Cyberinfrastructure Technologies and Applications

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 http://grids.ucs.indiana.edu/ptliupages/presentations/ gcf@indiana.eduhttp://www.infomall.org

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. 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. 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

5. Service or Web Service Approach • One uses GML, CML etc. to define the datastructure 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

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. Computing and Cyberinfrastructure: TeraGrid Grid Infrastructure Group (UChicago) UW PSC UC/ANL NCAR PU NCSA UNC/RENCI IU Caltech ORNL USC/ISI SDSC TACC Resource Provider (RP) Software Integration Partner

7. Data and Cyberinfrastructure • DIKW: Data  Information  Knowledge Wisdomtransformation • 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

8. Information and Cyberinfrastructure SS Database SS SS SS SS SS SS SS Raw Data  Data  Information  Knowledge  Wisdom AnotherGrid Decisions AnotherGrid SS SS SS SS FS FS OS MD MD FS Portal OS OS FS OS OS Inter-Service Messages FS FS FS AnotherService 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 AnotherGrid FS MetaData FS FS FS MD Sensor Service SS SS SS SS SS SS SS SS SS SS AnotherService

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

10. Virtual Observatory Astronomy GridIntegrate Experiments Radio Far-Infrared Visible Dust Map Visible + X-ray Galaxy Density Map

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 • Terrible connectivity from base and field camps to Continental subGrid

13. OSCAR Document Analysis InChI Generation/Search Computational Chemistry (Gamess, Jaguar etc.) Varuna.net Quantum Chemistry 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

14. Process Chemistry-Biology Interaction Data from HTS (High Throughput Screening) Percent Inhibition or IC50 data is retrieved from HTS Scientists at IU prefer Web 2.0 to Grid/Web Service for workflow Grids can link data analysis ( e.g image processing developed in existing Grids), traditional Chem-informatics 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 atPubChem ECCR centers MLSCN centers Workflows encoding plate & control well statistics, distribution analysis, etc Question: Was this screen successful? Workflows encoding distribution analysis of screening results Question: What should the active/inactive cutoffs be? 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 PROCESS CHEMINFORMATICS GRIDS

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 • 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 • 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

16. “Best Web 2.0 Sites” -- 2006 Extracted from http://web2.wsj2.com/ Social Networking Start Pages Social Bookmarking Peer Production News Social Media Sharing Online Storage (Computing) 16

17. Web 2.0 Systems are Portals, Services, Resources • Captures the incredible development of interactive Web sites enabling people to create and collaborate

18. 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, 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 http://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,

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

20. Applications, Infrastructure, Technologies • The discussion is confused by inconsistent use of terminology – this is what I mean • Multicore, Narrow and BroadGrids 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

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 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)

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

23. Semantic Research Grid (SRG) Architecture 1/7/2020 23

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

25. Mashups v Workflow? Mashup Tools are reviewed at http://blogs.zdnet.com/Hinchcliffe/?p=63 Workflow Tools are reviewed by Gannon and Foxhttp://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) 25

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

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. Web 2.0 Mashups and APIs • http://www.programmableweb.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)

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

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. 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 Development 32

33. Typical Google Gadget Structure … Lots of HTML and JavaScript </Content> </Module> Google Gadgets are an example of Start Page technologySee http://blogs.zdnet.com/Hinchcliffe/?p=8 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. 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. 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

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 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 36

37. The Ten areas covered by the 60 core WS-* Specifications

38. WS-* Areas and Web 2.0

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

40. Intel’s Projection

41. Today Tomorrow RMS: Recognition Mining Synthesis Recognition Mining Synthesis Is it …? What is …? What if …? Find a model instance Create a model instance Model Model-less Real-time streaming and transactions on static – structured datasets Very limited realism Model-based multimodal recognition Real-time analytics on dynamic, unstructured, multimodal datasets Photo-realism and physics-based animation

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: http://splweb.bwh.harvard.edu:8000/pages/images_movies.html

43. Intel’s Application Stack

44. Multicore SALSA at IU • Service Aggregated Linked Sequential Activities • http://www.infomall.org/multicore • 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

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 http://msdn.microsoft.com/robotics/ 45

46. Timing of HP Opteron Multicore as a function of number of simultaneous two-way service messages processed (November 2006 DSS Release) Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better DSS Service Measurements 46

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

49. Parallel Multicore Clustering (C# on Windows) Parallel Overheadon 8 Threads running on Intel 8 core Speedup = 8/(1+Overhead) 10 Clusters Overhead = Constant1 + Constant2/n Constant1 = 0.05 to 0.1 (Client Windows) due to threadruntime fluctuations 20 Clusters 10000/(Grain Size n = points per core) PC07Intro gcf@indiana.edu

50. We use DSS as Service Framework as Integrated with CCR Supporting MPI/Threading