Optimized Java computing as an application for Desktop Grid

1. Optimized Java computing as an application for Desktop Grid Olejnik Richard1, Bernard Toursel1, Marek Tudruj2, Eryk Laskowski2 1 Université des Sciences et Technologies de Lille Laboratoire d’Informatique Fondamentale de Lille (LIFL UMR CNRS 8022) {olejnik, toursel}@lifl.fr http://www.lifl.fr/PALOMA 2 Institute of Computer Science Polish Academy of Sciences Warsaw, Poland {tudruj, laskowsk}@ipipan.waw.pl

2. Components for Desktop Grid

3. Heterogeneous Applications • Each application is composed of one or more tasks. • Different tasks may have different computational needs. • There may be inter-task communication. • Each task can be assigned to a computer with any architecture.

4. Grid'5000 Grid'5000 is a research effort developing a large scale infrastructure for Grid research. 10 laboratories are involved, in the objective of providing the community of Grid researchers a testbed allowing experiments in all the software layers between the network protocols up to the applications. The current plans are to assemble a physical platform featuring 8 clusters, connected by the Renater Education and Research Networkat 1Gb/s (10 Gb/s is expected in future).

5. Objectives • Execution efficiency of parallel and distributed Java applications on Grid. • Transparent and optimized object placement with dynamic load balancing strategies (execution aspects). • Transparent control of parallelism and easy collecting of results (programming aspects).

6. What we propose • Single System Image (SSI)of clusters. • Special mechanisms at the middleware level: • Dynamic and automatic adaptation to variations of computation methods and execution platforms. • Special mechanisms at the programming environment level, which facilitate expression of parallelism and distribution. • Using Components in Grid environment

7. Issues • Issues in building distributed applications • Collaboration between different applications • Cross programming languages and platforms • Controlling parallel applications transparently • Managing software complexity and evolution • Reuse and sharing of existing scientific code • Encapsulation and modular construction

8. ADAJ Tools for expression of parallelism Load Balancing System Migration of active objects Transparency JavaParty Distant Access RMI DG-ADAJ Environment CCA Application Builder Control Components Framework Services JVM Portability JVM Grid working node Grid working node Network

9. CCADAJ Component Architectures • Control component library • Provides parallel/distributed control • Connects components in a parallel way • Data exchange between components: • Demand driven & Data driven • Event notification • Multiple level composition • CCADAJ features • CCA Compliant (Common Component Architecture http://www.cca-forum.org/) • Services • Instantiation, Connection • ConnectionEvent, ComponentEvent • Composition

10. CCA Features • Components and Ports • Components interact through interfaces (ports) • Components can provide ports: • implementation of port interfaces • the service a component offers • Components can use ports • Call of the method in the port • Capability the component needs to use • Connecting components through ”provides-uses” ports

11. GoPort Special port to “execute” a component Implements go() method, which starts execution of the component Framework search for go ports and uses them UsesPort (multiplierPort ,AdderPort) Call getPort to obtain port byservices Call method on port Ex: x=multiplierPort.getProduct(y,z); Connection uses/provides ports ”No uses”/”uses neither provides” /”provides” Ports connected by types Port types must match Port names are unique in a component Framework puts info about provider into user component’s service object AdderComponent AdderPort MultiplierComponent MultiplierPort StarterComponent GoPort AdderPort MultiplierPort CCA: Interactions between components connect StarterComponentMultiplierPortMultiplierComponentMultiplierPort connect MultiplierComponentAdderPortAdderComponentAdderPort

12. ProvidesPort SuperComponent UsesPort GoPort InnerComp2 InUses2 Some Additional Services InnerComponent InnerProvides InnerUses Composition (super-component) • Super component • Component • Incorporates ”provides”, ”uses”, and/or ”go” ports • Additional services (framework) • Instantiation of inner components • Mechanism for exposing inner component’s ports to the outside • Inner components activations • Inner components connection

13. The static optimization heuristics Before a Java program is executed on a GRID, an introductory optimization algorithm is performed, which will determine an initial distribution of program elements (objects) on Java Virtual Machines. The algorithm starts with creating a methoddependence graph of a Java program. In a MDG,methods are shown as nodes, their mutual calls are shown as edges.

14. The optimization algorithm • Execute programs for some representative data • Carry out the measurements of the number of mutual method calls and the number of new threadsspawnings • Store measurement results in a trace file • Create a method call graphwith the use of method dependency graph and trace file • Perform clustering and mapping phases: • in the clustering phase, the algorithm merges pairs of nodes from MCG if it leads to a reduction of the program execution time, • the mapping phase assigns clusters to the real physical JVMs with load balancing.

15. Method Call Graph (MCG)

16. Dynamic Objects Redistribution • Workload is computed as a function of the intensity of method invocations • Two-phasealgorithm is performed concurrently withapplication execution: • to detect an application distribution imbalance • to correct the imbalance in a distributed manner • Objectsfrom overloaded machines are transferred to the underloaded ones

17. Observation mechanism of relations between objects JVM global object + local object JVM global object global object inputInvocation (II) + outputGlobalInvocation (OGI) outputLocalInvocation (OLI) local object • Legend • invocation • sum local object local object JVM

18. Inter-Object Dynamic Relation Graph

19. Conclusions • Some optimization algorithms for distributed Java programs basedonanalysis of graph representations. • The optimization explores both static and dynamic dependencies between objects and their methods. • The proposed static analysis is used as a preliminary stage that is followed by a dynamic load balancing, which, in most cases, exploitsa little of static information coming from Java source code.

20. A dynamic load balancing mechanism is used, supported by three kinds of information: information about the computer load and performance, information about dynamic relation between objects and information deduced from code analysis. • Compile-time optimizations do not introduce any penalty in execution time of the program, and thus, they can use more sophisticated heuristics, which give better results.

21. Future Works • CCADAJ development • Add new functionality • Emphasis on parallel/distributed control components • Component deployment • Collaboration with other CCA-Compliant frameworks • Real problem solving