Realistic modelling of complex problems on Grids

1. Realistic modelling of complex problems on Grids John Brooke (University of Manchester) Peter Coveney PI RealityGrid (University College London) Stephen Pickles (University of Manchester) Thanks also to the other RealityGrid co-Investigators John Darlington (Imperial College) Steve Kenny and Roy Kalawsky (Loughborough University) John Gurd (University of Manchester) Mike Cates (University of Edinburgh) Adrian Sutton (University of Oxford)

2. The RealityGrid project Mission: “Using Grid technology to closely couple high performance computing, high throughput experiment and visualization, RealityGrid will move the bottleneck out of the hardware and back into the human mind.” Scientific aims: • to predict the realistic behavior of matter using diverse simulation methods (Lattice Boltzmann, Molecular Dynamics and Monte Carlo) spanning many time and length scales • to discover new materials through integrated experiments. http://www.realitygrid.org

3. Partners Industrial • Schlumberger • Edward Jenner Institute for Vaccine Research • Silicon Graphics Inc • Computation for Science Consortium • Advanced Visual Systems • Fujitsu Academic • University College London • Queen Mary, University of London • Imperial College • University of Manchester • University of Edinburgh • University of Oxford • University of Loughborough http://www.realitygrid.org

4. RealityGrid Storage devices Grid infrastructure (Globus, Unicore,…) “Instruments”: XMT devices, LUSI,… HPC resources Scalable MD, MC, mesoscale modelling User with laptop/PDA (web based portal) Steering ReG steering API Performance control/monitoring Visualization engines VR and/or AG nodes Moving the bottleneck out of the hardware and into the human mind… http://www.realitygrid.org

5. RealityGrid Characteristics • Grid-enabled (Globus, UNICORE) • Component-based, service-oriented • Steering is central • Computational steering • On-line visualisation of large, complex datasets • Feedback-based performance control • Remote control of novel, grid-enabled, instruments (LUSI) • Advanced Human-Computer Interfaces (Loughborough) • Everything is (or should be) distributed and collaborative • High performance computing, visualization and networks • All in a materials science domain • multiple length scales, many "legacy" codes (Fortran90, C, C++, mostly parallel) http://www.realitygrid.org

6. Three dimensional Lattice-Boltzmann simulations • Code (LB3D) written in Fortran90 and parallelized using MPI. • Scales linearly on all available resources. • Fully steerable. • Future plans include move to parallel data format PHDF5. • Data produced during a single large scale simulation can exceed hundreds of gigabytes or even terabytes. • Simulations require supercomputers • High end visualization hardware and parallel rendering software (e.g. VTK) needed for data analysis. 3D datasets showing snapshots from a simulation of spinodal decomposition: A binary mixture of water and oil phase separates. ‘Blue’ areas denote high water densities and ‘red’ visualizes the interface between both fluids. http://www.realitygrid.org

7. Exploring parameter spacethrough computational steering Cubic micellar phase, high surfactant density gradient. Cubic micellar phase, low surfactant density gradient. Initial condition: Random water/ surfactant mixture. Self-assembly starts. Lamellar phase: surfactant bilayers between water layers. Rewind and restart from checkpoint. http://www.realitygrid.org

8. Computational Steering - Why? • Terascale simulations can generate in days data that takes months to understand • Problem: to efficiently explore and understand the parameter spaces of materials science simulations • Computational steering aims to short circuit post facto analysis • Brute force parameter sweeps create a huge data-mining problem • Instead, we use computational steering to navigate to interesting regions of parameter space • Simultaneous on-line visualization develops and engages scientist's intuition • thus avoiding wasted cycles exploring barren regions, or even doing the wrong calculation http://www.realitygrid.org

9. Computational steering – how? • We instrument (add "knobs" and "dials" to) simulation codes through a steering library • Library provides: • Pause/resume • Checkpoint and windback • Set values of steerable parameters • Report values of monitored (read-only) parameters • Emit "samples" to remote systems for e.g. on-line visualization • Consume "samples" from remote systems for e.g. resetting boundary conditions • Images can be displayed at sites remote from visualization system, using e.g. SGI OpenGL VizServer, or Chromium • Implemented in 5+ independent parallel simulation codes, F90, C, C++ http://www.realitygrid.org

10. Philosophy • Provide right level of steering functionality to application developer • Instrumentation of existing code for steering • should be easy • should not bifurcate development tree • Hide details of implementation and supporting infrastructure • eg. application should not be aware of whether communication with visualisation system is through filesystem, sockets or something else • permits multiple implementations • application source code is proof against evolution of implementation and infrastructure http://www.realitygrid.org

11. Steering library Steering library Steering library Display Display Display Steering and Visualization Simulation Client data transfer Visualization Visualization http://www.realitygrid.org

12. Steering library Steering library Steering library Architecture Communication modes: • Shared file system • Files moved by UNICORE daemon • GLOBUS-IO • SOAP over http/https Simulation Client data transfer Data mostly flows from simulation to visualization. Reverse direction is being exploited to integrate NAMD&VMD into RealityGrid framework. Visualization Visualization http://www.realitygrid.org

13. Steering GS Steering library Steering library Steering library Steering GS Steering in the OGSA Simulation bind Steering library publish Client connect data transfer Steering client Registry find publish bind Visualization Visualization http://www.realitygrid.org

14. Steering in OGSA continued… • Each application has an associated OGSI-compliant “Steering Grid Service” (SGS) • SGS provides public interface to application • Use standard grid service technology to do steering • Easy to publish our protocol • Good for interoperability with other steering clients/portals • Future-proofed next step to move away from file-based steering or Modular Visualisation Environments with steering capabilities • SGSs used to bootstrap direct inter-component connections for large data transfers • Early working prototype of OGSA Steering Grid Service exists • Based on light-weight Perl hosting environment OGSI::Lite • Lets us use OGSI on a GT2 Grid such as UK e-Science Grid today http://www.realitygrid.org

15. Steering client • Built using C++ and Qt library – currently have execs. for Linux and IRIX • Attaches to any steerable RealityGrid application • Discovers what commands are supported • Discovers steerable & monitored parameters • Constructs appropriate widgets on the fly • Web client (portal) under development http://www.realitygrid.org

16. RealityGrid-L2: LB3D on the L2G Visualization SGI Onyx Vtk + VizServer SGI OpenGL VizServer Laptop Vizserver Client Steering GUI GLOBUS used to launch jobs Simulation Data GLOBUS-IO Steering (XML) File based communication via shared filesystem: Steering GUI X output is tunnelled back using ssh. program lbe use lbe_init_module use lbe_steer_module use lbe_invasion_module Simulation LB3D with RealityGrid Steering API ReG steering GUI http://www.realitygrid.org

17. application performance steerer application component 1 component performance steerer component performance steerer component performance steerer component 2 component 3 Performance Control http://www.realitygrid.org

18. Advance Reservation and Co-allocation:Summary of Requirements • Computational steering + remote, on-line visualization demand: • co-allocation of HPC (processors) and visualization (graphics pipes and processors) resources • at times to suit the humans in the loop • advanced reservation • For medium to large datasets, Network QoS is important • between simulation and visualization, • visualisation and display • Integration with Access Grid • want to book rooms and operators too • Cannot assume that all resources are owned by same VO • Want programmable interfaces that we can rely on • must be ubiquitous, standard, and robust • Reservations (agreements) should be re-negotiable • Hard to change attitudes of sysadmins and (some) vendors http://www.realitygrid.org

19. Steering and workflows • Steering adds extra channels of information and control to Grid services. • Steering and steered components must be state-aware, underlying mechanisms in OS and lower-level schedulers, monitors, brokers must be continually updated with changing state. • How do we store and restore the metadata for the state of the parameter space search? • Human factors are built into our architecture, humans continually interact with orchestrated services. What implications for workflow languages? http://www.realitygrid.org

20. Collaborative aspects • Multiple groups exploring multiple regions of parameter space. • How to record and restore the state of the collaboration? • How to extend the collaboration over multiple sessions? • What are the services and abstractions necessary to bootstrap collaborative sessions? • How do we reliably recreate the resources required by the services, in terms of computation, visualization, instrumentation and networking. http://www.realitygrid.org

21. Integration with Access Grid? Participants location and access rights Virtual Venues Server Multicast addressing Bridges Visualization Workflow Workflows saved from Previous sessions or Created in this session Who participates? Service for Bootstrapping session Contains “just enough” Information to start other Services, red arrows indicate bootstrapping Process Repository Collaborative processes Captured using ontology Can be enacted by Workflow engines Simulation Workflow Workflows saved from Previous sessions or Created in this session What do they use? Application Repository Uses application specific ontology to describe what in silico processes need To be utilised for the session Data Source Workflow Workflows saved from Previous sessions or Created in this session Application data, computation and visualization requirements http://www.realitygrid.org

22. How far have we got? Linking US Extended Terascale Facilities and UK HPC resources via a Trans-Atlantic Grid • We used these combined resources as the basis for an exciting project • to perform scientific research on a hitherto unprecedented scale • Computational steering, spawning, migrating of massive simulations for study of defect dynamics in gyroid cubic mesophases • Visualisation output was streamed to distributed collaborating sites via the Access Grid • Workshop presentation with FZ Juelich and HLRS, Stuttgart on the theme of computational steering. • At Supercomputing, Phoenix, USA, November 2003 TRICEPS entry won “Most Innovative Data-Intensive Application” http://www.realitygrid.org

23. Summary • All our workflow concepts are built around the idea of Steerable Grid Services. • Resources used by services have complex state, may migrate, may be reshaped. • Collaborative aspects of “Humans in the loops” are becoming more and more important. • The problems of allocating and managing the resources necessary for realistic modelling are very hard, they require (at present) getting below the Grid abstractions. • Clearly the Grid abstractions are not yet sufficiently comprehensive and in particular lack support for expression of synchronicity. http://www.realitygrid.org

24. London University Search Instrument LUSI is located at and developed by Queen Mary College, University of London Aim: Find ceramics (e.g. rare earth metal oxides) with interesting / valuable properties (e.g. high temperature superconductivity) Motivation: theory cannot indicate how to construct a compound with a particular property. Established methodology in pharmaceutical industry uses automated sample generation and testing. Let's apply the same idea in materials science, exploring properties that are difficult to predict: superconductivity, luminescence, dielectric response… Furnace XY Table Instruments Printer http://www.realitygrid.org

25. c c Newmaterials Robot Database LUSI - schematic c c Predictions Neural network Measured data http://www.realitygrid.org