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Web 2.0, Grids and Parallel Computing

OGF WorkshopeScience 2007

December 10 2007

Geoffrey FoxCommunity Grids Laboratory, School of informatics

Indiana University

http://www.infomall.org/multicore gcf@indiana.edu, http://www.infomall.org

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Abstract of Web 2.0, Grids and Parallel Computing

We discuss the application of Web 2.0 to support scientific research (e-Science) and related e-moreorlessanything applications.

Web 2.0 offers interesting technical approaches to build the core e-infrastructure (Cyberinfrastructure) as well as a host of interesting services exemplified by Facebook, YouTube, Amazon S3/EC2 and Google maps.

We discuss why some of the original Grid goals of linking the world's computer systems may not be so relevant today and that interoperability is needed at the data and not always at the infrastructure level.

Web 2.0 may also support Parallel Programming 2.0 -- a better parallel computing software environment motivated by the need to run commodity applications on multicore chips.

Applications, Infrastructure, Technologies

This field is confused by inconsistent use of terminology; I define Web Services, Grids and (aspects of) Web 2.0 (Enterprise 2.0) are

technologies Grids could be everything (Broad Grids implementing some sort

of managed web) or reserved for specific architectures like OGSA or Web Services (Narrow Grids)

These technologies combine and compete to build electronic infrastructures termed e-infrastructure or Cyberinfrastructure

e-moreorlessanything is an emerging application area of broad importance that is hosted on the infrastructures e-infrastructure or Cyberinfrastructure

e-Science or perhaps better e-Research is a special case of e-moreorlessanything

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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-IndiaResearch and e-Outsourcing ….

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

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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

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 four classes

• Simulations• Data access, storage, federation, discovery• Filters for data mining and manipulation• General capabilities like collaboration, security etc.

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

Relevance of Web 2.0 They say that Web 1.0 was a read-only Web while Web

2.0 is the wildly read-write collaborative Web Web 2.0 can help e-Science in many ways Its tools can enhance scientific collaboration, i.e.

effectively support virtual organizations, in different ways from grids

The popularity of Web 2.0 can provide high quality technologies and software that (due to large commercial investment) can be very useful in e-Science and preferable to Grid or Web Service solutions

The usability and participatory nature of Web 2.0 can bring science and its informatics to a broader audience

Web 2.0 can even help the emerging challenge of using multicore chips i.e. in improving parallel computing programming and runtime environments

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“Best Web 2.0 Sites” -- 2006 Extracted from http://web2.wsj2.com/ All important capabilities for e-Science Social Networking

Start Pages

Social Bookmarking

Peer Production News

Social Media Sharing

Online Storage (Computing)

Web 2.0, Grids 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 but still small 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

Web 2.0 Systems like Grids have Portals, Services, Resources

Captures the incredible development of interactive Web sites enabling people to create and collaborate

Web 2.0, Grids 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 Web 2.0 standards like Google Maps and

powerful suppliers like Google/Microsoft which “define the architectures/interfaces”

One can easily combine SOAP (Web Service) based services/systems with HTTP messages but dominance of “lowest common denominator” suggests additional structure/complexity of SOAP will not easily survive

Distribution of APIs and Mashups per Protocol

REST SOAP XML-RPC REST,XML-RPC

REST,XML-RPC,

SOAP

REST,SOAP

JS Other

google google mapsmaps

netvibesnetvibes

live.comlive.com

virtual virtual earthearth

google google searchsearch

amazon S3amazon S3

amazon amazon ECSECS

flickrflickrebayebay

youtubeyoutube

411sync411syncdel.icio.usdel.icio.us

yahoo! searchyahoo! searchyahoo! geocodingyahoo! geocoding

technoratitechnorati

yahoo! imagesyahoo! imagestrynttrynt

yahoo! localyahoo! local

Number ofMashups

Number ofAPIs

SOAP is quite a small fraction

Too much Computing? Historically both grids and parallel computing have tried to

increase computing capabilities by• Optimizing performance of codes at cost of re-usability• Exploiting all possible CPU’s such as Graphics co-

processors and “idle cycles” (across administrative domains)

• Linking central computers together such as NSF/DoE/DoD supercomputer networks without clear user requirements

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 on commodity systems – especially on clients• Only 2 releases of standard software (e.g. Office) in this

time span so need solutions that can be implemented in next 3-5 years

Intel RMS analysis: Gaming and Generalized decision support (data mining) are ways of using these cycles

Intel’s Projection

Too much Data to the Rescue? Multicore servers have clear “universal parallelism” as many

users can access and use machines simultaneously Maybe also need application parallelism (e.g. datamining) as

needed on client machines Over next years, we will be submerged of course in data

deluge• Scientific observations for e-Science• Local (video, environmental) sensors• Data fetched from Internet defining users interests

Maybe data-mining of this “too much data” will use up the “too much computing” both for science and commodity PC’s• PC will use this data(-mining) to be intelligent user

assistant?• Must have highly parallel algorithms

Where did Narrow Grids and Web Services go wrong? Interoperability Interfaces will be for data not for

infrastructure• Google, Amazon, TeraGrid, European Grids will not

interoperate at the resource or compute (processing) level but rather at the data streams flowing in and out of independent Grid clouds

• Data focus is consistent with Semantic Grid/Web but not clear if latter has learnt the usability message of Web 2.0

Lack of detailed standards in Web 2.0 preferable to industry who can get proprietary advantage inside their clouds

One needs to share computing, data, people in e-moreorlessanything, Grids initially focused on computing but data and people are more important

eScience is healthy as is e-moreorlessanything Most Grids are solving wrong problem at wrong point in stack

with a complexity that makes friendly usability difficult

Information System 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:• “Original data”• Transformations or filters implementing DIKW (Data Information

Knowledge Wisdom) lattice • Some filters could correspond to large simulations• Final “Decision Support” step converting wisdom into action• Generic services such as security, profiles etc.

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

e-moreorlessanything Interoperability at DIKW interface not at details of computing and repository resources

Database

SS

SS

SS

SS

SS

SS

SS

FS

FS

FS

FS

FS

FS

FS

FS

FS

FSFS

FS

FS

FS

FS

FS

FS FS

FS

FS

PortalFS

Filter ServiceData in

Data out

Sensor or DataInterchange

Service

AnotherGrid

Raw Data Data Information Knowledge Wisdom

Decisions

SS

SS

AnotherService

SSAnother

Grid SS

AnotherGrid

SS

SS

SS

SS

SS

SS

SS

SS

FS

Inter-Service Messages

FS Filter Service

StorageCloud

ComputeCloud

SS

SS

SS

SS

Superior (from broad usage) technologies of Web 2.0

Mash-ups can replace Workflow

Gadgets can replace Portlets

UDDI replaced by user generated registries

2020

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)

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Grid Workflow Datamining in Earth Science Work with Scripps Institute Grid services controlled by scripting 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

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

Taverna another well known Grid/Web Service workflow tool

Recent Web 2.0 visual Mashup tools include Yahoo Pipes and Microsoft Popfly

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

This is the Web 2.0 UDDI (service registry)

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 Google maps

dominates but Amazon S3 growing in popularity

Now to Portals2525

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

QuakeSim has a typical Grid technology portal

Such Server side Portlet-based approaches to portals are being challenged by client side gadgets from Web 2.0

Portlets aggregated on server using Java analogous to JSP, JSF

Gadgets aggregated on client using Javascript analogous to “classic” DHTML

Mashups can still be totally server side like workflow

Note Web 2.0 more than a user interface

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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 and Gadget 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 (Web 2.0 term for portals) technologySee http://blogs.zdnet.com/Hinchcliffe/?p=8

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)

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

Web 2.0 can also help address long standing difficulties with

parallel programming environments

Too much computing addresses too much data andimplies need for multicore datamining algorithms

ClusteringPrincipal Component Analysis (SVD)

Expectation-Maximization EM (mixture models)Hidden Markov Models HMM

Multicore SALSA at CGL Service Aggregated Linked Sequential Activities 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 Developing set of services (library) of multicore parallel data

mining algorithms Looking at Intel list of algorithms (and all previous experience),

we find there are two styles of “micro” parallelism• Dynamic search as in integer programming, Hidden Markov Methods

(and computer chess); irregular synchronization with dynamic threads

• “MPI Style” i.e. several threads running typically in SPMD (Single Program Multiple Data); collective synchronization of all threads together

Most Intel RMS are “MPI Style” and very close to scientific algorithms even if applications are not science

Scalable Parallel Components There are no agreed high-level programming environments for

building library members that are broadly applicable. However lower level approaches where experts define

parallelism explicitly are available and have clear performance models.

These include MPI for messaging or just locks within a single shared memory.

There are several patterns to support here including the collective synchronization of MPI, dynamic irregular thread parallelism needed in search algorithms, and more specialized cases like discrete event simulation.

We use Microsoft CCR http://msdn.microsoft.com/robotics/ as it supports both MPI and dynamic threading style of parallelism

There is MPI style messaging and .. OpenMP annotation or Automatic Parallelism of existing

software is practical way to use those pesky cores with existing code• As parallelism is typically not expressed precisely, one needs luck to get

good performance• Remember writing in Fortran, C, C#, Java … throws away information

about parallelism HPCS Languages should be able to properly express parallelism

but we do not know how efficient and reliable compilers will be• High Performance Fortran failed as language expressed a subset of

parallelism and compilers did not give predictable performance PGAS (Partitioned Global Address Space) like UPC, Co-array

Fortran, Titanium, HPJava• One decomposes application into parts and writes the code for each

component but use some form of global index • Compiler generates synchronization and messaging• PGAS approach should work but has never been widely used – presumably

because compilers not mature

Summary of micro-parallelism On new applications, use MPI/locks with explicit

user decomposition A subset of applications can use “data parallel”

compilers which follow in HPF footsteps• Graphics Chips and Cell processor motivate such

special compilers but not clear how many applications can be done this way

OpenMP and/or Compiler-based Automatic Parallelism for existing codes in conventional languages

Composition of Parallel Components The composition (macro-parallelism) step has many excellent solutions

as this does not have the same drastic synchronization and correctness constraints as one has for scalable kernels• Unlike micro-parallelism step which has no very good solutions

Task parallelism in languages such as C++, C#, Java and Fortran90; General scripting languages like PHP Perl Python Domain specific environments like Matlab and Mathematica Functional Languages like MapReduce, F# HeNCE, AVS and Khoros from the past and CCA from DoE Web Service/Grid Workflow like Taverna, Kepler, InforSense KDE,

Pipeline Pilot (from SciTegic) and the LEAD environment built at Indiana University.

Web solutions like Mash-ups and DSS Many scientific applications use MPI for the coarse grain composition

as well as fine grain parallelism but this doesn’t seem elegant The new languages from Darpa’s HPCS program support task

parallelism (composition of parallel components) decoupling composition and scalable parallelism will remain popular and must be supported.

“Service Aggregation” in SALSA Kernels and Composition must be supported both inside

chips (the multicore problem) and between machines in clusters (the traditional parallel computing problem) or Grids.

The scalable parallelism (kernel) problem is typically only interesting on true parallel computers as the algorithms require low communication latency.

However composition is similar in both parallel and distributed scenarios and it seems useful to allow the use of Grid and Web composition tools for the parallel problem. • This should allow parallel computing to exploit large

investment in service programming environments Thus in SALSA we express parallel kernels not as traditional

libraries but as (some variant of) services so they can be used by non expert programmers

For parallelism expressed in CCR, DSS represents the natural service (composition) model.

Parallel Programming 2.0 Web 2.0 Mashups will (by definition the largest market)

drive composition tools for Grid, web and parallel programming

Parallel Programming 2.0 will build on Mashup tools like Yahoo Pipes and Microsoft Popfly

Yahoo Pipes

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.)

Need to make all this parallel

Clustering Data Cheminformatics was tested successfully with small datasets and

compared to commercial tools Cluster on properties of chemicals from high throughput

screening results to chemical properties (structure, molecular weight etc.)

Applying to PubChem (and commercial databases) that have 6-20 million compounds• Comparing traditional fingerprint (binary properties) with real-valued

properties GIS uses publicly available Census data; in particular the 2000

Census aggregated in 200,000 Census Blocks covering Indiana• 100MB of data

Initial clustering done on simple attributes given in this data• Total population and number of Asian, Hispanic and Renters

Working with POLIS Center at Indianapolis on clustering of SAVI (Social Assets and Vulnerabilities Indicators) attributes at http://www.savi.org) for community and decision makers• Economy, Loans, Crime, Religion etc.

Where are we? We have deterministically annealed clustering running well on 8-

core (2-processor quad core) Intel systems using C# and Microsoft Robotics Studio CCR/DSS

Could also run on multicore-based parallel machines but didn’t do this (is there a large Windows quad core cluster on TeraGrid?)• This would also be efficient on large problems

Applied to Geographical Information Systems (GIS) and census data• Could be an interesting application on future broadly deployed PC’s• Visualize nicely on Google Maps (and presumably Microsoft Virtual Earth)

Applied to several Cheminformatics problems and have parallel efficiency but visualization harder as in 150-1024 (or more) dimensions

Will develop a family of such parallel annealing data-mining tools where basic approach known for• Clustering• Gaussian Mixtures (Expectation Maximization)• and possibly Hidden Markov Methods

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Microsoft 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/

Preliminary Results• Parallel Deterministic Annealing Clustering in

C# with speed-up of 7 on Intel 2 quadcore systems

• Analysis of performance of Java, C, C# in MPI and dynamic threading with XP, Vista, Windows Server, Fedora, Redhat on Intel/AMD systems

• Study of cache effects coming with MPI thread-based parallelism

• Study of execution time fluctuations in Windows (limiting speed-up to 7 not 8!)

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

SALSA Performance

The macroscopic inter-service DSS Overhead is about 35µs

DSS is composed from CCR threads that have4µs overhead for spawning threads in dynamic search applications20µs overhead for MPI Exchange

Parallel MulticoreDeterministic Annealing Clustering

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 Intel 8b

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

Renters

Total

Asian

Hispanic

Renters

IUB

Purdue

10 Clusters

Total

Asian

Hispanic

Renters

30 Clusters

Clustering is typical of data mining methods that are needed for tomorrow’s clients or servers bathed in a data rich environment

Clustering Census data in Indiana on dual quadcore processorsImplemented with CCR and DSS

Use deterministic annealing that uses multiscale method to avoid local minima

Efficiency is 90% limited by peculiar Windows thread scheduling effects

Parallel Multicore Deterministic Annealing Clustering

0.000

0.050

0.100

0.150

0.200

0.250

0 5 10 15 20 25 30 35

#cluster

over

head

“Constant1”

Increasing number of clusters decreases communication/memory bandwidth overheads

Parallel Overhead for large (2M points) Indiana Census clustering on 8 Threads Intel 8bThis fluctuating overhead due to 5-10% runtime fluctuations between threads

Parallel Multicore Deterministic Annealing Clustering

“Constant1”

Increasing number of clusters decreases communication/memory bandwidth overheads

Parallel Overhead for subset of PubChem clustering on 8 Threads (Intel 8b)

The fluctuating overhead is reduced to 2% (as bits not doubles)40,000 points with 1052 binary properties (Census is 2 real valued properties)

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

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

Looking to the Future Web 2.0 has momentum as it is driven by success of social web

sites and the user friendly protocols attracting many developers of mashups

Grids momentum driven by the success of eScience and the commercial web service thrusts largely aimed at Enterprise

We expect applications such as business and military where predictability and robustness important might be built on a Web Service (Narrow Grid) core with perhaps Web 2.0 functionality enhancements• But even this Web Service application may not survive

Multicore usability driving Parallel Programming 2.0 Simplicity, supporting many developers are forces pressuring

Grids! Robustness and coping with unstructured blooming of a 1000

flowers are forces pressuring Web 2.0 Need work on Grid Cloud Data Interchange standards and

multicore programming