Transcript Web 2.0 - Community Grids Lab

Web 2.0, Grids
and Parallel Computing
OGF Workshop
eScience 2007
December 10 2007
Geoffrey Fox
Community Grids Laboratory, School of informatics
Indiana University
http://www.infomall.org/multicore
[email protected], http://www.infomall.org
1
Abstract of Web 2.0,
Grids and Parallel Computing
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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 einfrastructure (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
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run commodity applications on multicore chips.
Applications, Infrastructure,
Technologies
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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 emoreorlessanything
e-moreorlessanything
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‘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 eIndiaResearch 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
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Cyberinfrastructure is (from NSF) infrastructure that
supports distributed science (e-Science)– data, people,
computers
• Clearly core concept more general than Science
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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
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Service or Web Service Approach
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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
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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
“Best Web 2.0 Sites” -- 2006
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Extracted from http://web2.wsj2.com/
All important capabilities for e-Science
Social Networking
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Start Pages
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Social Bookmarking
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Peer Production News
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Social Media Sharing
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Online Storage
(Computing)
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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, metadata, discovery, notification etc.
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“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
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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
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Captures the incredible development of interactive Web
sites enabling people to create and collaborate
Web 2.0, Grids and Web Services II
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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
google
maps
Number of
APIs
Number of
Mashups
del.icio.us
411sync
yahoo! search
yahoo! geocoding
SOAP is quite a small fraction
virtual
earth
technorati
netvibes
yahoo! images
trynt
yahoo! local
amazon
ECS
google
search
flickr
SOAP
ebay
youtube
amazon S3
REST
live.com
XML-RPC
REST,
XML-RPC
REST,
XML-RPC,
SOAP
REST,
SOAP
JS
Other
Too much Computing?
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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 coprocessors 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?
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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 emoreorlessanything, 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
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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.
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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
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e-moreorlessanything Interoperability at DIKW interface not at
details of computing and repository resources
Raw Data 
Information 
 Wisdom
Knowledge
Another
Grid
Decisions
S
S
S
S
Another
Grid
Data 
S
S
S
S
FS
FS
SS
FS
SS
Another
Service
FS
FS
FS
SS
SS
FS
Filter Service
FS
F
S
FS
FS
FS
FS
F
S
SS
FS
FS
SS
Another
Grid
FS
SS
S
S
S
S
FS
S
S
Filter Service
Data in
Data out
FS
FS
FS
Compute
Cloud
Database
FS
FS
S
S
S
S
S
S
S
S
S
S
S
S
Storage
Cloud
S
S
Sensor or Data
Interchange
Service
Superior (from broad usage)
technologies of Web 2.0
Mash-ups can replace Workflow
Gadgets can replace Portlets
UDDI replaced by user generated
registries
Mashups v Workflow?
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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
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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
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NASA GPS
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Work with Scripps Institute
Grid services controlled by scripting workflow process
real time data from ~70 GPS Sensors in Southern
California
Earthquake
Streaming Data
Support
Archival
Transformations
Data Checking
Hidden Markov
Datamining (JPL)
Real Time
Display (GIS)
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Grid Workflow Data Assimilation in Earth Science
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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
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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
This is the Web 2.0 UDDI
(service registry)
The List of Web
2.0 API’s
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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
Grid-style portal as used in Earthquake Grid
The 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
QuakeSim has a typical Grid technology portal
science VLAB portal with
Such Server side Portlet-based approaches to portals are University
being challenged
by client
of Minnesota
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
Now to Portals
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Note the many competitions powering Web 2.0
Mashup and Gadget Development
Portlets v. Google Gadgets
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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!
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Typical Google Gadget Structure
Google Gadgets are an example of
Start Page (Web 2.0 term for portals)
technology
See http://blogs.zdnet.com/Hinchcliffe/?p=8
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… 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
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 (PublishSubscribe)
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 useful
SOAP 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 MapReduce
Scripting 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 and
implies need for multicore datamining algorithms
Clustering
Principal Component Analysis (SVD)
Expectation-Maximization EM (mixture models)
Hidden Markov Models HMM
Multicore SALSA at CGL
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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
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Most Intel RMS are “MPI Style” and very close to scientific
algorithms even if applications are not science
Scalable Parallel Components
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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 ..
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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
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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
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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
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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
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OpenMP and/or Compiler-based Automatic
Parallelism for existing codes in conventional
languages
Composition of Parallel Components
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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
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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
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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
Cheminformatics Services
Statistics Services
Database Services
Core functionality
Fingerprints
Similarity
Descriptors
2D diagrams
File format conversion
Computation functionality
Regression
Classification
Clustering
Sampling distributions
3D structures by
CID
SMARTS
3D Similarity
Docking scores/poses by
CID
SMARTS
Protein
Docking scores
Applications
Applications
Docking
Predictive models
Filtering
Feature selection
Druglikeness
2D plots
Toxicity predictions
Arbitrary R code (PkCell)
Mutagenecity predictions
PubChem related data by
Anti-cancer activity predictions
Need to make
Pharmacokinetic parameters
CID, SMARTS
all this parallel
OSCAR Document Analysis
InChI Generation/Search
Computational Chemistry (Gamess, Jaguar etc.)
Core Grid Services
Service Registry
Job Submission and Management
Local Clusters
IU Big Red, TeraGrid, Open Science Grid
Varuna.net
Quantum Chemistry
Portal Services
RSS Feeds
User Profiles
Collaboration as in Sakai
Clustering Data
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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 620 million compounds
• Comparing traditional fingerprint (binary properties) with real-valued
properties
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GIS uses publicly available Census data; in particular the 2000
Census aggregated in 200,000 Census Blocks covering Indiana
• 100MB of data
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Initial clustering done on simple attributes given in this data
• Total population and number of Asian, Hispanic and Renters
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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?
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We have deterministically annealed clustering running well on 8core (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
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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)
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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
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/
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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
Process
SALSANemesis
Performance
8
4.21
Intel8c:gf20
(8 core 2.33 Ghz)
Fedora
MPJE
Process
8
157
mpiJava
Process
8
111
The macroscopic inter-service
DSS Overhead
is about
35µs
MPICH2
Process
8
Process
8
64.2
Intel8b
Vista
DSS
from
(8 core is
2.66composed
Ghz)
Fedora
MPJE
170
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
XP
CCR
Thread
4
25.8
CCRMPJE
threads that
Processhave
8
142
4µs overhead for
spawningmpiJava
threads inProcess
dynamic search
applications
Fedora
8
100
20µs overhead for
MPI Exchange
Vista
CCR (C#)
Thread
8
20.2
Intel4 (4 core 2.8 Ghz)
Parallel Multicore
Deterministic Annealing Clustering
Parallel Overhead
on 8 Threads Intel 8b
0.45
10 Clusters
0.4
Overhead = Constant1 + Constant2/n
Speedup = 8/(1+Overhead)
0.35
Constant1 = 0.05 to 0.1 (Client Windows) due to thread
runtime fluctuations
0.3
0.25
20 Clusters
0.2
0.15
0.1
0.05
10000/(Grain Size n = points per core)
0
0
0.5
1
1.5
2
2.5
3
3.5
4
Total
Clustering is typical of data
mining methods that are needed for
Total
tomorrow’s clients or servers bathed in a data rich environment
Asian
Clustering Census data in
Indiana on dual quadcore processors
Asian
Implemented with CCR and DSS
Hispanic
Hispanic
Use deterministic annealing that uses multiscale method to avoid
local minima
Purdue
Renters
Renters
Efficiency is 90% limitedRenters
by peculiar Windows thread scheduling
effects
IUB
30 Clusters
10 Clusters
Parallel Multicore
Deterministic Annealing Clustering
Parallel Overhead for large (2M points) Indiana Census clustering
on 8 Threads Intel 8b
This fluctuating overhead due to 5-10% runtime fluctuations between threads
0.250
0.200
overhead
“Constant1”
0.150
0.100
0.050
Increasing number of clusters decreases
communication/memory bandwidth overheads
0.000
0
5
10
15
20
#cluster
25
30
35
Parallel Multicore
Deterministic Annealing Clustering
0.200
Parallel Overhead for subset of PubChem clustering on 8 Threads
(Intel 8b)
0.180
“Constant1”
The fluctuating overhead
is reduced to 2% (as bits not doubles)
40,000 points with 1052 binary properties
(Census is 2 real valued properties)
0.160
overhead
0.140
0.120
0.100
0.080
0.060
0.040
Increasing number of clusters decreases
communication/memory bandwidth overheads
0.020
0.000
0
2
4
6
8
10
#cluster
12
14
16
18
Intel 8-core C# with 80 Clusters: Vista Run
Time Fluctuations for Clustering Kernel
• 2 Quadcore Processors
80 Cluster(ratio
of std to timeofvsrun
#thread)
• This is average of standard
deviation
time of the 8 threads
between messaging synchronization points
0.1
Standard Deviation/Run Time
10,000 Datpts
50,000 Datapts
0.05
500,000 Datapts
Number of Threads
0
0
1
2
3
4
5
6
7
8
Intel 8 core with 80 Clusters: Redhat Run
Time Fluctuations for Clustering Kernel
• This is average of standard deviation of run time of the
80 Cluster(ratio of std to time vs #thread)
8 threads between messaging synchronization points
0.006
Standard Deviation/Run Time
0.004
10,000 Datapts
50,000 Datapts
0.002
500,000 Datapts
Number of Threads
0
1
2
3
4
5
6
7
8
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