1 Cyberinfrastructure Technologies and Applications Summit on Cyberinfrastructure: Innovation At Work Banff Springs Hotel Banff Canada October Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN

22 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

33 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

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 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

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

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

8 Database SS SSSSSSSSS FS FSFS Portal FSFS OSOS OSOS OSOS OSOS OSOS OSOS OSOS OSOS OSOS OSOS OSOS OSOS MD MetaData Filter Service Sensor Service Other Service Another Grid Raw Data  Data  Information  Knowledge  Wisdom Decisions S S Another Service S Another Grid S SS FS Inter-Service Messages Information and Cyberinfrastructure

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 Grid Integrate Experiments RadioFar-InfraredVisible Visible + X-ray Dust Map Galaxy Density Map

C YBERINFRASTRUCTURE C ENTER FOR P OLAR S CIENCE (CICPS) 11

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

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.)

14 Process Chemistry-Biology Interaction Data from HTS (High Throughput Screening) Percent Inhibition or IC 50 data is retrieved from HTS Question: Was this screen successful? Question: What should the active/inactive cutoffs be? Question: What can we learn about the target protein or cell line from this screen? Compound data submitted to PubChem Workflows encoding distribution analysis of screening results 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 at PubChem ECCR centers MLSCN centers Workflows encoding plate & control well statistics, distribution analysis, etc 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 CHEMINFORMATICSPROCESSGRIDS Scientists at IU prefer Web 2.0 to Grid/Web Service for workflow

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 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

1616 “Best Web 2.0 Sites” Extracted from Social Networking Start Pages Social Bookmarking Peer Production News Social Media Sharing Online Storage (Computing)

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 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 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

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 computinghttp:// 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 10/2/

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

2525 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)

2626 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 Transformations Data Checking Hidden Markov Datamining (JPL) Display (GIS) NASA GPS Earthquake Real Time Archival

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

2828 Web 2.0 uses all types of Services Here a Gadget Mashup uses a 3 service workflow with a JavaScript Gadget Client

29 Web 2.0 Mashups and APIs web.com/apis has (Sept ) 2312 Mashups and 511 Web 2.0 APIs and with GoogleMaps the most often used in Mashups web.com/apis 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 Now to Portals 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

3232 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

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 technology See

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

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 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 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)

38 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:// 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

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 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 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

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

Pradeep K. Dubey, 42 What is a tumor?Is there a tumor here? What if the tumor progresses? It is all about dealing efficiently with complex multimodal datasets Recognition MiningSynthesis Images courtesy:

43 Intel’s Application Stack

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 ’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

4646 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

47 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

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

PC07Intro Parallel Multicore Clustering (C# on Windows) Parallel Overhead on 8 Threads running on Intel 8 core 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

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

PC07Intro 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

PC07Intro 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

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) ?