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Cyberinfrastructure Technologies and Applications


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Geoffrey Fox's presentation from Cybera's 2007 Banff Cyberinfrastructure Summit

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Cyberinfrastructure Technologies and Applications

  1. 1. 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 1
  2. 2. 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- AlbertaEnterprise 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 2
  3. 3. 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 3
  4. 4. Underpinnings of Cyberinfrastructure  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 4
  5. 5. 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 5
  6. 6. 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. Grid Infrastructure Group (UChicago) UW UC/ANL PSC NCAR PU NCSA IU UNC/RENCI Caltech ORNL USC/ISI SDSC TACC Resource Provider (RP) Software Integration Partner Computing and Cyberinfrastructure: TeraGrid
  7. 7. Data and Cyberinfrastructure  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 7
  8. 8. Information andInformation  Knowledge Raw Data  Data  Cyberinfrastructure Wisdom Another Decisions Grid Another S S S S Grid SS S S S FS FS S OS MD In te SS MD r- Se Po FS OS OS FS OS rv rt ic al OS e SS FS M FS es Another FS FS sa FS g Service SS MD MD es OS MD OS SS FS OS Other FS FS FS Service MD FS SS OS OS OS FS FS MD FS MD SS FS Filter Service OS Another FS FS FS FS MetaData Grid MD SS S S S S S S S S S Sensor Service S S S S S S S S S SS Another 8 Database Service
  9. 9. Information Cyberinfrastructure 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: • 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.  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 9
  10. 10. Virtual Observatory Astronomy Grid Integrate Experiments Radio Far-Infrared Visible Dust Map Visible + X-ray Galaxy Density Map 10
  12. 12. 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 12 – Terrible connectivity from base and field camps to Continental subGrid
  13. 13. CICC Chemical Informatics and Cyberinfrastructure Collaboratory Web Service Infrastructure Cheminformatics Services Statistics Services Database Services Core functionality Computation functionality 3D structures by Fingerprints Regression CID Similarity Classification SMARTS Descriptors Clustering 3D Similarity 2D diagrams Sampling distributions File format conversion Docking scores/poses by Applications Applications CID Docking Predictive models SMARTS Filtering Feature selection Protein Druglikeness 2D plots Docking scores Toxicity predictions Arbitrary R code (PkCell) Mutagenecity predictions Anti-cancer activity predictions PubChem related data by Pharmacokinetic parameters CID, SMARTS OSCAR Document Analysis InChI Generation/Search Computational Chemistry (Gamess, Jaguar etc.) Quantum Chemistry Core Grid Services Portal Services Service Registry RSS Feeds Job Submission and Management User Profiles Local Clusters Collaboration as in Sakai IU Big Red, TeraGrid, Open Science Grid
  14. 14. Process Chemistry-Biology Interaction Data from HTS (High Throughput Screening) Percent Inhibition Scientists at IU prefer Web 2.0 to or IC50 data is retrieved from HTS Grid/Web Service for workflow Workflows encoding Grids can link data Question: Was this plate & control well analysis ( e.g image statistics, distribution processing developed screen successful? analysis, etc in existing Grids), traditional Chem- Workflows encoding informatics tools, as Question: What should the active/inactive cutoffs be? distribution analysis of well as annotation screening results tools (Semantic Web, and Question: What can we Workflows encoding enhance lead ID and learn about the target statistical comparison of SAR analysis protein or cell line from this results to similar screen? screens, docking of A Grid of Grids linking compounds into proteins to correlate binding, with collections of services activity, literature search at Compound data submitted of active compounds, PubChem to PubChem etc ECCR centers PROCESS CHEMINFORMATICS MLSCN centers 14 GRIDS
  15. 15. People and Cyberinfrastructure: Web 2.0  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 , 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 •  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 co- authors 15
  16. 16. “Best Web 2.0 Sites” -- 2006  Extracted from  Social Networking  Start Pages  Social Bookmarking  Peer Production News  Social Media Sharing  Online Storage (Computing) 16
  17. 17. Web 2.0 Systems are Portals, Services, Resources  Captures the incredible development of interactive Web sites enabling people to create and collaborate 17
  18. 18.  Web 2.0 clearly defined protocols (SOAP) and aI well Web Services have and Web Services 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 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, 18
  19. 19. Web 2.0 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 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 19
  20. 20. Applications, Infrastructure, Technologies  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  e-moreorlessanything is an emerging application area of broad importance that is hosted on the infrastructures e-infrastructure or Cyberinfrastructure 20
  21. 21. 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) 21
  22. 22. Use blog to create posts. Display blog RSS feed in MediaWiki. 22
  23. 23. Semantic Research Grid (SRG) Architecture 10/22/07 23 23
  24. 24. MSI-CIEC Portal MSI-CIEC Minority Serving Institution CyberInfrastructure Empowerment Coalition 24
  25. 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
  26. 26. Grid Workflow Datamining in Earth Science  Work with Scripps Institute NASA GPS  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) 26
  27. 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 27
  28. 28. Web 2.0 uses all types of Services  Here a Gadget Mashup uses a 3 service workflow with a JavaScript Gadget Client 28
  29. 29. Web 2.0 Mashups and APIs  http://www.programmable 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) 29
  30. 30. 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  Amazon S3 growing in popularity 30
  31. 31. 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 31
  32. 32. Note the many competitions powering Web 2.0 Portlets v. Google Gadgets Mashup Development  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! 32
  33. 33. Typical Google Gadget Structure Google Gadgets are an example of Start Page 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
  34. 34. 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  “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 34
  35. 35. 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!
  36. 36. 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 • Semantic Web/Grid has structure to allow reasoning • Annotation in sites like 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 36
  37. 37. 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) 37
  38. 38. 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 Mashups, Google MapReduce (no Transactions in Web 2.0) 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 38
  39. 39. Too much Computing?  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  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  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  There is “Too much data” as well as “Too much computing” but unclear implications 39
  40. 40. Intel’s Projection 40
  41. 41. RMS: Recognition Mining Synthesis Recognition Mining Synthesis What is …? Is it …? What if …? Find a model Create a model Model instance instance Today Model-less Real-time streaming and Very limited realism transactions on static – structured datasets Tomorrow Model-based Real-time analytics on Photo-realism and multimodal dynamic, unstructured, physics-based recognition multimodal datasets animation Pradeep K. Dubey, 41
  42. 42. Recognition Mining Synthesis What is a tumor? Is there a tumor here? What if the tumor progresses? It is all about dealing efficiently with complex multimodal datasets Images courtesy: Pradeep K. Dubey, 42
  43. 43. Intel’s Application Stack 43
  44. 44. Multicore SALSA at IU  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 10-100’s µs  Developing Service (library) of multicore parallel data mining algorithms 44
  45. 45. 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 • 45
  46. 46. DSS quot;Getquot; (loop 1 to 10000; two services on one node) 350 300 DSS Service Measurements Average run time (microseconds) 250 200 150 100 50 0 1 10 100 1000 10000 Timing of HP Opteron Multicore as aRound tripsnumber of simultaneous two- function of way service messages processed (November 2006 DSS Release)  Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better 46 46
  47. 47. MPI Exchange Latency in µs (20-30 µs computation between messaging) Machine OS Runtime Grains Parallelism MPI Exchange Latency Intel8c:gf12 Redhat MPJE (Java) Process 8 181 (8 core 2.33 Ghz) MPICH2 (C) Process 8 40.0 (in 2 chips) MPICH2: Fast Process 8 39.3 Nemesis Process 8 4.21 Intel8c:gf20 Fedora MPJE Process 8 157 (8 core 2.33 Ghz) mpiJava Process 8 111 MPICH2 Process 8 64.2 Intel8b Vista MPJE Process 8 170 (8 core 2.66 Ghz) Fedora MPJE Process 8 142 Fedora mpiJava Process 8 100 Vista CCR (C#) Thread 8 20.2 AMD4 XP MPJE Process 4 185 (4 core 2.19 Ghz) Redhat MPJE Process 4 152 mpiJava Process 4 99.4 MPICH2 Process 4 39.3 XP CCR Thread 4 16.3 Intel4 (4 core 2.8 Ghz) XP CCR Thread 4 25.8 47
  48. 48. 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
  49. 49. Parallel Multicore Clustering (C# on Windows) 0.45 Parallel Overhead 10 Clusters on 8 Threads running on Intel 8 core 0.4 Speedup = 8/(1+Overhead) Overhead = Constant1 + Constant2/n 0.35 Constant1 = 0.05 to 0.1 (Client Windows) due to thread 0.3 runtime fluctuations 0.25 20 Clusters 0.2 0.15 0.1 0.05 10000/(Grain Size n = points per core) 0 PC07Intro 49 0 0.5 1 1.5 2 2.5 3 3.5 4
  50. 50. We use DSS as Service Framework as Integrated with CCR Supporting MPI/Threading 50
  51. 51. Intel 8-core C# with 80 Clusters: Vista Run Time Fluctuations for Clustering Kernel • 2 Quadcore Processors • This is average of standard deviation vs #thread)time of the 8 threads 80 Cluster(ratio of std to time of run between messaging synchronization points 0.1 Standard Deviation/Run Time 10,000 Datpts 50,000 Datapts std / time 0.05 500,000 Datapts Number of Threads 0 PC07Intro 51 0 1 2 3 4 5 6 7 8 thread
  52. 52. 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 Number of Threads52 0 1 2 3 4 5 6 7 8
  53. 53. 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