1. 1 Web 2.0, Grids and Parallel Computing Oxford University December 07 2007 Geoffrey Fox Community Grids Laboratory, School of informatics Indiana University http://www.infomall.org/multicore gcf@indiana.edugcf@indiana.edu, http://www.infomall.orghttp://www.infomall.org

2. 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 2.0 -- a better parallel computing software environment motivated by the need to run commodity applications on multicore chips.

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. 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- OxfordResearch and e-OxfordEducation …. 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

5. 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

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 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

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. 8 “Best Web 2.0 Sites” -- 2006 Extracted from http://web2.wsj2.com/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)

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 http://www.programmableweb.com/apishttp://www.programmableweb.com/apis Web 2.0 also has many well known capabilities with Google Maps and Amazon Compute/Storage services of clear general relevance There are also Web 2.0 services supporting novel collaboration modes and user interaction with the web as seen in social networking sites, portals, MySpace, YouTube

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 RESTSOAPXML-RPCREST, XML-RPC REST, XML-RPC, SOAP REST, SOAP JSOther 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 Mashups Number of APIs SOAP is quite a small fraction

13. Too much Computing? Historically both grids and parallel computing have tried to increase computing capabilities by Optimizing performance of codes at cost of re-usability Exploiting all possible CPU’s such as Graphics co- processors and “idle cycles” (across administrative domains) Linking central computers together such as NSF/DoE/DoD supercomputer networks without clear user requirements Next Crisis in technology area will be the opposite problem – commodity chips will be 32-128way parallel in 5 years time and we currently have no idea how to use them on commodity systems – especially on clients Only 2 releases of standard software (e.g. Office) in this time span so need solutions that can be implemented in next 3-5 years Intel RMS analysis: Gaming and Generalized decision support (data mining) are ways of using these cycles

14. Intel’s Projection

15. Too much Data to the Rescue? Multicore servers have clear “universal parallelism” as many users can access and use machines simultaneously Maybe also need application parallelism (e.g. datamining) as needed on client machines Over next years, we will be submerged of course in data deluge Scientific observations for e-Science Local (video, environmental) sensors Data fetched from Internet defining users interests Maybe data-mining of this “too much data” will use up the “too much computing” both for science and commodity PC’s PC will use this data(-mining) to be intelligent user assistant? Must have highly parallel algorithms

16. Where did Narrow Grids and Web Services go wrong? Interoperability Interfaces will be for data not for infrastructure Google, Amazon, TeraGrid, European Grids will not interoperate at the resource or compute (processing) level but rather at the data streams flowing in and out of independent Grid clouds Data focus is consistent with Semantic Grid/Web but not clear if latter has learnt the usability message of Web 2.0 Lack of detailed standards in Web 2.0 preferable to industry who can get proprietary advantage inside their clouds One needs to share computing, data, people in e- moreorlessanything, Grids initially focused on computing but data and people are more important eScience is healthy as is e-moreorlessanything Most Grids are solving wrong problem at wrong point in stack with a complexity that makes friendly usability difficult

17. 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

18. Database SS SS S SSS FS FSFS Portal FSFS Filter Service Data in Data out Sensor or Data Interchange Service Another Grid Raw Data  Data  Information  Knowledge  Wisdom Decisions S S Another Service S Another Grid S SS FS Inter-Service Messages FS Filter Service Storage Cloud Compute Cloud S S S S

19. 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 http://www.connotea.org/user/crmc for parallel computinghttp://www.connotea.org/user/crmc 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

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

21. 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 9/11/2015 21

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

23. 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?

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

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

26. 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”

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

28. 28 Mashups v Workflow? Mashup Tools are reviewed at http://blogs.zdnet.com/Hinchcliffe/?p=63 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 http://grids.ucs.indiana.edu/ptliupages/publications/Workflow-overview.pdf Both include scripting in PHP, Python, sh etc. as both implement distributed programming at level of services Mashups use all types of service interfaces and perhaps do not have the potential robustness (security) of Grid service approach Mashups typically “pure” HTTP (REST)

29. 29 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 Earthquake Real Time Archival

30. 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

31. Web 2.0 Mashups and APIs 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 http://www.programmable web.com/apis This is the Web 2.0 UDDI (service registry)

32. 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

33. Now to Portals 33 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 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

34. 34 Portlets v. Google Gadgets Portals for Grid Systems are built using portlets with software like GridSphere integrating these on the server-side into a single web-page Google (at least) offers the Google sidebar and Google home page which support Web 2.0 services and do not use a server side aggregator Google is more user friendly! The many Web 2.0 competitions is an interesting model for promoting development in the world-wide distributed collection of Web 2.0 developers I guess Web 2.0 model will win! Note the many competitions powering Web 2.0 Mashup and Gadget Development

35. Typical Google Gadget Structure … Lots of HTML and JavaScript Portlets build User Interfaces by combining fragments in a standalone Java Server Google Gadgets build User Interfaces by combining fragments with JavaScript on the client Google Gadgets are an example of Start Page (Web 2.0 term for portals) technology See http://blogs.zdnet.com/Hinchcliffe/?p=8

36. The Ten areas covered by the 60 core WS-* Specifications WS-* Specification AreaTypical Grid/Web Service Examples 1: Core Service ModelXML, WSDL, SOAP 2: Service InternetWS-Addressing, WS-MessageDelivery; Reliable Messaging WSRM; Efficient Messaging MOTM 3: NotificationWS-Notification, WS-Eventing (Publish- Subscribe) 4: Workflow and TransactionsBPEL, WS-Choreography, WS-Coordination 5: SecurityWS-Security, WS-Trust, WS-Federation, SAML, WS-SecureConversation 6: Service DiscoveryUDDI, WS-Discovery 7: System Metadata and StateWSRF, WS-MetadataExchange, WS-Context 8: ManagementWSDM, WS-Management, WS-Transfer 9: Policy and AgreementsWS-Policy, WS-Agreement 10: Portals and User InterfacesWSRP (Remote Portlets)

37. WS-* Areas and Web 2.0 WS-* Specification AreaWeb 2.0 Approach 1: Core Service ModelXML becomes optional but still useful SOAP becomes JSON RSS ATOM WSDL becomes REST with API as GET PUT etc. Axis becomes XmlHttpRequest 2: Service InternetNo special QoS. Use JMS or equivalent? 3: NotificationHard with HTTP without polling– JMS perhaps? 4: Workflow and Transactions (no Transactions in Web 2.0) Mashups, Google MapReduce Scripting with PHP JavaScript …. 5: SecuritySSL, HTTP Authentication/Authorization, OpenID is Web 2.0 Single Sign on 6: Service Discoveryhttp://www.programmableweb.com 7: System Metadata and StateProcessed by application – no system state – Microformats are a universal metadata approach 8: Management==InteractionWS-Transfer style Protocols GET PUT etc. 9: Policy and AgreementsService dependent. Processed by application 10: Portals and User InterfacesStart Pages, AJAX and Widgets(Netvibes) Gadgets

38. 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

39. Multicore SALSA at CGL Service Aggregated Linked Sequential Activities Aims to link parallel and distributed (Grid) computing by developing parallel applications as services and not as programs or libraries Improve traditionally poor parallel programming development environments Developing set of services (library) of multicore parallel data mining algorithms Looking at Intel list of algorithms (and all previous experience), we find there are two styles of “micro” parallelism Dynamic search as in integer programming, Hidden Markov Methods (and computer chess); irregular synchronization with dynamic threads “MPI Style” i.e. several threads running typically in SPMD (Single Program Multiple Data); collective synchronization of all threads together Most Intel RMS are “MPI Style” and very close to scientific algorithms even if applications are not science

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

41. There is MPI style messaging and.. OpenMP annotation or Automatic Parallelism of existing software is practical way to use those pesky cores with existing code As parallelism is typically not expressed precisely, one needs luck to get good performance Remember writing in Fortran, C, C#, Java … throws away information about parallelism HPCS Languages should be able to properly express parallelism but we do not know how efficient and reliable compilers will be High Performance Fortran failed as language expressed a subset of parallelism and compilers did not give predictable performance PGAS (Partitioned Global Address Space) like UPC, Co-array Fortran, Titanium, HPJava One decomposes application into parts and writes the code for each component but use some form of global index Compiler generates synchronization and messaging PGAS approach should work but has never been widely used – presumably because compilers not mature

42. Summary of micro-parallelism On new applications, use MPI/locks with explicit user decomposition A subset of applications can use “data parallel” compilers which follow in HPF footsteps Graphics Chips and Cell processor motivate such special compilers but not clear how many applications can be done this way OpenMP and/or Compiler-based Automatic Parallelism for existing codes in conventional languages

43. Composition of Parallel Components The composition (macro-parallelism) step has many excellent solutions as this does not have the same drastic synchronization and correctness constraints as one has for scalable kernels Unlike micro-parallelism step which has no very good solutions Task parallelism in languages such as C++, C#, Java and Fortran90; General scripting languages like PHP Perl Python Domain specific environments like Matlab and Mathematica Functional Languages like MapReduce, F# HeNCE, AVS and Khoros from the past and CCA from DoE Web Service/Grid Workflow like Taverna, Kepler, InforSense KDE, Pipeline Pilot (from SciTegic) and the LEAD environment built at Indiana University. Web solutions like Mash-ups and DSS Many scientific applications use MPI for the coarse grain composition as well as fine grain parallelism but this doesn’t seem elegant The new languages from Darpa’s HPCS program support task parallelism (composition of parallel components) decoupling composition and scalable parallelism will remain popular and must be supported.

44. “Service Aggregation” in SALSA Kernels and Composition must be supported both inside chips (the multicore problem) and between machines in clusters (the traditional parallel computing problem) or Grids. The scalable parallelism (kernel) problem is typically only interesting on true parallel computers as the algorithms require low communication latency. However composition is similar in both parallel and distributed scenarios and it seems useful to allow the use of Grid and Web composition tools for the parallel problem. This should allow parallel computing to exploit large investment in service programming environments Thus in SALSA we express parallel kernels not as traditional libraries but as (some variant of) services so they can be used by non expert programmers For parallelism expressed in CCR, DSS represents the natural service (composition) model.

45. 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

46. CICC Chemical Informatics and Cyberinfrastructure Collaboratory Web Service Infrastructure Portal Services RSS Feeds User Profiles Collaboration as in Sakai Core Grid Services Service Registry Job Submission and Management Local Clusters IU Big Red, TeraGrid, Open Science Grid Varuna.net Quantum Chemistry OSCAR Document Analysis InChI Generation/Search Computational Chemistry (Gamess, Jaguar etc.) Need to make all this parallel

47. Clustering Data Cheminformatics was tested successfully with small datasets and compared to commercial tools Cluster on properties of chemicals from high throughput screening results to chemical properties (structure, molecular weight etc.) Applying to PubChem (and commercial databases) that have 6- 20 million compounds Comparing traditional fingerprint (binary properties) with real-valued properties GIS uses publicly available Census data; in particular the 2000 Census aggregated in 200,000 Census Blocks covering Indiana 100MB of data Initial clustering done on simple attributes given in this data Total population and number of Asian, Hispanic and Renters Working with POLIS Center at Indianapolis on clustering of SAVI (Social Assets and Vulnerabilities Indicators) attributes at http://www.savi.org) for community and decision makers http://www.savi.org Economy, Loans, Crime, Religion etc.

48. Where are we? We have deterministically annealed clustering running well on 8- core (2-processor quad core) Intel systems using C# and Microsoft Robotics Studio CCR/DSS Could also run on multicore-based parallel machines but didn’t do this (is there a large Windows quad core cluster on TeraGrid?) This would also be efficient on large problems Applied to Geographical Information Systems (GIS) and census data Could be an interesting application on future broadly deployed PC’s Visualize nicely on Google Maps (and presumably Microsoft Virtual Earth) Applied to several Cheminformatics problems and have parallel efficiency but visualization harder as in 150-1024 (or more) dimensions Will develop a family of such parallel annealing data-mining tools where basic approach known for Clustering Gaussian Mixtures (Expectation Maximization) and possibly Hidden Markov Methods

49. 49 Microsoft CCR Supports exchange of messages between threads using named ports FromHandler: Spawn threads without reading ports Receive: Each handler reads one item from a single port MultipleItemReceive: Each handler reads a prescribed number of items of a given type from a given port. Note items in a port can be general structures but all must have same type. MultiplePortReceive: Each handler reads a one item of a given type from multiple ports. JoinedReceive: Each handler reads one item from each of two ports. The items can be of different type. Choice: Execute a choice of two or more port-handler pairings Interleave: Consists of a set of arbiters (port -- handler pairs) of 3 types that are Concurrent, Exclusive or Teardown (called at end for clean up). Concurrent arbiters are run concurrently but exclusive handlers are http://msdn.microsoft.com/robotics/

50. 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!)

51. MPI Exchange Latency in µs (20-30 µs computation between messaging) MachineOSRuntimeGrainsParallelismMPI Exchange Latency Intel8c:gf12 (8 core 2.33 Ghz) (in 2 chips) RedhatMPJE (Java)Process8181 MPICH2 (C)Process840.0 MPICH2: FastProcess839.3 NemesisProcess84.21 Intel8c:gf20 (8 core 2.33 Ghz) FedoraMPJEProcess8157 mpiJavaProcess8111 MPICH2Process864.2 Intel8b (8 core 2.66 Ghz) VistaMPJEProcess8170 FedoraMPJEProcess8142 FedorampiJavaProcess8100 VistaCCR (C#)Thread820.2 AMD4 (4 core 2.19 Ghz) XPMPJEProcess4185 RedhatMPJEProcess4152 mpiJavaProcess499.4 MPICH2Process439.3 XPCCRThread416.3 Intel4 (4 core 2.8 Ghz) XPCCRThread425.8 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

52. Parallel Multicore Deterministic Annealing Clustering Parallel Overhead on 8 Threads Intel 8b Speedup = 8/(1+Overhead) 10000/(Grain Size n = points per core) Overhead = Constant1 + Constant2/n Constant1 = 0.05 to 0.1 (Client Windows) due to thread runtime fluctuations 10 Clusters 20 Clusters

53. Renters Total Asian Hispanic Renters IUB Purdue 10 Clusters Total Asian Hispanic Renters 30 Clusters Clustering is typical of data mining methods that are needed for tomorrow’s clients or servers bathed in a data rich environment Clustering Census data in Indiana on dual quadcore processors Implemented with CCR and DSS Use deterministic annealing that uses multiscale method to avoid local minima Efficiency is 90% limited by peculiar Windows thread scheduling effects

54. Parallel Multicore Deterministic Annealing Clustering “Constant1” Increasing number of clusters decreases communication/memory bandwidth overheads 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

55. Parallel Multicore Deterministic Annealing Clustering “Constant1” Increasing number of clusters decreases communication/memory bandwidth overheads Parallel Overhead for subset of PubChem clustering on 8 Threads (Intel 8b) The fluctuating overhead is reduced to 2% (as bits not doubles) 40,000 points with 1052 binary properties (Census is 2 real valued properties)

56. Intel 8-core C# with 80 Clusters: Vista Run Time Fluctuations for Clustering Kernel 2 Quadcore Processors This is average of standard deviation of run time of the 8 threads between messaging synchronization points Number of Threads Standard Deviation/Run Time

57. Intel 8 core with 80 Clusters: Redhat Run Time Fluctuations for Clustering Kernel This is average of standard deviation of run time of the 8 threads between messaging synchronization points Number of Threads Standard Deviation/Run Time

58. 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