1. Web 2.0, Grids and Parallel Computing
Tsinghua University October
Geoffrey Fox
Community Grids Laboratory, School of informatics Indiana University 1 1

2. Abstract of Web 2.0, Grids and Parallel Computing
We discuss the application of Web 2.0 to support scientific research (e-Science) and related e-moreorlessanything applications.
Web 2.0 offers interesting technical approaches to build the core e-infrastructure (Cyberinfrastructure) as well as a host of interesting services exemplified by Facebook, YouTube, Amazon S3/EC2 and Google maps.
We discuss why some of the original Grid goals of linking the world's computer systems may not be so relevant today and that interoperability is needed at the data and not always at the infrastructure level.
Web 2.0 may also support Parallel Programming a better parallel computing software environment motivated by the need to run commodity applications on multicore chips. 2 2

3. Applications, Infrastructure, Technologies
This field is confused by inconsistent use of terminology; I define
Web Services, Grids and (aspects of) Web 2.0 (Enterprise 2.0) are technologies
Grids could be everything (Broad Grids implementing some sort of managed web) or reserved for specific architectures like OGSA or Web Services (Narrow Grids)
These technologies combine and compete to build electronic infrastructures termed e-infrastructure or Cyberinfrastructure
e-moreorlessanything is an emerging application area of broad importance that is hosted on the infrastructures e-infrastructure or Cyberinfrastructure
e-Science or perhaps better e-Research is a special case of e-moreorlessanything

4. e-moreorlessanything
‘e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it.’ from its inventor John Taylor Director General of Research Councils UK, Office of Science and Technology
e-Science is about developing tools and technologies that allow scientists to do ‘faster, better or different’ research
Similarly e-Business captures an emerging view of corporations as dynamic virtual organizations linking employees, customers and stakeholders across the world.
This generalizes to e-moreorlessanything including presumably e-TsinghuaResearch and e-olympics, e-chinesefood ….
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 4 4

5. What is Cyberinfrastructure
Cyberinfrastructure is (from NSF) infrastructure that supports distributed science (e-Science)– data, people, computers
Clearly core concept more general than Science
Exploits Internet technology (Web2.0) adding (via Grid technology) management, security, supercomputers etc.
It has two aspects: parallel – low latency (microseconds) between nodes and distributed – highish latency (milliseconds) between nodes
Parallel needed to get high performance on individual large simulations, data analysis etc.; must decompose problem
Distributed aspect integrates already distinct components – especially natural for data
Cyberinfrastructure is in general a distributed collection of parallel systems
Cyberinfrastructure is made of services (originally Web services) that are “just” programs or data sources packaged for distributed access 5 5

6. Service or Web Service Approach
One uses GML, CML etc. to define the data structure in a system and one uses services to capture “methods” or “programs”
In eScience, important services fall in three classes
Simulations
Data access, storage, federation, discovery
Filters for data mining and manipulation
Services could use something like WSDL (Web Service Definition Language) to define interoperable interfaces but Web 2.0 follows old library practice: one just specifies interface
Service Interface (WSDL) establishes a “contract” independent of implementation between two services or a service and a client
Services should be loosely coupled which normally means they are coarse grain
Services will be composed (linked together) by mashups (typically scripts) or workflow (often XML – BPEL)
Software Engineering and Interoperability/Standards are closely related

7. Relevance of Web 2.0
They say that Web 1.0 was a read-only Web while Web 2.0 is the wildly read-write collaborative Web
Web 2.0 can help e-Science in many ways
Its tools can enhance scientific collaboration, i.e. effectively support virtual organizations, in different ways from grids
The popularity of Web 2.0 can provide high quality technologies and software that (due to large commercial investment) can be very useful in e-Science and preferable to Grid or Web Service solutions
The usability and participatory nature of Web 2.0 can bring science and its informatics to a broader audience
Web 2.0 can even help the emerging challenge of using multicore chips i.e. in improving parallel computing programming and runtime environments

8. “Best Web 2.0 Sites” -- 2006 Extracted from http://web2.wsj2.com/
All important capabilities for e-Science
Social Networking
Start Pages
Social Bookmarking
Peer Production News
Social Media Sharing
Online Storage (Computing) 8 8

9. Web 2.0, Grids and Web Services I
Web Services have clearly defined protocols (SOAP) and a well defined mechanism (WSDL) to define service interfaces
There is good .NET and Java support
The so-called WS-* specifications provide a rich sophisticated but complicated standard set of capabilities for security, fault tolerance, meta-data, discovery, notification etc.
“Narrow Grids” build on Web Services and provide a robust managed environment with growing but still small adoption in Enterprise systems and distributed science (so called e-Science)
Web 2.0 supports a similar architecture to Web services but has developed in a more chaotic but remarkably successful fashion with a service architecture with a variety of protocols including those of Web and Grid services
Over 500 Interfaces defined at
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

10. Web 2.0 Systems like Grids have Portals, Services, Resources
Captures the incredible development of interactive Web sites enabling people to create and collaborate

11. Web 2.0, Grids and Web Services II
I once thought Web Services were inevitable but this is no longer clear to me
Web services are complicated, slow and non functional
WS-Security is unnecessarily slow and pedantic (canonicalization of XML)
WS-RM (Reliable Messaging) seems to have poor adoption and doesn’t work well in collaboration
WSDM (distributed management) specifies a lot
There are de facto Web 2.0 standards like Google Maps and powerful suppliers like Google/Microsoft which “define the architectures/interfaces”
One can easily combine SOAP (Web Service) based services/systems with HTTP messages but dominance of “lowest common denominator” suggests additional structure/complexity of SOAP will not easily survive

12. Distribution of APIs and Mashups per Protocol
REST
SOAP
XML-RPC
REST,
XML-RPC, SOAP JS
Other
google maps
netvibes
live.com
virtual earth
google search
amazon S3
amazon ECS
flickr
ebay
youtube
411sync
del.icio.us
yahoo! search
yahoo! geocoding
technorati
yahoo! images
trynt
yahoo! local
Number of
APIs
Number of
Mashups
SOAP is quite a small fraction

13. Where did Narrow Grids and Web Services go wrong?
Too much Computing: historically one (including narrow grids) has tried to increase computing capabilities by
Optimizing performance of codes at cost of re-usability
Exploiting all possible CPU’s such as Graphics co-processors and “idle cycles” (across administrative domains)
Linking central computers together such as NSF/DoE/DoD supercomputer networks without clear user requirements
Next Crisis in technology area will be the opposite problem – commodity chips will be way 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
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 islands
Data focus is consistent with Semantic Grid/Web but not clear if latter has learnt the usability message of Web 2.0
One needs to share computing, data, people in e-moreorlessanything, Grids initially focused on computing but data and people are more important
eScience is healthy as is e-moreorlessanything
Most Grids are solving wrong problem at wrong point in stack with a complexity that makes friendly usability difficult

14. Information System Architecture
The Party Line approach to Information Infrastructure is clear – one creates a Cyberinfrastructure consisting of distributed services accessed by portals/gadgets/gateways/RSS feeds
Services include:
“Original data”
Transformations or filters implementing DIKW (Data Information Knowledge Wisdom) lattice
Some filters could correspond to large simulations
Final “Decision Support” step converting wisdom into action
Generic services such as security, profiles etc.
Infrastructure will be set up as a System of Systems (Grids of Grids)
Services and/or Grids just accept some form of DIKW and produce another form of DIKW
“Original data” has no explicit input; just output
e-moreorlessanything Interoperability at DIKW interface not at details of computing and repository resources

15. Information and Cyberinfrastructure
Raw Data  Data  Information  Knowledge  Wisdom
Another Grid
Decisions
Another Grid SS SS SS SS SS FS FS OS MD MD FS
Portal OS OS FS OS OS
Inter-Service Messages FS FS FS
Another Service FS FS MD MD OS MD OS OS FS
Other
Service FS FS FS FS MD OS OS OS FS FS FS MD MD FS
Filter Service OS
Another Grid FS
MetaData FS FS FS MD
Sensor Service SS SS SS SS SS SS SS SS SS SS
Database
Another Service

16. Some Web 2.0 Activities at IU
Use of Blogs, RSS feeds, Wikis etc.
Use of Mashups for Cheminformatics Grid workflows
Moving from Portlets to Gadgets in portals (or at least supporting both)
Use of Connotea to produce tagged document collections such as 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)
Multicore SALSA project using for Parallel Programming 2.0

17. Use blog to create posts.
Display blog RSS feed in MediaWiki. 17

18. Semantic Research Grid (SRG)
Integrates tagging and search system that allows users to use multiple sites and consistently integrate them with traditional citation databases
We built a mashup linking to del.icio.us, CiteULike, Connotea allowing exchange of tags between sites and between local repositories
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)
We implement a rather more powerful access control mechanism
We build heuristic tools to mine “web lists” for citations
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
11/2/2019 18

19. Semantic Scholars Grid
Web 2.0
MySpace
Windows Live Academic Search
Traditional Grid Cyberinfrastructure
Web service Wrappers
Export: RSS, Bibtex Endnote etc.
Del.icio.us
Google Scholar
CiteULike
Citeseer
Connotea
MyResearch Database
Bibliographic Database
Science.gov
Bibsonomy
PubChem
Biolicious
Generic Document Tools
MASHUP
PubMed
CMT
Conference Management
Community Tools
Manuscript
Central
Integration/ Enhancement
User Interface
etc.
Existing
User Interface
New Document-enhanced
Research Tools
Existing Document based Tools

20. Example Parallel Computing Collection selected on Cell Tag
So far no clear “winner” in tagging space
Maybe CiteUlike with different metadata better
How do I preserve investment?

21. del.icio.us Tags del.icio.us Tags
Download to
Local System
del.icio.us Tags

22. Minority Serving Institution CyberInfrastructure Empowerment Coalition
MSI-CIEC Portal
MSI-CIEC
Minority Serving Institution CyberInfrastructure Empowerment Coalition

23. NSF Grants Tag System
NSF has the ability to get information (in XML) on all of the grants a particular person worked on
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.
The investigators are imported as users
Each has a bookmark for each project they worked on
They are also represented in the tags of these projects.
Can now form research collaborations by linking researchers with common tags
Hopefully will enable broader collaborations and not just those between “usual suspects”

24. Superior (from broad usage) technologies of Web 2
Superior (from broad usage) technologies of Web 2.0 Mash-ups can replace Worflow Gadgets can replace Portlets UDDI replaced by user generated registries

25. Mashups v Workflow?
Mashup Tools are reviewed at
Workflow Tools are reviewed by Gannon and Fox
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) 25 25

26. Grid Workflow Datamining in Earth Science
Work with Scripps Institute
Grid services controlled by scripting workflow process real time data from ~70 GPS Sensors in Southern California
Streaming Data
Support
Transformations
Data Checking
Hidden Markov Datamining (JPL)
Display (GIS)
NASA GPS
Real Time
Archival
Earthquake 26 26

27. Grid Workflow Data Assimilation in Earth Science
Grid services triggered by abnormal events and controlled by workflow process real time data from radar and high resolution simulations for tornado forecasts
Typical graphical interface to service composition
Taverna another well known Grid/Web Service workflow tool
Recent Web 2.0 visual Mashup tools include Yahoo Pipes and Microsoft Popfly

28. Web 2.0 Mashups and APIs
has (Sept ) 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)

29. The List of Web 2.0 API’s Each site has API and its features
Divided into broad categories
Only a few used a lot (49 API’s used in 10 or more mashups)
RSS feed of new APIs
Google maps dominates but Amazon S3 growing in popularity

30. 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
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
Now to Portals 30 30

31. Portlets v. Google Gadgets
Note the many competitions powering Web 2.0 Mashup and Gadget Development
Portlets v. Google Gadgets
Portals for Grid Systems are built using portlets with software like GridSphere integrating these on the server-side into a single web-page
Google (at least) offers the Google sidebar and Google home page which support Web 2.0 services and do not use a server side aggregator
Google is more user friendly!
The many Web 2.0 competitions is an interesting model for promoting development in the world-wide distributed collection of Web 2.0 developers
I guess Web 2.0 model will win! 31 31

32. Typical Google Gadget Structure
Google Gadgets are an example of
Start Page Web 2.0 term for portals) technology See
… 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 32

33. 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

34. Multicore SALSA at CGL Service Aggregated Linked Sequential Activities
Aims to link parallel and distributed (Grid) computing by developing parallel applications as services and not as programs or libraries
Improve traditionally poor parallel programming development environments
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 ’s µs
Developing set of services (library) of multicore parallel data mining algorithms

35. Parallel Programming Model
If multicore technology is to succeed, mere mortals must be able to build effective parallel programs
There are interesting new developments – especially the Darpa HPCS Languages X10, Chapel and Fortress
However if mortals are to program the core chips expected in 5-7 years, then we must use today’s technology and we must make it easy
This rules out radical new approaches such as new languages
The important applications are not scientific computing but most of the algorithms needed are similar to those explored in scientific parallel computing
Intel RMS analysis
We can divide problem into two parts:
High Performance scalable (in number of cores) parallel kernels or libraries
Composition of kernels into complete applications
We currently assume that the kernels of the scalable parallel algorithms/applications/libraries will be built by experts with a
Broader group of programmers (mere mortals) composing library members into complete applications.

36. Scalable Parallel Components
There are no agreed high-level programming environments for building library members that are broadly applicable.
However lower level approaches where experts define parallelism explicitly are available and have clear performance models.
These include MPI for messaging or just locks within a single shared memory.
There are several patterns to support here including the collective synchronization of MPI, dynamic irregular thread parallelism needed in search algorithms, and more specialized cases like discrete event simulation.
We use Microsoft CCR as it supports both MPI and dynamic threading style of parallelism
It already supports a Web 2.0 compatible service model DSS

37. Composition of Parallel Components
The composition step has many excellent solutions as this does not have the same drastic synchronization and correctness constraints as for scalable kernels
Unlike kernel 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.

38. Parallel Programming 2.0
Web 2.0 Mashups will (by definition the largest market) drive composition tools for Grid, web and parallel programming
Parallel Programming 2.0 will build on Mashup tools like Yahoo Pipes and Microsoft Popfly
Yahoo Pipes

39. 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)
40.0
MPICH2: Fast
39.3
Nemesis
4.21
Intel8c:gf20
Fedora
MPJE
157
mpiJava
111
MPICH2
64.2
Intel8b
(8 core 2.66 Ghz)
Vista
170
142
100
CCR (C#)
Thread
20.2
AMD4
(4 core 2.19 Ghz) XP 4
185
152
99.4
CCR
16.3
Intel4 (4 core 2.8 Ghz)
25.8
SALSA Performance
The macroscopic inter-service DSS Overhead is about 35µs
DSS is composed from CCR threads that have 4µs overhead for spawning threads in dynamic search applications
20µs overhead for MPI Exchange

40. Clustering Census data in Indiana on dual quadcore processors
Total
Asian
Hispanic
Renters
30 Clusters
Total
Asian
Hispanic
Renters
IUB
Purdue
10 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 DSS
Use deterministic annealing that uses multiscale method to avoid local minima
Efficiency is 90% limited by peculiar Windows thread scheduling effects
Renters

41. Parallel Multicore GIS Deterministic Annealing Clustering
Parallel Overhead on 8 Threads Intel 8b
Speedup = 8/(1+Overhead)
10 Clusters
Overhead = Constant1 + Constant2/n
Constant1 = 0.02 to 0.1 (Windows) due to thread runtime fluctuations
20 Clusters
10000/(Grain Size n = points per core)

42. Web 2.0 v Narrow Grid I
Web 2.0 and Grids are addressing a similar application class although Web 2.0 has focused on user interactions
So technology has similar requirements
Web 2.0 chooses simplicity (REST rather than SOAP) to lower barrier to everyone participating
Web 2.0 and Parallel Computing tend to use traditional (possibly visual) (scripting) languages for equivalent of workflow whereas Grids use visual interface backend recorded in BPEL
Web 2.0 and Grids both use SOA Service Oriented Architectures
Services will be used everywhere: Grids, Web 2.0 and Parallel Computing
“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
Web 2.0 suggest data not infrastructure system linkage 42 42

43. The world does itself in large numbers!
Web 2.0 v Narrow Grid II
Web 2.0 has a set of major services like GoogleMaps or Flickr but the world is composing Mashups that make new composite services
End-point standards are set by end-point owners
Many different protocols covering a variety of de-facto standards
Narrow Grids have a set of major software systems like Condor and Globus and a different world is extending with custom services and linking with workflow
Popular Web 2.0 technologies are PHP, JavaScript, JSON, AJAX and REST with “Start Page” e.g. (Google Gadgets) interfaces
Popular Narrow Grid technologies are Apache Axis, BPEL WSDL and SOAP with portlet interfaces
Robustness of Grids demanded by the Enterprise?
Not so clear that Web 2.0 won’t eventually dominate other application areas and with Enterprise 2.0 it’s invading Grids
The world does itself in large numbers!

44. Web 2.0 v Narrow Grid III
Narrow Grids have a strong emphasis on standards and structure
Web 2.0 lets a 1000 flowers (protocols) and a million developers bloom and focuses on functionality, broad usability and simplicity
Interoperability at user (data) level not at service level
Puts semantics into application (user) level (like KML for maps) and minimizes general system level semantics
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?
Flickr has geocoded (structured) and unstructured tags
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 44 44

45. The Ten areas covered by the 60 core WS-* Specifications
WS-* Specification Area
Typical Grid/Web Service Examples
1: Core Service Model
XML, WSDL, SOAP
2: Service Internet
WS-Addressing, WS-MessageDelivery; Reliable Messaging WSRM; Efficient Messaging MOTM
3: Notification
WS-Notification, WS-Eventing (Publish-Subscribe)
4: Workflow and Transactions
BPEL, WS-Choreography, WS-Coordination
5: Security
WS-Security, WS-Trust, WS-Federation, SAML, WS-SecureConversation
6: Service Discovery
UDDI, WS-Discovery
7: System Metadata and State
WSRF, WS-MetadataExchange, WS-Context
8: Management
WSDM, WS-Management, WS-Transfer
9: Policy and Agreements
WS-Policy, WS-Agreement
10: Portals and User Interfaces
WSRP (Remote Portlets)

46. 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
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

47. 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