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Network and Grid Computing

Network and Grid Computing

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Network and Grid Computing

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  1. Network and Grid Computing Geoffrey Fox Andrea Donnellan May 3, 2004 Computational Geoinformatics Workshop

  2. Solid Earth Science Questions • What is the nature of deformation at plate boundaries and what are the implications for earthquake hazards? • How do tectonics and climate interact to shape the Earth’s surface and create natural hazards? • What are the interactions among ice masses, oceans, and the solid Earth and their implications for sea level change? • How do magmatic systems evolve and under what conditions do volcanoes erupt? • What are the dynamics of the mantle and crust and how does the Earth’s surface respond? • What are the dynamics of the Earth’s magnetic field and its interactions with the Earth system? From NASA’s Solid Earth Science Working Group Report, Living on a Restless Planet, Nov. 2002

  3. The Solid Earth is:Complex, Nonlinear, and Self-Organizing SESWG fed into NASA ESE Computational TechnologyRequirements Workshop, May 2002 Relevent questions that Computational technologies can help answer: • How can the study of strongly correlated solid earth systems be enabled by space-based data sets? • What can numerical simulations reveal about the physical processes that characterize these systems? • How do interactions in these systems lead to space-time correlations and patterns? • What are the important feedback loops that mode-lock the system behavior? • How do processes on a multiplicity of different scales interact to produce the emergent structures that are observed? • Do the strong correlations allow the capability to forecast the system behavior in any sense?

  4. Characteristics of Computing for Solid Earth Science • Widely distributed heterogeneous datasets • Multiplicity of time and spatial scales • Decomposable problems requiring interoperability for full models • Distributed models and expertise Enabled by Grids and Networks

  5. Objectives • IT approaches: Integrate multiple scales into computer simulations. • Web services: Simplified access to data, simulation codes, and flow between simulations of varying types.

  6. What are Grids Good for? • They are “Internet Scale Distributed Computing” and support the linking of globally distributed entities in e-Science concept • Computers • Data from repositories and sensors • People • Early Grids focused on metacomputing (linking computers together) but recently e-Science has highlighted integration of data and building communities • Grid technology naturally build Problem Solving Environments

  7. Some Relevant Grid/Framework Projects • QuakeSim and Solid Earth Research Virtual Observatory SERVOGrid (JPL …) • GEON: Cyberinfrastructure for the Geosciences (San Diego, Missouri, USGS ..) • CME: Community Modeling Environment from SCEC • CIG: Computational Infrastructure for Geodynamics • Geoframework.org Caltech/VPAC • ESMF: Earth System Modeling Framework (NASA) • NERCGrid: Natural Environment Research Council UK e-Science • Earth Systems Grid in DoE Science Grid

  8. Earth Science Computing Capability Capacity

  9. Earth Science Data

  10. Analysis and Visualization Large Disks Metacomputing Grid Large Scale Parallel Computers NO Capability: Spread a single large Problem over multiple supercomputers YES Capacity: Seamless access to multiple computers

  11. RepositoriesFederated Databases Streaming Data Sensors Field Trip Data Database Database Research Education SERVOGrid ? Data FilterServices Customization Services From Researchto Education ResearchSimulations Discovery Services Analysis and VisualizationPortal EducationGrid Computer Farm Geoscience Research and Education Grids

  12. More General Material on Grids • Grids today are built in terms of Web Services – a technology designed to support Enterprise Software and e-Business • Provides wonderful support tools • Provides a new software engineering model supporting interoperability • Grids do not compete with parallel computing • They let MPI run untouched so your parallel codes run as fast as they used to do • Grids do “control/management/metadata management” where higherlatency(around 10 milliseconds – thousand times worse than MPI) acceptable • Global Grid Forum, W3C, OASIS set relevant standards and support community

  13. Database GridComputingEnvironments Portal Services UserServices SystemServices SystemServices Application Service Application Metadata Middleware “Core”Grid SystemServices SystemServices SystemServices Raw (HPC) Resources Actual Application

  14. Grids provide • “Service Oriented Architecture” supporting distributed programs in scalable fashion with clean software engineering • “Multi-tier” architecture supporting seamless access with brokers mediating access to diverse computers and data sources • “Workflow” integrating different distributed services in a single application • Event services to notify computers and people of issues (earthquake struck, job completed) • Easy support of parameter searches and other pleasingly parallel applications with many related non-communicating jobs • Security (Web Services), Database access (OGSA-DAI), Collaboration (Access Grid, GlobalMMCS) • File, dataand meta-data management

  15. Web Services • Web services are the fundamental pieces of distributed Service Oriented Architectures. • We should define lots of useful services that are remotely available • Archival data access services supporting queries, real time sensor access, and mesh generation all seem to be popular choices. • Web services have two important parts: • Distributed services • Client applications • These two pieces are decoupled: one can build clients to remote services without caring about the programming language implementation of the remote service. • Java, C++, Python

  16. Web Services, Continued • Clients can be built in any number of styles • We build portlet clients: ubiquitous, can combine • One can build fancier GUI client applications. • You can even embed Web service client stubs (library routines) in your application code, so that your code can make direct calls to remote data sources, etc. • Regardless of the client one builds, the services are the same in all cases: • my portal and your application code may each use the same service to talk to the same database. • So we need to concentrate on services and let clients bloom as they may: • Client applications (portals, GUIs, etc.) will have a much shorter lifecycle than service interface definitions, if we do our job correctly. • Client applications that are locked into particular services, use proprietary data formats and wire protocols, etc., are at risk. Use WSRF/JSR-168 Portlet standards

  17. Data Deluged Science • During the HPCC Initiative 1990-2000, we worried about data in the form of parallel I/O or MPI-IO, but we didn’t consider it as an enabler of new algorithms and new ways of computing • Data assimilation was not central to HPCC • DoE ASCI (Stockpile Stewardship)set up because didn’t want/have test data! • Now particle physics will get 100 petabytes from CERN LHC • Nuclear physics (Jefferson Lab) in same situation • Use continuously ~30,000 CPU’s simultaneously 24X7 • Weather, climate, solid earth (EarthScope) • Bioinformatics curated databases • Virtual Observatory and SkyServer in Astronomy • Environmental Sensor nets

  18. Data Assimilation Information Simulation Model Datamining Ideas Reasoning ComputationalScience Data DelugedScienceComputing Paradigm Informatics

  19. Data Data Filter Filter Filter Data Filter Data OGSA-DAIGrid Services AnalysisControl Visualize Grid Data Filter Data Deluged ScienceComputing Architecture HPC Simulation Grid Data Assimilation Other Gridand Web Services Distributed Filters massage data For simulation

  20. Some Questions for Data Deluged Science • A new trade-off:How to split funds betweensensors and simulationengines • No systematic study of how best to represent data deluged sciences without known equations at resolution of interest • Data assimilation very relevant • Relationship to “just” interpolating data and then extrapolating a little • Role of Uncertainty Analysis – everything (equations, model, data) is uncertain! • Relationship of data mining and simulation • Growing interest in Data curation and provenance • Role of Cellular Automata (CA) Potts Models and Neural Networks which are “fundamental equation free” approaches

  21. Recommendations of NASA’s Computational Technologies Workshop (May 2002) • Create a Solid Earth Research Virtual Observatory (SERVO) • Numerous distributed heterogeneous real-time datasets • Seamless access to large distributed volumes of data • Data handling and archiving part of framework • Tools for visualization, datamining, pattern recognition, and data fusion • Develop an Solid Earth Science Problem Solving Environment (PSE) • Addresses the NASA specific challenges of multiscale modeling • Model and algorithm development and testing, visualization, and data assimilation • Scalable to workstations or supercomputer depending on size of problem • Numerical libraries existing within a compatible framework • Improve the Computational Environment • PetaFLOP computers with Terabytes of RAM • Distributed and cluster computers for decomposable problems • Development of GRID technologies

  22. SERVOGrid Requirements • Seamless Access to Data repositories and large scale computers • Integration of multiple data sources including sensors, databases, file systems with analysis system • Including filtered OGSA-DAI (Grid database access) • Rich meta-data generation and access with SERVOGrid specific Schema extending openGIS (Geography as a Web service) standards and using Semantic Grid • Portalswith component model for user interfaces and web control of all capabilities • Collaboration to support world-wide work • Basic Grid tools: workflow and notification • Notmetacomputing

  23. Solid Earth Research Virtual Observatory (SERVO) SERVO … Goddard Ames JPL Tier2 Center Tier2 Center Tier2 Center Tier2 Center Tier2 Center HPSS HPSS HPSS HPSS … Observations Archive • 1 PB per year data rate in 2010 • Distributed Heterogeneous Real-Time Datasets ~TBytes/day … Archive … … Downlink … Downlink Tier 0 +1 HPSS Archive … … Downlink 100 TeraFLOPs sustained Tier 1 Tier 2 Fully functional problem solving environment Tier 3 • Program-to-program communication in milliseconds • Approximately 100 model codes Institute Institute Institute Institute 100 - 1000 Mbits/sec Data cache Tier 4 Workstations, other portals • Plug and play composing of parallel programs from algorithmic modules • On-demand downloads of 100 GB in 5 minutes • 106 volume elements rendering in real-time

  24. Virtual Observatory Project • Solid earth research virtual observatory (SERVO) • On-demand downloads of 100 GB files from 40 TB datasets within 5 minutes. • Uniform access to 1000 archive sites with volumes from 1 TB to 1 PB Scaled to 100 sites Prototype cooperative federated data base service integrating 5 datasets of 10 TB each Prototype modeling service capable of integrating 5 modules Capability Decomposition into services with requirements Prototype 1920x1080 pixels at 120 frames per second visualization service Prototype data analysis service Architecture & technology approach 2003 2004 2005 2006 2007 2008 2009 2010 Timeline NASA CT Workshop, May 2002

  25. Problem Solving Environment Project • Fully functional PSE used to develop models for building blocks for simulations. • Program-to-program communication in milliseconds using staging, streaming, and advanced cache replication • Integrated with SERVO • Plug and play composing of parallel programs from algorithmic modules Integrated visualization service with volumetric rendering • Extend PSE to Include • 20 users collaboratory with shared windows • Seamless access to high-performance computers linking remote processes over Gb data channels. Capability Plug and play composing of sequential programs from algorithmic modules Prototype PSE front end (portal) integrating 10 local and remote services Isolated platform dependent code fragments 2003 2004 2005 2006 2007 2008 2009 2010 Timeline NASA CT Workshop, May 2002

  26. Computational Environment ~100 model codes with parallel scaled efficiency of 50% ~104 PetaFLOPs throughput per subfield per year ~100 TeraFLOPs sustained capability per model ~106 volume elements rendering in real time Capability Access to mixture of platforms low cost clusters (20-100) to supercomputers with massive memory and thousands of processors This slide appears inconsistent with slide 8 100’s GigaFLOPs 40 GB RAM 1 Gb/s network bandwidth 2003 2004 2005 2006 2007 2008 2009 2010 Timeline NASA CT Workshop, May 2002

  27. Solid Earth Research Virtual Observatory (iSERVO) • Web-services (portal) based Problem Solving Environment (PSE) • Couples data with simulation, pattern recognition software, and visualization software • Enable investigators to seamlessly merge multiple data sets and models, and create new queries. • Data • Spaced-based observational data • Ground-based sensor data (GPS, seismicity) • Simulation data • Published/historical fault measurements • Analysis Software • Earthquake fault • Lithospheric modeling • Pattern recognition software

  28. Philosophy • Store simulated and observed data • Archive simulation data with original simulation code and analysis tools • Access heterogeneous distributed data through cooperative federated databases • Couple distributed data sources, applications, and hardware resources through an XML-based Web Services framework. • Users access the services (and thus distributed resources) through Web browser-based Problem Solving Environment clients.  • The Web services approach defines standard, programming language-independent application programming interfaces, so non-browser client applications may also be built.

  29. SERVOGrid Basics • Under development in collaboration with researchers at JPL, UC-Davis, USC, and Brown University. • Geoscientists develop simulation codes, analysis and visualization tools. • We need a way to bind distributed codes, tools, and data sets. • We need a way to deliver it to a larger audience • Instead of downloading and installing the code, use it as a remote service.

  30. SERVOGrid Application Descriptions • Codes range from simple “rough estimate” codes to parallel, high performance applications. • Disloc: handles multiple arbitrarily dipping dislocations (faults) in an elastic half-space. • Simplex: inverts surface geodetic displacements for fault parameters using simulated annealing downhill residual minimization. • GeoFEST: Three-dimensional viscoelastic finite element model for calculating nodal displacements and tractions. Allows for realistic fault geometry and characteristics, material properties, and body forces. • VirtualCalifornia: Program to simulate interactions between vertical strike-slip faults using an elastic layer over a viscoelastic half-space • RDAHMM: Time series analysis program based on Hidden Markov Modeling. Produces feature vectors and probabilities for transitioning from one class to another. • PARK: Boundary element program to calculate fault slip velocity history based on fault frictional properties.a model for unstable slip on a single earthquake fault. • Preprocessors, mesh generators • Visualization tools: RIVA, GMT

  31. iSERVO Web Services • Job Submission: supports remote batch and shell invocations • Used to execute simulation codes (VC suite, GeoFEST, etc.), mesh generation (Akira/Apollo) and visualization packages (RIVA, GMT). • File management: • Uploading, downloading, backend crossloading (i.e. move files between remote servers) • Remote copies, renames, etc. • Job monitoring • Apache Ant-based remote service orchestration • For coupling related sequences of remote actions, such as RIVA movie generation. • Database services: support SQL queries • Data services: support interactions with XML-based fault and surface observation data. • For simulation generated faults (i.e. from Simplex) • XML data model being adopted for common formats with translation services to “legacy” formats. • Migrating to Geography Markup Language (GML) descriptions.

  32. Some Conclusions • Grids facilitates support • International Collaborations • Integration of computing with distributed data repositories and real-time sensors • Web services from a variety of fields (e.g. map services from openGIS) • Seamless access to multiple networked compute resources including computational steering • Software infrastructure for Problem Solving Environments