Performance Control in RealityGrid

1. Performance Control in RealityGrid Ken Mayes, Mikel Luján, Graham Riley, Rupert Ford, Len Freeman, John Gurd !st RealityGrid Workshop, June 2003

2. Overview • Preamble • The case for Performance Control • Context • Malleable, component-based Grid applications • RealityGrid Application • The LB3D Use Case • RealityGrid Performance Control System • Design and status report • Generalisation . . .

3. “Performance Analysis and Distributed Computing” . . . • We want specific computational performance and resource levels • We want these in an environment that is • Distributed (physically) • Heterogeneous (inherently) • Dynamic (by necessity) • We want them with a minimum effort

4. . . . Implies Performanceand Resource Control • There is no viable alternative apart from automating the process of achieving them • monitoring and analysis (sensing) • change when unsatisfactory (actuation) • This is a classical control system scenario, and we need to use established control system techniques to deal with it

5. Control Systems Overview feedback function actuator error • Control systems involve appropriate change of actuated variables informed by (timely) feedback of monitoring information:

6. Summary • Traditional performance “control” amounts to “open loop” control, mediated by human “performance engineers” • Distributed environments introduce enough extra complexity that “closed loop” control becomes essential • Successful closed loop control demands accurate and rapid feedback data, thus affecting achievable control limits

7. Context • Grid applications will be distributed and, in some sense, component-based. • To deliver the power grid model, adaptation is key! • Elevate users above Grid resource and performance details. • Our work is considering adaptation and its impact on performance: • Adaptation in component deployment, • Initial model configuration and deployment on resources. • Flexibility in deployment of compositions of coupled models. • Adaptation via malleable components. • At run time, re-allocation of resources in response to changes.

8. RealityGrid - LB3D Use Case Display simulation time equal Tracking a parameter search Params 1 LB3D Output rate 1 Resolution 1 T=100 User Changes dynamically Output rate 2 Resolution 2 T=100 LB3D Params 2

9. LB3D Use Case Tracking a parameter study Params 1 LB3D Output rate 1 Resolution 1 Params 2 LB3D Output rate 2 Resolution 2 User Display rates ~equal

10. “Malleable” LB3D - mechanisms • LB3D will respond to requests to change resources • Use more (or less) pes (& mem) on current system • Move from one system to another • Via machine independent (parallel) checkpoint/restart • LB3D will output science data (for remote visualisation) at higher or lower rates • LB3D will (one day) respond to requests to continue running at higher (or lower) lattice resolution • Each of the above affects performance (e.g. timesteps per second rate) • Each has an associated cost . . .

11. Use Case - continued • User might be tracking many parameter set developments (one per LB3D instance) • Some will be uninteresting (for a while) • Lower output rate / resolution / terminate • Some will become interesting • Increase output rate / resolution • One aim: Re-distribute resources across all LB3D instances to maintain highest possible timestep rate

12. A General Grid Application Visualise Data Process Data Generate Data Component 1 Component 2 Component 3 Computational Grid Resources Applications and components exhibit phased behaviour

13. Life is Hierarchical • Can we use hierarchy to “divide and conquer” complex system problems? • Introduce “Performance Steerers” at component- and application-levels . . .

14. Performance Steerers - Design External Resource Scheduler APS CPS CPS CPS Component 1 Component 2 Component 3 Initial deployment + Run-time adaptation Component Framework Computational Grid Resources

15. Full System APS Predictor Predictor CPS Component 2 External Resource Scheduler Application Performance Repository Component Performance Repository Resource Usage Monitor Loader

16. Performance Prediction • Role of APS • To distribute available resources to the components in such a way that the predicted performance of components gives a minimum predicted execution time. • Role of CPS • To utilise allocated resources and component performance effectors (actuators) so that the predicted execution time of the steered component is minimised.

17. Life is Repetitive • Many programs iterate more-or-less the same thing over-and-over again • We can take advantage of this • e.g. for load balance in weather prediction • and, possibly, for performance control

18. Application Progress Points Ph 1 Ph 2 Ph 3 Ph 4 Ph 5 Ph 6 Ph 7 0 1 2 3 4 5 6 7 • Assume that the execution of the application proceeds through phases. Phase boundaries are marked by Progress Points. • NB. Can take decisions about performance and take actions at the progress points: • Must be safe points

19. Component Progress Points • Information about progress points will be contained in some repository. APS Application progress points CPS Component progress points Component Time

20. Implementation: LB3D APS CPS APS as daemon, CPS as library Comms I/f Sockets (rpc) CPS Progress Comms I/f Procedure calls Component Interface Component control

21. Implementation: Machine 1 APS socket Machine 2 Machine 3 CPS LB3D shmem shmem MPIRUN Component Loader Component Loader socket DUROC + RSL + GLOBUS + GRIDFTP

22. Start-up Process • GlobusRun, RSL script for Component loaders (one per machine in Grid) plus APS daemon. • Loaders connect to each other. • LB3D started by Loader (via MPIRUN), calls CPS (a library) at start-up. • CPS connects to APS. • LB3D calls CPS at each subsequent progress point and CPS communicates with APS. • Continue until LB3D completed (e.g. no. of timesteps complete).

23. Status • We have a prototype implementation (basic mechanisms) First Experiment: • Every N tsteps, move LB3D between machines 2 and 3 – determined by APS. • At tstep mod N progress point in LB3D, APS tells CPS (which tells the component) to checkpoint, CPS writes certain status information to the shmem area and then LB3D (and CPS) dies. • Loader on machine it ran on communicates to loader on machine it is to run on. The restart file is Gridftp’d along with restart info. (e.g. tstep to shmem area of new loader). • New LB3D is started and CPS manages the restart. • Continue until no more tsteps.

24. Status • Preliminary performance results np 4, data 64x64x64 checkpoint file (XDR) size 32.8 MB average resident tstep time for cronus 3.384 (s.) average migration tstep time to cronus 43.718 (s.) average resident tstep time for centaur3 6.675 (s.) average migration tstep time to centaur3 55.280 (s.) np 4, data 8x8x8 checkpoint file (XDR) size 64 KB average resident tstep time for cronus 0.017 (s.) average migration tstep time to cronus 0.504 (s.) average resident tstep time for centaur3 0.061 (s.) average migration tstep time to centaur3 3.038 (s.) cronus = SGI Origin 3400 and centaur3 = Sun TCF

25. Status • We have a prototype implementation Second Experiment: • Three platforms (2 SGI, 1 Solaris – Linux coming soon) • Move LB3D at random between machines, as determined by APS. • Has exposed Gridftp problems – worked around. • Have timings for 2 machines, but not yet 3 • Now looking at ICENI framework.

26. Status • Developing implementation of performance repository • Berkeley Database (linked as library) • Survey of prediction and machine learning algorithms • runtime vs. off line analysis • accuracy of predictions • amount of history data required • Learning control theory and understand how to apply it to Performance Control.

27. Generalisation • LB3D as a component is an “easy” case • how does the above scheme generalise? • Are components necessary? • Is hierarchy necessary? • component < application < scheduler • What different kinds of component and/or application might there be? • redefinition of the “process” • a topic for my forthcoming leave of absence

28. Summary • Aim: Develop an architecture that enables us to investigate different mechanisms for Performance Control of malleable component based applications on the Grid • Main characteristic: • dynamic adaptation • Design/implementation tensions: • general vs. specific purpose • APS <-> CPS communication • APS <-> CPS ratio • performance prediction algorithms - accuracy vs. execution time • APS/CPS overhead vs. application execution time • Work in development (first year in one sentence): • A Grid Framework for Malleable Component-based Application Migration

29. Related Projects at Manchester • Met Office – FLUME - design of next generation Unified Model software • Coupled models • Tyndall Centre – SoftIAM - Climate Impact, Integrated assessment modelling • Coupling climate and economic models • Computational Markets • 1 RA and 1 PhD - positions being filled at present • UK e-Science funded (Imperial College led) For more information check http://www.cs.man.ac.uk/cnc http://www.realitygrid.org