S-Matrix and the Grid

1. S-Matrix and the Grid Geoffrey FoxProfessor of Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401 December 12 2003 gcf@indiana.edu http://www.infomall.org

2.  Exchange Target S-Matrix and PWA • We need an amplitude analysis to find most “interesting” resonances • If this makes sense, we are effectively parameterizing photon-Reggeon amplitude with resonance at “top” vertex in full (123 in diagram) or partial (12, 23, 31) channel • Complicated as off diagonal, one “fake” particle and often more than 2 final particles • This requires a lot of approximations whose effect can be estimated with S-Matrix Theory • Analyticity, Unitarity, Crossing, Regge Theory, Spin formalism, Duality, Finite Energy Sum Rules Regge in Top Vertex 1  Reggeon Exchange forProduction 2 3

3. Some Lessons from the past I • All confusing effects exist and no fundamental (correct) way to remove. So one should: • Minimize effect of the hard (insoluble) problems such as “particles from wrong vertex”, “unestimatable exchange effects” sensitive to slope of unclear Regge trajectories, absorption etc. • Carefully identify where effects are “additive” and where confusingly overlapping • Note many of effects are intrinsically MORE important in multiparticle casethan in relatively well studiedπ N π N • Try to estimate impact of uncertainties from each effect on results • It would be very helpful to get systematic very high statistic studies of relatively clean cases where spectroscopy may be less interesting but one can examine uncertainties • Possibilities are A1 A2 A3 B1peripherally produced and even π N ππ N;K or π beams good

4. S-Matrix Approach • S-Matrix ideas that work reasonably include: • Regge theory for production process • Two-component duality adding Regge dual to Regge to background dual to the Pomeron • Can help to identify if a resonance is classic qq or exotic • Use of Regge exchange at top vertex to estimate high partial waves in amplitude analysis • Finite Energy Sum Rules for top vertex as constraints on low mass amplitudes and most quantitative way of linking high and low masses • Ignore Regge Cuts in Production • Unitarity effects not included directly due to duality double counting

5. Investigate Uncertainties • There are several possible sources of error • Errors in Quasi 2-body and limited number of amplitudes approximation • Unitarity(final state interactions) • Errors in the two-component duality picture • Exotic particles are produced and are just different • Photon beams, π exchange or some other “classic effect” not present in original πN analyses behaves unexpectedly • Failure of quasi two body approximation • Regge cuts cannot be ignored • Background from other channels • Develop tests for these in both “easy” cases (such as “old” meson beam data) and in photon beam data at Jefferson laboratory • Investigate all effects on any interesting result from PWA

6. Grid Computing: Making The Global Infrastructure a Reality • Note book withFran Berman andAnthony J.G. Hey, • ISBN: 0-470-85319-0 • Hardcover 1080 Pages • Published March 2003 • http://www.grid2002.org • I had more fun in days gone by; no more do I write • “Skeletons in the Regge Cupboard” or • “The Importance of being an Amplitude”

7. Some Further Links • A talk on Grid and e-Science was webcast in an Oracle technology serieshttp://webevents.broadcast.com/techtarget/Oracle/100303/index.asp?loc=10 • See also the “Gap Analysis” survey of Grid technologyhttp://grids.ucs.indiana.edu/ptliupages/publications/GapAnalysis30June03v2.pdf • This presentation is at http://grids.ucs.indiana.edu/ptliupages/presentations • Next Semester – course on “e-Science and the Grid” given by Access Grid • Write up for May Conference describes proposed Physics Strategyhttp://grids.ucs.indiana.edu/ptliupages/publications/gluonic_gcf.pdfhttp://grids.ucs.indiana.edu/ptliupages/presentations/pwamay03.ppt

8. e-Business e-Science and the Grid • e-Business captures an emerging view of corporations as dynamic virtual organizations linking employees, customers and stakeholders across the world. • The growing use of outsourcing is one example • e-Science is the similar vision for scientific research with international participation in large accelerators, satellites or distributed gene analyses. • The Grid integrates the best of the Web, traditional enterprise software, high performance computing and Peer-to-peer systems to provide the information technology infrastructure for e-moreorlessanything. • A deluge of data of unprecedented and inevitable size must be managed and understood. • People, computers, data and instruments must be linked. • On demand assignment of experts, computers, networks and storage resources must be supported

9. What is a High Performance Computer? • We might wish to consider three classes of multi-node computers • 1) Classic MPP with microsecond latency and scalable internode bandwidth (tcomm/tcalc ~ 10 or so) • 2) Classic Cluster which can vary from configurations like 1) to 3) but typically have millisecond latency and modest bandwidth • 3) Classic Grid or distributed systems of computers around the network • Latencies of inter-node communication – 100’s of milliseconds but can have good bandwidth • All have same peak CPU performance but synchronization costs increase as one goes from 1) to 3) • Cost of system (dollars per gigaflop) decreases by factors of 2 at each step from 1) to 2) to 3) • One should NOT use classic MPP if class 2) or 3) suffices unless some security or data issues dominates over cost-performance • One should not use a Grid as a true parallel computer – it can link parallel computers together for convenient access etc.

10. Sources of Grid Technology • Grids support distributed collaboratories or virtual organizations integrating concepts from • The Web • Agents • Distributed Objects(CORBA Java/Jini COM) • Globus, Legion, Condor, NetSolve, Ninf and other High Performance Computing activities • Peer-to-peer Networks • With perhaps the Web and P2P networks being the most important for “Information Grids” and Globus for “Compute Grids” • Service Architecture based on Web Services most critical feature

11. Portal Services SystemServices SystemServices Application Service Middleware SystemServices SystemServices SystemServices Raw (HPC) Resources Database Typical Grid Architecture UserServices “Core”Grid

12. PortalService Security Catalog A typical Web Service • In principle, services can be in any language (Fortran .. Java .. Perl .. Python) and the interfaces can be method calls, Java RMI Messages, CGI Web invocations, totally compiled away (inlining) • The simplest implementations involve XML messages (SOAP) and programs written in net friendly languages like Java and Python PaymentCredit Card Web Services WSDL interfaces Warehouse Shipping control WSDL interfaces Web Services

13. What is Happening? • Grid ideas are being developed in (at least) two communities • Web Service – W3C, OASIS • Grid Forum (High Performance Computing, e-Science) • Service Standards are being debated • Grid Operational Infrastructure is being deployed • Grid Architecture and core software being developed • Particular System Services are being developed “centrally” – OGSA framework for this in • Lots of fields are setting domain specific standards and building domain specific services • There is a lot of hype • Grids are viewed differently in different areas • Largely “computing-on-demand” in industry (IBM, Oracle, HP, Sun) • Largely distributed collaboratories in academia

14. Technical Activities of Note • Look at different styles of Grids such as Autonomic(Robust Reliable Resilient) • New Grid architectures hard due to investment required • Critical Services Such as • Security – build message based not connection based • Notification – event services • Metadata – Use Semantic Web, provenance • Databases and repositories – instruments, sensors • Computing – Submit job, scheduling, distributed file systems • Visualization, Computational Steering • Fabric and Service Management • Network performance • Program the Grid – Workflow • Access the Grid – Portals, Grid Computing Environments

15. 1) Types of Grid R3 Lightweight P2P Federation and Interoperability 2) Core Infrastructure and Hosting Environment Service Management Component Model Service wrapper/Invocation Messaging 3) Security Services Certificate Authority Authentication Authorization Policy 4) Workflow Services and Programming Model Enactment Engines (Runtime) Languages and Programming Compiler Composition/Development 5) Notification Services 6) Metadata and Information Services Basic including Registry Semantically rich Services and meta-data Information Aggregation (events) Provenance 7) Information Grid Services OGSA-DAI/DAIT Integration with compute resources P2P and database models 8) Compute/File Grid Services Job Submission Job Planning Scheduling Management Access to Remote Files, Storage and Computers Replica (cache) Management Virtual Data Parallel Computing 9) Other services including Grid Shell Accounting Fabric Management Visualization Data-mining and Computational Steering Collaboration 10) Portals and Problem Solving Environments 11) Network Services Performance Reservation Operations Issues and Types of Grid Services

16. Not OGSA specific services Domain - More specialized services: data Possibly OGSA replication, workflow, etc., etc. Broadly applicable services: registry, OGSA Environment authorization, monitoring, data access, etc., etc. OGSI on Web Services Hosting Environment for WS Given to us from on high Network OGSA OGSI & Hosting Environments • Start with Web Services in a hosting environment • Add OGSI to get a Grid service and a component model • Add OGSA to get Interoperable Grid “correcting” differences in base platform and adding key functionalities

17. WSDL Of Filter OGSA-DAI Interface Filter DB Integration of Data and Filters • One has the OGSA-DAI Data repository interface combined with WSDL of the (Perl, Fortran, Python …) filter • User only sees WSDL not data syntax • Some non-trivial issues as to where the filtering compute power is • Microsoft says filter next to data

18. Remote Grid Service Remote Grid Service 1: Plan Execution 4: Job Submittal Data Data 10: Job Status 1: Job Management Service (Grid Service Interface to user or program client) 2: Schedule and control Execution 8: VirtualData 3: Access to Remote Computers 6: File and Storage Access 7: CacheDataReplicas 5: Data Transfer Technology Components of (Services in)a Computing Grid 9: Grid MPI

19. Grid Strategy • LHC Computing will be very well established and handling 10-100 times as much data as GlueX when we need to go into production • GriPhyn iVDGL EDG EGEE PPDG GridPP will customize core Grid technology for accelerator-based experiments • Transport Data • Cache Data • Manage initial data analysis and Monte Carlo • Not clear if GT2, GT3, OGSI but will certainly be Web Service based • Need to keep in close touch with these activities • Build GlueX physics analysis consistent with this infrastructure

20. Closely coupled Java/Python … Coarse Grain Service Model Service B Service A Module B Module A Messages 0.1 to 1000 millisecond latency Method Calls.001 to 1 millisecond Implementing Grids • Need to design a service architecture for GlueX • Build on services from HEP and other fields • Need some specific gluexML meta-data specifying services and properties specific to GlueX • Specify data structures and method interfaces in XML • Use portlets for user-interfaces as in http://www.ogce.org • Break-up into services where-ever possible but only if “coarse-grain”

21. Collage of Portals Earthquakes – NASAFusion – DoE OGCE Components – NSF Publications -- CGL

22. Application WS WS linking to user and Other WS (data sources) Typicalcodes Approach • Convert every code into a Web Service • Convert every utility like “visualization” into a Web service • Have good support for authoring and manipulating meta-data • Use existing code/database technology (SQL/Fortran/C++) linked to “Application Web/OGSA services” • XML specification of models, computational steering, scale supported at “Web Service” level as don’t need “high performance” here • Allows use of Semantic Grid technology

23. Database UserServices Portal Services GridComputingEnvironments VisualizationService ModelingServices FittingService Data AccessService Middleware “Core”Grid(Globus) SystemServices SystemServices SystemServices Raw Data and Compute Resources

24. CERN LHC Data Analysis Grid