new capabilities

As well as Internet/Communication revolutions (distributed systems), multicore chips will likely be hugely important (parallel systems)

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 Service Definition Language) to define interoperable interfaces but Web 2.0 follows old library practice: one just specifies interface

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

Services should be loosely coupled which normally means they are coarse grain

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

Software Engineering and Interoperability/Standards are closely related

SDSC

TACC

UC/ANL

NCSA

ORNL

PU

IU

PSC

NCAR

Caltech

USC/ISI

UNC/RENCI

UW

Resource Provider (RP)

Software Integration Partner

Grid Infrastructure Group (UChicago)

Computing and Cyberinfrastructure: TeraGrid

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

77

Data and Cyberinfrastructure DIKW: Data Information Knowledge Wisdom

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

outsourcing), Military Command and Control and general decision support

(SOAP or just RSS) messages transport information expressed in a semantically rich fashion between sources and services that enhance and transform information so that complete system provides• 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 technology progress are dubious

8

Database

SS

SS

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SS

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SS

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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

OS

OS

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 Cyberinfrastructure Architecture

The Party Line approach to Information Infrastructure is clear – one creates a Cyberinfrastructure consisting of distributed services 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

CYBERINFRASTRUCTURE CENTER FOR POLAR SCIENCE (CICPS)

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 with multiple 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 Cyberinfrastructure Collaboratory 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 byFingerprints Regression CIDSimilarity Classification SMARTSDescriptors Clustering 3D Similarity2D diagrams Sampling distributionsFile format conversion

Docking scores/poses byApplications Applications CID

Docking Predictive models SMARTSFiltering Feature selection Protein

2D plots Docking scoresToxicity predictions

Anti-cancer activity predictionsCID, SMARTS

Cheminformatics Services

DruglikenessArbitrary R code (PkCell)

Mutagenecity predictionsPubChem related data by

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

14

Process Chemistry-Biology Interaction Data from HTS (High Throughput Screening)

Percent Inhibition or IC50 data is retrieved from HTS

Question: Was this screen successful?

Question: What should the active/inactive cutoffs be?

Question: What can we learn about the target protein or cell line from this screen?

Compound data submitted to PubChem

Workflows encoding distribution analysis of screening results

Grids can link data analysis ( e.g image processing developed in existing Grids), traditional Chem-informatics tools, as well as annotation tools (Semantic Web, del.icio.us) and enhance lead ID and SAR analysis

A Grid of Grids linking collections of services atPubChemECCR centersMLSCN centers

Workflows encoding plate & control well statistics, distribution analysis, etc

Workflows encoding statistical comparison of results to similar screens, docking of compounds into proteins to correlate binding, with activity, literature search of active compounds, etcCHEMINFORMATICSPROCESS GRIDS

Scientists at IU prefer Web 2.0 to Grid/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 Web Services

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

Kazaa, Instant Messengers, Skype, Napster, BitTorrent for P2P Collaboration – text, audio-video conferencing, files

del.icio.us, Connotea, Citeulike, Bibsonomy, Biolicious manage shared bookmarks

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

Writely, Wikis and Blogs are powerful specialized shared document systems

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

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“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)

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

Web sites enabling people to create and collaborate

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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 robust managed environment with growing adoption in Enterprise systems and distributed science (so called e-Science)

Web 2.0 supports a similar architecture to Web services but has developed in a more chaotic but remarkably successful fashion with a service architecture with a variety of protocols including those 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 Google Maps and Amazon Compute/Storage services of clear general relevance

There are also Web 2.0 services supporting novel collaboration modes and user interaction with the web as seen in social networking 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 poor adoption 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 “lowest common denominator” suggests additional structure/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 (Enterprise 2.0) are technologies

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

most important client and server applications of the future will be internet enhanced/enabled so key aspect of multicore is its role and integration in e-infrastructure

e-moreorlessanything is an emerging application area of broad importance that is hosted on the infrastructures e-infrastructure or 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/crmc for parallel computing

Semantic Research Grid integrates multiple tagging and search systems and copes with overlapping inconsistent annotations

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

Institution faculty)

2222

Use blog to create posts.

Display blog RSS feed in MediaWiki.

2323

Semantic Research Grid (SRG) Architecture

04/21/2323

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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 implement distributed programming at level of services

Mashups use all types of service interfaces and perhaps do not have the potential robustness (security) of Grid 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

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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

Typical graphical interface to service composition

28282828

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

a JavaScript Gadget Client

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Web 2.0 Mashups and APIs

http://www.programmableweb.com/apis has (Sept 12 2007) 2312 Mashups and 511 Web 2.0 APIs and with GoogleMaps the most often used in Mashups

The Web 2.0 UDDI (service registry)

3030

The List of Web 2.0 API’s

Each site has API and its features

Divided into broad categories

Only a few used a lot (49 API’s used in 10 or 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 interface fragments for each service that are composed into the full interface – uses OGCE technology as does planetary science VLAB portal with University of Minnesota

32323232

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

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

Google (at least) offers the Google sidebar and Google home page which support Web 2.0 services and do not use 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-wide distributed 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 of Start 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 lower

barrier to everyone participating Web 2.0 and Parallel Computing tend to use traditional (possibly

visual) (scripting) languages for equivalent of workflow whereas Grids 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 composite services• 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 Condor and Globus and a different world is extending with custom services 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, BPEL WSDL 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 million developers bloom and focuses on functionality, broad usability and 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 search replaces structured ontologies

Portals are likely to feature both Web and “desktop client” technology although it is possible that Web approach will be adopted more or less uniformly

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

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

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

WS-* Specification Area Typical Grid/Web Service Examples

1: Core Service Model XML, WSDL, SOAP

2: Service Internet WS-Addressing, WS-MessageDelivery; Reliable Messaging WSRM; Efficient Messaging MOTM

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

4: Workflow and Transactions BPEL, WS-Choreography, WS-Coordination

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

6: Service Discovery UDDI, WS-Discovery

7: System Metadata and State WSRF, WS-MetadataExchange, WS-Context

8: Management WSDM, WS-Management, WS-Transfer

9: Policy and Agreements WS-Policy, WS-Agreement

10: Portals and User Interfaces WSRP (Remote Portlets)

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WS-* Areas and Web 2.0 WS-* Specification Area Web 2.0 Approach

1: Core Service Model XML becomes optional but still usefulSOAP becomes JSON RSS ATOM WSDL becomes REST with API as GET PUT etc.Axis becomes XmlHttpRequest

2: Service Internet No special QoS. Use JMS or equivalent?

3: Notification Hard with HTTP without polling– JMS perhaps?

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

Mashups, Google MapReduceScripting with PHP JavaScript ….

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

6: Service Discovery http://www.programmableweb.com

7: System Metadata and State Processed by application – no system state – Microformats are a universal metadata approach

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

9: Policy and Agreements Service dependent. Processed by application

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

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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 and we 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 two obvious 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” but unclear implications

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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 SynthesisRMS: Recognition Mining Synthesis

Model-basedmultimodalrecognition

Find a modelinstance

Model

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 datasetsIt is all about dealing efficiently with complex multimodal datasets

Recognition Mining Synthesis

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

43 Intel’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 not as 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 data mining 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 be general structures but all must have same type.

• MultiplePortReceive: Each handler reads a one item of a given type from 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 for clean up). Concurrent arbiters are run concurrently but exclusive handlers are

• http://msdn.microsoft.com/robotics/

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Round trips

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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

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

Machine OS Runtime Grains Parallelism MPI Exchange Latency

Intel8c:gf12

(8 core 2.33 Ghz)

(in 2 chips)

Redhat MPJE (Java) Process 8 181

MPICH2 (C) Process 8 40.0

MPICH2: Fast Process 8 39.3

Nemesis Process 8 4.21

Intel8c:gf20

(8 core 2.33 Ghz)

Fedora MPJE Process 8 157

mpiJava Process 8 111

MPICH2 Process 8 64.2

Intel8b

(8 core 2.66 Ghz)

Vista MPJE Process 8 170

Fedora MPJE Process 8 142

Fedora mpiJava Process 8 100

Vista CCR (C#) Thread 8 20.2

AMD4

(4 core 2.19 Ghz)

XP MPJE Process 4 185

Redhat MPJE Process 4 152

mpiJava Process 4 99.4

MPICH2 Process 4 39.3

XP CCR Thread 4 16.3

Intel4 (4 core 2.8 Ghz) XP CCR Thread 4 25.8

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Clustering algorithm annealing by decreasing distance scale and gradually finds more clusters 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

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We use DSS as Service Framework as Integrated with CCR Supporting MPI/Threading

PC07Intro gcf@indiana.edu 51

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

• 2 Quadcore Processors

• This is average of standard deviation of run time of the 8 threads between messaging synchronization points

Number of Threads

Standard Deviation/Run Time

PC07Intro gcf@indiana.edu 52

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

• This is average of standard deviation of run time of the 8 threads between messaging synchronization points

Number of Threads

Standard Deviation/Run Time

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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 DIKW pipeline)• Should we care just about “original data” or also about the whole pipeline DIKW?

Scope out supercomputer/computer services needed and exploit OGF standards

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 rich world” (future of PC) ?