Web 2 0, Grids and Parallel Computing

1

Web 2.0, Grids

and Parallel Computin

Oxford Universit December 07 2007

Geoffrey Fox

Community Grids Laboratory, School of informatics Indiana University

http://www.infomall.org/multicore

2

Abstract of Web 2.0,

Grids and Parallel Computing

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

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

n 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.

n Web 2.0 may also support Parallel Programming 2.0 -- a better

Applications, Infrastructure,

Technologies

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

technologies

n Grids could be everything (Broad Grids implementing some sort

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

n These technologies combine and compete to build electronic

infrastructures termed e-infrastructure or Cyberinfrastructure

n e-moreorlessanything is an emerging application area of broad

importance that is hosted on the infrastructures e-infrastructure

or Cyberinfrastructure

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

4

e-moreorlessanything

n ‘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

n e-Science is about developing tools and technologies that allow

scientists to do ‘faster, better or different’ research

n Similarly e-Business captures an emerging view of corporations as

dynamic virtual organizations linking employees, customers and stakeholders across the world.

n This generalizes to e-moreorlessanything including presumably

e-OxfordResearch and e-OxfordEducation ….

n A deluge of data of unprecedented and inevitable size must be

managed and understood.

n People (see Web 2.0), computers, data (including sensors and

instruments) must be linked.

n On demand assignment of experts, computers, networks and

5

What is Cyberinfrastructure

n Cyberinfrastructure is (from NSF) infrastructure that

supports distributed science (e-Science)– data, people, computers

• Clearly core concept more general than Science

n Exploits Internet technology (Web2.0) adding (via Grid

technology) management, security, supercomputers etc.

n It has two aspects: parallel – low latency (microseconds)

between nodes and distributed – highish latency (milliseconds) between nodes

n Parallel needed to get high performance on individual large

simulations, data analysis etc.; must decompose problem

n Distributed aspect integrates already distinct components –

especially natural for data

n Cyberinfrastructure is in general a distributed collection of

parallel systems

n Cyberinfrastructure is made of services (originally Web

Service or Web Service Approach

n One uses GML, CML etc. to define the data structure in a system and one

uses services to capture “methods” or “programs”

n 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.

n 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

n Service Interface (WSDL) establishes a “contract” independent of

implementation between two services or a service and a client

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

grain

n Services will be composed (linked together) by mashups (typically scripts) or

workflow (often XML – BPEL)

Relevance of Web 2.0

n

They say that Web

1.0

was a

read-only

Web while Web

2.0

is the wildly

read-write collaborative

Web

n

Web 2.0

can

help e-Science

in many ways

n

Its tools can enhance scientific collaboration, i.e.

effectively

support virtual organizations

, in different

ways from grids

n

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

n

The

usability

and

participatory

nature of Web 2.0 can

bring science and its informatics to a

broader audience

n

Web 2.0 can even help the emerging challenge of using

multicore

chips i.e. in improving

parallel computing

8

“Best Web 2.0 Sites” -- 2006

n Extracted from http://web2.wsj2.com/ n All important capabilities for e-Science n Social Networking

n Start Pages

n Social Bookmarkin

n Peer Production News

n Social Media Sharing

n Online Storage

Web 2.0, Grids and Web Services I

n 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.

n “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)

n 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

n Web 2.0 also has many well known capabilities with Google

Maps and Amazon Compute/Storage services of clear general relevance

n There are also Web 2.0 services supporting novel collaboration

Web 2.0 Systems like Grids have Portals, Services, Resources

n

Captures the incredible development of interactive

Web 2.0, Grids and Web Services II

n I once thought Web Services were inevitable but this is no longer

clear to me

n 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

n There are de facto Web 2.0 standards like Google Maps and

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

n One can easily combine SOAP (Web Service) based

Distribution of APIs and Mashups per

Protocol

REST SOAP XML-RPC REST,

XML-RPC XML-RPC,REST, SOAP

REST,

SOAP JS Other

google maps netvibes live.com virtual earth google search amazon S3 amazon ECS flickr ebay youtube 411syncdel.icio.us yahoo! search yahoo! geocoding technorati yahoo! images trynt yahoo! local Number of Mashups Number of APIs

Too much Computing?

n 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

n 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

n Intel RMS analysis: Gaming and Generalized decision

Too much Data to the Rescue?

n Multicore servers have clear “universal parallelism” as many

users can access and use machines simultaneously

n Maybe also need application parallelism (e.g. datamining) as

needed on client machines

n 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

n 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?

Where did Narrow Grids and Web Services go wrong?

n 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

n Lack of detailed standards in Web 2.0 preferable to industry

who can get proprietary advantage inside their clouds

n One needs to share computing, data, people in

e-moreorlessanything, Grids initially focused on computing but

data and people are more important

n eScience is healthy as is e-moreorlessanything

n Most Grids are solving wrong problem at wrong point in stack

Information System Architecture

n The Party Line approach to Information Infrastructure is clear

– one creates a Cyberinfrastructure consisting of distributed services accessed by portals/gadgets/gateways/RSS feeds

n 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.

n Infrastructure will be set up as a System of Systems (Grids of

Grids)

n Services and/or Grids just accept some form of DIKW and

produce another form of DIKW

• “Original data” has no explicit input; just output

n e-moreorlessanything Interoperability at DIKW interface not at

Database

S S

S

S SS

S

S S_S S_S S_S

F S F S F S F S F S F S F S F S _F S F

S _SF

F S F S F S F S F S F S F S F S F

S Portal

F S

Filter Servic Data in Data out

Sensor or Data Interchange

Service

Anothe Grid

Raw Data  Data  Information  Knowledge  Wisdom Decisions S S S S Anothe Service S S Anothe

Grid S S

Anothe Grid S S S S S S S S S S S S S S S S F S Inter-S ervi ce Messag es F

S Filter Service

Storag Cloud Comput

Cloud

S

S S_S S_S S

Some Web 2.0 Activities at IU

n

Use of

Blogs

, RSS feeds, Wikis etc.

n

Use of

Mashups

for Cheminformatics Grid workflows

n

Moving from

Portlets

to

Gadgets

in portals (or at least

supporting both)

n

Use of

Connotea

to produce tagged document collections

such as htt

p://www.connotea.org/user/crmc for

parallel

computing

n

Semantic Research Grid

integrates multiple tagging and

search systems and copes with overlapping inconsistent

annotations

n

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)

Use blog to create posts.

Semantic Research Grid (SRG)

n Integrates tagging and search system that allows users to use

multiple sites and consistently integrate them with traditional citation databases

n We built a mashup linking to del.icio.us, CiteULike, Connotea

allowing exchange of tags between sites and between local repositories

n Repositories also link to local sources (PubsOnline) and Google

Scholar (GS) and Windows Academic Live (WLA)

• GS has number of cited publications.

• WLA has Digital Object Identifier (DOI)

n We implement a rather more powerful access control mechanism n We build heuristic tools to mine “web lists” for citations

n We have an “event” based architecture (consistency model)

allowing change actions to be preserved and selectively changed

• Supports integrating different inconsistent views of a given document and

its updates on different tagging systems

Existing User Interface

Semantic Scholars Grid

Example

n Parallel

Computing Collection selected on

Cell Tag

n So far no clear

“winner” in tagging space

n Maybe

CiteUlike with different

metadata better

n How do I

preserve

del.icio.us Tags

Download to Local System

MSI-CIEC Portal

MSI-CIEC

NSF Grants Tag System

n

NSF has the ability to get information (in XML) on all of the

grants a particular person worked on

n

We downloaded, parsed, and bookmarked this info using a

little scavenger robot.

• Each grant is represented by a bookmark and tagged with

relevant information in MSI-CIEC Portal

• Grant tags point to URLs of the NSF award page.

n

The investigators

are imported as users

n

Each has a bookmark for each project they worked on

• They are also represented in the tags of these projects.

n

Can now

form research collaborations

by linking

researchers with common tags

n

Hopefully will enable

broader collaborations

and not

Superior (from broad usage)

technologies of Web 2.

Mash-ups can replace Workflo

Gadgets can replace Portlet

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Mashups v Workflow?

n Mashup Tools are reviewed at

http://blogs.zdnet.com/Hinchcliffe/?p=63

n Workflow Tools are reviewed by Gannon and Fox

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

n Both include scripting

in PHP, Python, sh etc. as both implement

distributed

programming at level of services

n Mashups use all types

of service interfaces and perhaps do not have the potential

robustness (security) of Grid service approach

n Mashups typically

29

Grid Workflow Datamining in Earth Science

n Work with Scripps Institute

n Grid services controlled by scripting workflow process real time data from ~70 GPS Sensors in Southern

California

Streaming Data Support

Transformations Data Checking

Hidden Marko Datamining (JPL)

Display (GIS)

NASA GPS

Earthquake

Grid Workflow Data Assimilation in Earth Science

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

Web 2.0 Mashups

and APIs

n

http://www.programmable

web.com/apis

has (Sept 12

2007) 2312 Mashups and

511

Web 2.0 APIs

and with

GoogleMaps the most often

used in Mashups

n

This is the

Web 2.0 UDDI

The List of

Web 2.0 API’s

n

Each site has API and

its features

n

Divided into broad

categories

n

Only a few used a lot

(

49 API’s

used in

10

or more

mashups

)

n

RSS feed of new APIs

n

Google maps

dominates but

Now to Portals

33

Grid-style portal as used in Earthquake Grid

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

34

Portlets v. Google Gadgets

n

Portals for Grid Systems are built using portlets with

software like GridSphere integrating these on the

server-side into a single web-page

n

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

n

Google is more user friendly!

n

The many Web 2.0 competitions is an interesting model

for promoting development in the world-wide

distributed collection of Web 2.0 developers

n

I guess Web 2.0 model will win!

Typical Google Gadget Structure

 … Lots of HTML and JavaScript </Content> </Module>

Portlets build User Interfaces by combining fragments in a standalone Java Server

Google 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) technolog

The Ten areas covered by the 60 core WS-*

Specifications

WSRP (Remote Portlets) 10: Portals and User

Interfaces

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

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

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

UDDI, WS-Discovery 6: Service Discovery

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

5: Security

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

Transactions

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

3: Notification

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

XML, WSDL, SOAP 1: Core Service Model

WS-* Areas and Web 2.0

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

Interfaces

Service dependent. Processed by application 9: 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.com 6: Service Discovery

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

5: Security

Mashups, Google MapReduce

Scripting with PHP JavaScript …. 4: Workflow and

Transactions (no

Transactions in Web 2.0)

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

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

XML becomes optional but still useful SOAP becomes JSON RSS ATOM

WSDL becomes REST with API as GET PUT etc. Axis becomes XmlHttpRequest

1: Core Service Model

Web 2.0

can also help

address

long standing difficulties

with

parallel programming

environments

Too much computing addresses too much data an implies need for multicore datamining algorithms

Clustering

Principal Component Analysis (SVD)

Expectation-Maximization EM (mixture models)

Multicore SALSA at CGL

n Service Aggregated Linked Sequential Activities

n 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

n Developing set of services (library) of multicore parallel data

mining algorithms

n 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

n Most Intel RMS are “MPI Style” and very close to scientific

Scalable Parallel Components

n There are no agreed high-level programming environments for

building library members that are broadly applicable.

n However lower level approaches where experts define

parallelism explicitly are available and have clear performance models.

n These include MPI for messaging or just locks within a single

shared memory.

n 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.

n We use Microsoft CC

There is MPI style messaging and ..

n 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

n 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

n 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

Summary of micro-parallelism

n

On

new applications

, use MPI/locks with explicit

user decomposition

n

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

n

OpenMP and/or Compiler-based Automatic

Composition of Parallel Components

n 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

n Task parallelism in languages such as C++, C#, Java and Fortran90;

n General scripting languages like PHP Perl Python

n Domain specific environments like Matlab and Mathematica

n Functional Languages like MapReduce, F#

n HeNCE, AVS and Khoros from the past and CCA from DoE

n Web Service/Grid Workflow like Taverna, Kepler, InforSense KDE,

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

n Web solutions like Mash-ups and DSS

n Many scientific applications use MPI for the coarse grain composition

as well as fine grain parallelism but this doesn’t seem elegant

n The new languages from Darpa’s HPCS program support task

parallelism (composition of parallel components) decoupling

“Service Aggregation” in

SALSA

n

Kernels and Composition must be supported both

inside

chips

(the multicore problem) and

between machines

in

clusters (the traditional parallel computing problem) or

Grids.

n

The scalable parallelism (kernel) problem is typically only

interesting on true parallel computers as the algorithms

require low communication latency.

n

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

n

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

n

For

parallelism expressed in CCR

,

DSS

represents the

Parallel Programming 2.0

n

Web 2.0 Mashups

will (by definition the largest

market) drive

composition tools

for Grid, web and

parallel programming

n

Parallel Programming 2.0

will build on Mashup tools

like Yahoo Pipes and Microsoft Popfly

CICC Chemical Informatics and Cyberinfrastructure Collaboratory Web Service Infrastructure

Portal Services

RSS Feeds User Profiles

Collaboration as in Sakai

Core Grid Services

Service Registry

Job Submission and Management

Local Clusters

IU Big Red, TeraGrid, Open Science Grid

Varuna.net

Quantum Chemistry

OSCAR Document Analysis InChI Generation/Search

Computational Chemistry (Gamess, Jaguar etc.)

Clustering Data

n Cheminformatics was tested successfully with small datasets and

compared to commercial tools

n Cluster on properties of chemicals from high throughput

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

n Applying to PubChem (and commercial databases) that have

6-20 million compounds

• Comparing traditional fingerprint (binary properties) with real-valued

properties

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

Census aggregated in 200,000 Census Blocks covering Indiana

• 100MB of data

n Initial clustering done on simple attributes given in this data

• Total population and number of Asian, Hispanic and Renters

n 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

Where are we?

n We have deterministically annealed clustering running well on

8-core (2-processor quad 8-core) Intel systems using C# and Microsoft Robotics Studio CCR/DSS

n 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

n 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)

n Applied to several Cheminformatics problems and have parallel

efficiency but visualization harder as in 150-1024 (or more) dimensions

n Will develop a family of such parallel annealing data-mining

tools where basic approach known for

• Clustering

49

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

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

25.8

4 Thread

CCR

XP Intel4(4 core 2.8 Ghz)

16.3 4 Thread CCR XP 39.3 4 Process MPICH2 99.4 4 Process mpiJava 152 4 Process MPJE Redhat 185 4 Process MPJE XP AMD4

(4 core 2.19 Ghz)

20.2 8 Thread CCR (C#) Vista 100 8 Process mpiJava Fedora 142 8 Process MPJE Fedora 170 8 Process MPJE Vista Intel8b

(8 core 2.66 Ghz)

64.2 8 Process MPICH2 111 8 Process mpiJava 157 8 Process MPJE Fedora Intel8c:gf20

(8 core 2.33 Ghz)

4.21 8 Process Nemesis 39.3 8 Process MPICH2: Fast 40.0 8 Process MPICH2 (C) 181 8 Process MPJE (Java) Redhat Intel8c:gf12

(8 core 2.33 Ghz) (in 2 chips)

MPI Exchange Latency Parallelism

Grains Runtime

OS Machine

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

SALSA Performance

The macroscopic inter-service DSS Overhead is about 35µs

DSS is composed from CCR threads that hav

4µs overhead for spawning threads in dynamic search applications

Parallel Multicor

Deterministic Annealing

Clustering

Parallel Overhea on 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 threa runtime fluctuations

10 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 processors

Implemented with CCR and DS

Use deterministic annealing that uses multiscale method to avoid local minima

Parallel Multicore

Deterministic Annealing

Clustering

“Constant1”

Increasing number of clusters decreases communication/memory bandwidth overheads

Parallel Overhead for large (2M points) Indiana Census clusterin on 8 Threads Intel 8

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

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

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

Looking to the Future

n 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

n Grids momentum driven by the success of eScience and the

commercial web service thrusts largely aimed at Enterprise

n 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

n Multicore usability driving Parallel Programming 2.0

n Simplicity, supporting many developers are forces pressuring

Grids!

n Robustness and coping with unstructured blooming of a 1000

flowers are forces pressuring Web 2.0

n Need work on Grid Cloud Data Interchange standards and