1. Dynamic adaptation of parallel codes Toward self-adaptable components for the Grid Françoise André, Jérémy Buisson & Jean-Louis Pazat IRISA / INSA de Rennes / Université de Rennes 1

2. Our view of the Grid Cluster resource WAN Cluster resource Application Our point of interest

3. Cluster resource Processor resources Our view of the Grid Network resource …

4. Our view of the Grid Environment that is: –Parallel Grid is built up from parallel machines –Dynamic Resource allocation may change dynamically –Distributed Resources are distributed over a network Resources are in different administration domains Need for a new programming technique Parallel self-adaptable distributed components

5. Related works Parallel and distributed components / objects exist –Example: GridCCM, PARDIS Self-adaptable components exist –Example: ACEEL, DART But no parallel and self-adaptable distributed component

6. Principles of parallel components Encapsulation of a parallel code –Collaboration of several communicating processes Goal: allow to easily couple parallel codes

7. Principles of dynamic adaptation Modification of the executed code –Reflexive programming Goal: better fit to allocated resources Execution flow 1. Event 2. Reaction

8. Dynamic adaptation Three key questions: – When should the component adapt? – How should the component be modified? – Where can the reaction be executed?

9. Dynamic adaptation When should the component adapt? –Upon reception of an event from a monitor –According to the policy Adaptation policy Decider Monitor Notifies of events Interprets

10. Dynamic adaptation How should the component be modified? –Executing special code –Following directives of the policy Reaction Adaptation policy Coordinator Decider Executes Requests execution of reactions Interprets

11. Dynamic adaptation Where can the reaction be executed? –At the next adaptation point –Approximated prediction of the next point Based on control flow graph Reaction An adaptation point Behavior 1 Not an adaptation point Behavior 2

12. Dynamic adaptation Behavior Reaction Adaptation policy Coordinator Decider Monitor Modifies Executes Requests execution of reactions Component Platform Interprets Notifies of events

13. Parallel coordinator Parallel reaction Mixing parallelism and adaptation Parallel behavior Adaptation policy Decider Monitor Modifies Executes Requests execution of reactions Component Platform Interprets Notifies of events Parallel behavior

14. Mixing parallelism and adaptation Introduction of global adaptation points –All the processes at the same state Need to coordinate all the processes Example: SPMD code –Adaptation point between each phase Local adaptation point Not global adaptation points Global adaptation points

15. Mixing parallelism and adaptation Need for a distributed algorithm for the parallel coordinator –Only consider globally reachable points In the future of all the processes –Make an agreement of all the processes Choose the same point for all the processes

16. Mixing parallelism and adaptation Need to control the non-determinism –Due to parallelism Dynamically insert synchronization statements –Due to unpredictable conditional instructions Force the result of the conditions if possible –Example: insertion of empty iterations in loops Otherwise postpone the decision-making

17. Experiment –Iterative SPMD code Adaptation points between each iteration –Increase of the number of processors Results –Negligible time in adaptation points –Gain thanks to the adaptation –Expected to scale well Adaptation

18. Related domains Computation steering –Notions equivalent to global adaptation points Need to execute some “special code” at the next “special point” –Particular use of adaptation mechanisms User interface instead of monitors Parallel coordinator Parallel reaction Parallel behavior Adaptation policy Decider Monitor Modifies Executes Requests execution of reactions Component Platform Interprets Notifies of events Parallel behavior Man-Machine Interface

19. Related domains Fault tolerance –Consider dynamic environment –Need for a global “consistent” state In the past for fault tolerance In the future for dynamic adaptation –Relation to dynamic adaptation An application? A complementary feature?

20. Work done Design of the overall architecture –Identification of functional “boxes” Distributed algorithm for the coordinator –Automated instrumentation by static behavioral reification –Simple negotiation protocol Demonstration prototype –Ad-hoc mechanisms –Proof of concept

21. Future work Generalizing the approach –Generic definition of global adaptation points Limits of the “same state” definition Case of non-SPMD codes –Expression of the adaptation policy Limits of explicit event-based rules Need for more sophisticated (intelligent?) policies –Smoothing measures of resource availability –Balancing instabilities

22. Future work Collaborative adaptation of components –Control side-effects Avoid adaptation cycles –Common policy at the level of: A group of components A composite The whole application –Consider full Grid applications Not only their components

23. Dynamic adaptation of parallel codes Toward self-adaptable components for the Grid