Transcript Cyberinfrastructure Technologies and Applications Summit on Cyberinfrastructure: Innovation At Work Banff Springs Hotel Banff Canada October 11 2007 Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana.

Cyberinfrastructure
Technologies and Applications
Summit on Cyberinfrastructure: Innovation At Work
Banff Springs Hotel
Banff Canada October 11 2007
Geoffrey Fox
Computer Science, Informatics, Physics
Pervasive Technology Laboratories
Indiana University Bloomington IN 47401
http://grids.ucs.indiana.edu/ptliupages/presentations/
[email protected]
http://www.infomall.org
1
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 eAlbertaEnterprise and e-oilandgas, e-geoscience ….
A deluge of data of unprecedented and inevitable size must be
managed and understood.
People (see Web 2.0), computers, data (including sensors and
instruments) must be linked.
On demand assignment of experts, computers, networks and
storage resources must be supported
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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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Underpinnings of
Cyberinfrastructure
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Distributed software systems are being “revolutionized” by
developments from e-commerce, e-Science and the consumer
Internet. There is rapid progress in technology families termed
“Web services”, “Grids” and “Web 2.0”
The emerging distributed system picture is of distributed services
with advertised interfaces but opaque implementations
communicating by streams of messages over a variety of protocols
• Complete systems are built by combining either services or
predefined/pre-existing collections of services together to
achieve new capabilities
As well as Internet/Communication revolutions (distributed
systems), multicore chips will likely be hugely important (parallel
systems)
Industry not academia is leading innovation in these technologies
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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 three classes
• Simulations
• Data access, storage, federation, discovery
• Filters for data mining and manipulation
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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
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TeraGrid resources include more than 250 teraflops of computing capability and more than 30 petabytes of
online and archival data storage, with rapid access and retrieval over high-performance networks. TeraGrid
is coordinated at the University of Chicago, working with the Resource Provider sites: Indiana University,
Oak Ridge National Laboratory, National Center for Supercomputing Applications, Pittsburgh
Supercomputing Center, Purdue University, San Diego Supercomputer Center, Texas Advanced Computing
Center, University of Chicago/Argonne National Laboratory, and the National Center for Atmospheric
Research.
UW
Grid Infrastructure
Group (UChicago)
PSC
UC/ANL
NCAR
PU
NCSA
Caltech
IU
UNC/RENCI
ORNL
USC/ISI
SDSC
TACC
Resource Provider (RP)
Software Integration Partner
Computing and Cyberinfrastructure: TeraGrid
Data and Cyberinfrastructure
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DIKW: Data  Information  Knowledge  Wisdom
transformation
Applies to e-Science, Distributed Business Enterprise (including
outsourcing), Military Command and Control and general
decision support
(SOAP or just RSS) messages transport information expressed in
a semantically rich fashion between sources and services that
enhance and transform information so that complete system
provides
• Semantic Web technologies like RDF and OWL might help us
to have rich expressivity but they might be too complicated
We are meant to build application specific information
management/transformation systems for each domain
• Each domain has Specific Services/Standards (for API’s and Information
such as KML and GML for Geographical Information Systems)
• and will use Generic Services (like R for datamining) and
• Generic Standards (such as RDF, WSDL)
 Standards made before consensus or not observant of technology
progress are dubious
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Raw Data 
Dataand

Information

Knowledge
 Wisdom
Information
Cyberinfrastructure
Another
S
S
FS
FS
SS
FS
FS
MD
O
S
FS
O
S
FS
MD
MD
O
S
FS
F
S
O
S
MD
Filter Service
FS
O
S
FS
Other
Service
MD
O
S
FS
MetaData
SS
S
S
Database
FS
FS
F
S
FS
MD
MD
FS
O
S
FS
FS
O
S
SS
Another
Grid
O
S
FS
SS
SS
S
S
Another
Service
O
S
O
S
SS
FS
MD
MD
FS
SS
FS
S
S
O
S
S
S
Another
Grid
Decisions
Grid
S
S
S
S
Another
Service
S
S
S
S
S
S
S
S
S
S
S
S
Sensor Service
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Information Cyberinfrastructure
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:
• Computing
• “original data”
• Transformations or filters implementing DIKW (Data Information
Knowledge Wisdom) pipeline
• Final “Decision Support” step converting wisdom into action
• Generic services such as security, profiles etc.
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Some filters could correspond to large simulations
Infrastructure will be set up as a System of Systems (Grids of
Grids)
• Services and/or Grids just accept some form of DIKW and produce
another form of DIKW
• “Original data” has no explicit input; just output
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Virtual Observatory Astronomy Grid
Integrate Experiments
Radio
Far-Infrared
Visible
Dust Map
Visible + X-ray
Galaxy Density Map10
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CYBERINFRASTRUCTURE CENTER FOR POLAR SCIENCE (CICPS)
CReSIS PolarGrid
• Important CReSIS-specific Cyberinfrastructure components include
– Managed data from sensors and satellites
– Data analysis such as SAR processing – possibly with parallel
algorithms
– Electromagnetic simulations (currently commercial codes) to design
instrument antennas
– 3D simulations of ice-sheets (glaciers) with non-uniform meshes
– GIS Geographical Information Systems
• Also need capabilities present in many Grids
– Portal i.e. Science Gateway
– Submitting multiple sequential or parallel jobs
• The need for three distinct types of components: Continental USA with
multiple base and field camps
– Base and field camps must be power efficient
– Terrible connectivity from base and field camps to Continental subGrid 12
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
Pharmacokinetic parameters
CID, SMARTS
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
Process Chemistry-Biology Interaction Data
from HTS (High Throughput Screening)
Percent Inhibition or
IC50 data is retrieved
from HTS
Question: Was this
screen successful?
Scientists at IU prefer Web 2.0 to
Grid/Web Service for workflow
Workflows encoding plate
& control well statistics,
distribution analysis, etc
Question: What should the
active/inactive cutoffs be?
Workflows encoding
distribution analysis of
screening results
Question: What can we learn
about the target protein or cell
line from this screen?
Workflows encoding
statistical comparison of
results to similar screens,
docking of compounds
into proteins to correlate
binding, with activity,
literature search of active
compounds, etc
Compound data submitted to
PubChem
Grids can link data
analysis ( e.g image
processing developed in
existing Grids),
traditional Cheminformatics tools, as well
as annotation tools
(Semantic Web,
del.icio.us) and enhance
lead ID and SAR analysis
A Grid of Grids linking
collections of services at
PubChem
ECCR centers
MLSCN centers
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PROCESS
CHEMINFORMATICS
GRIDS
People and Cyberinfrastructure: Web 2.0
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Web 2.0 has tools (sites) and technologies
• Technologies (later) are “competition” for Grids and Web
Services
• Sites (below) are the best way to integrate people into
Cyberinfrastructure
Kazaa, Instant Messengers, Skype, Napster, BitTorrent for P2P
Collaboration – text, audio-video conferencing, files
del.icio.us, Connotea, Citeulike, Bibsonomy, Biolicious manage
shared bookmarks
MySpace, YouTube, Bebo, Hotornot, Facebook, or similar sites
allow you to create (upload) community resources and share
them; Friendster, LinkedIn create networks
• http://en.wikipedia.org/wiki/List_of_social_networking_websites
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Writely, Wikis and Blogs are powerful specialized shared
document systems
Google Scholar and Windows Live Academic Search tells you who
has cited your papers while publisher sites tell you about coauthors
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“Best Web 2.0 Sites” -- 2006
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Extracted from http://web2.wsj2.com/
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 Systems are Portals, Services, Resources
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Captures the incredible development of interactive Web
sites enabling people to create and collaborate
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Web
2.0
and
Web
Services
I
Web Services have clearly defined protocols (SOAP) and a well
defined mechanism (WSDL) to define service interfaces
• There is good .NET and Java support
• The so-called WS-* specifications provide a rich sophisticated but
complicated standard set of capabilities for security, fault tolerance, metadata, discovery, notification etc.
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“Narrow Grids” build on Web Services and provide a robust
managed environment with growing adoption in Enterprise
systems and distributed science (so called e-Science)
Web 2.0 supports a similar architecture to Web services but has
developed in a more chaotic but remarkably successful fashion
with a service architecture with a variety of protocols including
those of Web and Grid services
• Over 500 Interfaces defined at http://www.programmableweb.com/apis
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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,
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Web 2.0 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 standards like Google Maps and
powerful suppliers like Google which “define the rules”
One can easily combine SOAP (Web Service) based
services/systems with HTTP messages but the “lowest
common denominator” suggests additional
structure/complexity of SOAP will not easily survive
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Applications, Infrastructure,
Technologies
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The discussion is confused by inconsistent use of terminology –
this is what I mean
Multicore, Narrow and Broad Grids and Web 2.0 (Enterprise
2.0) are technologies
These technologies combine and compete to build infrastructures
termed e-infrastructure or Cyberinfrastructure
• Although multicore can and will support “standalone” clients probably
most important client and server applications of the future will be internet
enhanced/enabled so key aspect of multicore is its role and integration in
e-infrastructure
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e-moreorlessanything is an emerging application area of broad
importance that is hosted on the infrastructures e-infrastructure
or Cyberinfrastructure
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Some Web 2.0 Activities at IU
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Use of Blogs, RSS feeds, Wikis etc.
Use of Mashups for Cheminformatics Grid workflows
Moving from Portlets to Gadgets in portals (or at least
supporting both)
Use of Connotea to produce tagged document
collections such as http://www.connotea.org/user/crmc
for parallel computing
Semantic Research Grid integrates multiple tagging
and search systems and copes with overlapping
inconsistent annotations
MSI-CIEC portal augments Connotea to tag a mix of
URL and URI’s e.g. NSF TeraGrid use, PI’s and
Proposals
• Hopes to support collaboration (for Minority Serving
Institution faculty)
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Use blog to
create posts.
Display blog RSS
feed in MediaWiki.
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Semantic Research Grid (SRG) Architecture
11/6/2015
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MSI-CIEC Portal
MSI-CIEC
Minority Serving Institution CyberInfrastructure Empowerment Coalition
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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 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
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Web 2.0 uses all types of Services
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Here a Gadget Mashup uses a 3 service workflow with
a JavaScript Gadget Client
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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
The Web 2.0 UDDI (service
registry)
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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
Amazon S3 growing
in popularity
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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
science VLAB portal with
University of Minnesota
Now to Portals
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Note the many competitions powering Web 2.0
Mashup 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 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
Web 2.0 v Narrow Grid I
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Web 2.0 and Grids are addressing a similar application class
although Web 2.0 has focused on user interactions
• So technology has similar requirements
Web 2.0 chooses simplicity (REST rather than SOAP) to lower
barrier to everyone participating
Web 2.0 and Parallel Computing tend to use traditional (possibly
visual) (scripting) languages for equivalent of workflow whereas
Grids use visual interface backend recorded in BPEL
Web 2.0 and Grids both use SOA Service Oriented Architectures
“System of Systems”: Grids and Web 2.0 are likely to build
systems hierarchically out of smaller systems
• We need to support Grids of Grids, Webs of Grids, Grids of
Services etc. i.e. systems of systems of all sorts
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Web 2.0 v Narrow Grid II
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Web 2.0 has a set of major services like GoogleMaps or Flickr
but the world is composing Mashups that make new composite
services
• End-point standards are set by end-point owners
• Many different protocols covering a variety of de-facto standards
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Narrow Grids have a set of major software systems like Condor
and Globus and a different world is extending with custom
services and linking with workflow
Popular Web 2.0 technologies are PHP, JavaScript, JSON,
AJAX and REST with “Start Page” e.g. (Google Gadgets)
interfaces
Popular Narrow Grid technologies are Apache Axis, BPEL
WSDL and SOAP with portlet interfaces
Robustness of Grids demanded by the Enterprise?
Not so clear that Web 2.0 won’t eventually dominate other
application areas and with Enterprise 2.0 it’s invading Grids
The world does itself in large numbers!
Web 2.0 v Narrow Grid III
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Narrow Grids have a strong emphasis on standards and
structure; Web 2.0 lets a 1000 flowers (protocols) and a million
developers bloom and focuses on functionality, broad usability
and simplicity
• Semantic Web/Grid has structure to allow reasoning
• Annotation in sites like del.icio.us and uploading to
MySpace/YouTube is unstructured and free text search
replaces structured ontologies
Portals are likely to feature both Web and “desktop client” technology
although it is possible that Web approach will be adopted more or less
uniformly
Web 2.0 has a very active portal activity which has similar architecture to
Grids
• A page has multiple user interface fragments
Web 2.0 user interface integration is typically Client side using Gadgets AJAX
and JavaScript while
• Grids are in a special JSR168 portal server side using Portlets WSRP and
Java
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The Ten areas covered by the 60 core WS-*
Specifications
WS-* Specification Area
Typical Grid/Web Service Examples
1: Core Service Model
XML, WSDL, SOAP
2: Service Internet
WS-Addressing, WS-MessageDelivery; Reliable
Messaging WSRM; Efficient Messaging MOTM
3: Notification
WS-Notification, WS-Eventing (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)
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WS-* Areas and Web 2.0
WS-* Specification Area
Web 2.0 Approach
1: Core Service Model
XML becomes optional but still 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
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Too much Computing?
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Historically one has tried to increase computing capabilities by
• Optimizing performance of codes
• Exploiting all possible CPU’s such as Graphics co-processors and “idle
cycles”
• Making central computers available such as NSF/DoE/DoD
supercomputer networks
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Next Crisis in technology area will be the opposite problem –
commodity chips will be 32-128way parallel in 5 years time and
we currently have no idea how to use them – especially on clients
• Only 2 releases of standard software (e.g. Office) in this time span
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Gaming and Generalized decision support (data mining) are two
obvious ways of using these cycles
• Intel RMS analysis
• Note even cell phones will be multicore
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There is “Too much data” as well as “Too much computing” but
unclear implications
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Intel’s Projection
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RMS: Recognition Mining Synthesis
Recognition
Mining
Synthesis
What is …?
Is it …?
What if …?
Model
Find a model
instance
Create a model
instance
Today
Model-less
Real-time streaming and
transactions on
static – structured datasets
Very limited realism
Tomorrow
Model-based
multimodal
recognition
Real-time analytics on
dynamic, unstructured,
multimodal datasets
Pradeep K. Dubey, [email protected]
Photo-realism and
physics-based
animation
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Recognition
What is a tumor?
Mining
Synthesis
Is there a tumor here?
What if the tumor progresses?
It is all about dealing efficiently with complex multimodal datasets
Images courtesy: http://splweb.bwh.harvard.edu:8000/pages/images_movies.html
Pradeep K. Dubey, [email protected]
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Intel’s Application Stack
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Multicore SALSA at IU
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Service Aggregated Linked Sequential Activities
• http://www.infomall.org/multicore
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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
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Can use messaging to link parallel and Grid services
but performance – functionality tradeoffs different
• Parallelism needs few µs latency for message latency and
thread spawning
• Network overheads in Grid 10-100’s µs
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Developing Service (library) of multicore parallel data
mining algorithms
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Microsoft CCR for Parallelism
• Use Microsoft CCR/DSS where DSS is mash-up/workflow service
model built from CCR and CCR supports MPI or Dynamic threads
• CCR Supports exchange of messages between threads using named
ports
• FromHandler: Spawn threads without reading ports
• Receive: Each handler reads one item from a single port
• MultipleItemReceive: Each handler reads a prescribed number of
items of a given type from a given port. Note items in a port can be
general structures but all must have same type.
• MultiplePortReceive: Each handler reads a one item of a given type
from multiple ports.
• JoinedReceive: Each handler reads one item from each of two ports.
The items can be of different type.
• Choice: Execute a choice of two or more port-handler pairings
• Interleave: Consists of a set of arbiters (port -- handler pairs) of 3
types that are Concurrent, Exclusive or Teardown (called at end for
clean up). Concurrent arbiters are run concurrently but exclusive
handlers are
• http://msdn.microsoft.com/robotics/
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Average run time (microseconds)
350
DSS Service Measurements
300
250
200
150
100
50
0
1
10
100
1000
10000
Timing of HP Opteron Multicore as aRound
functiontrips
of number of simultaneous twoway service messages processed (November 2006 DSS Release)
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Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better
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MPI Exchange Latency in µs (20-30 µs computation between messaging)
Machine
OS
Runtime
Grains
Parallelism
MPI Exchange
Latency
Intel8c:gf12
(8 core 2.33 Ghz)
(in 2 chips)
Redhat
MPJE (Java)
Process
8
181
MPICH2 (C)
Process
8
40.0
MPICH2: Fast
Process
8
39.3
Nemesis
Process
8
4.21
MPJE
Process
8
157
mpiJava
Process
8
111
MPICH2
Process
8
64.2
Vista
MPJE
Process
8
170
Fedora
MPJE
Process
8
142
Fedora
mpiJava
Process
8
100
Vista
CCR (C#)
Thread
8
20.2
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
Intel8c:gf20
(8 core 2.33 Ghz)
Intel8b
(8 core 2.66 Ghz)
AMD4
(4 core 2.19 Ghz)
Intel4 (4 core 2.8 Ghz)
Fedora
47
Clustering algorithm annealing by decreasing distance scale and gradually finds more
clusters as resolution improved
Here we see 10 increasing to 30 as algorithm progresses
48
Parallel Multicore Clustering
(C# on Windows)
Parallel Overhead
on 8 Threads running on Intel 8 core
0.45
10 Clusters
0.4
Speedup = 8/(1+Overhead)
Overhead = Constant1 + Constant2/n
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
PC07Intro [email protected]
2
2.5
3
3.5
4
49
We use DSS as Service Framework as Integrated
with CCR Supporting MPI/Threading
50
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
PC07Intro [email protected]
0
0
1
2
3
4
5
51
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
PC07Intro [email protected]
0
1
2
3
4
5
Number of Threads52
6
7
8
What should one do?


i.e. How does one Cyberinfrastructure enable a given area/application XYZ
As computing free, focus on identifying data/information/knowledge/wisdom
needed (there is probably too much data but not so much wisdom in DIKW
pipeline)
• Should we care just about “original data” or also about the whole pipeline DIKW?


Scope out supercomputer/computer services needed and exploit OGF
standards
Identify services (filters, often data mining) needed by XYZ?
• Will we need parallel implementations of filters – if so use multicore compatible
frameworks



Identify standards for application XYZ
Set up distributed XYZ Services
Use Web 2.0 (as it makes things easier) not current Grids (which makes things
harder)
• Build a “Programmable XYZ Web”’
• Emphasize Simplicity
• Is “Secrecy” important and in fact viable? Often important but hard

What are synergies of XYZ to pervasive capabilities such as Web 2.0 sites,
National resources like TeraGrid, and “Personal aides in an information rich
world” (future of PC) ?
53