Denis Caromel1

Denis.Caromel@inria.fr

A Strong Programming ModelBridging Distributed and Multi-Core Computing

1. Background: INRIA, Univ. Nice, OASIS Team2. Programming: Parallel Programming Models:

a) Asynchronous Active Objects, Futures, Typed Groupsb) High-Level Abstractions (OO SPMD, Comp., Skeleton)

3. Optimizing4. Deploying

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1. Background and Team at INRIA

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OASIS Team & INRIA

Computer Science and Control

• 8 Centers all over France

• Workforce: 3 800

•Strong in standardization committees:– IETF, W3C, ETSI, …

• Strong Industrial Partnerships

• Foster company foundation:

90 startups so far

- Ilog (Nasdaq, Euronext)

- …

- ActiveEon

A joint team between: INRIA, Nice Univ. CNRS

•Participation to EU projects:–CoreGrid, EchoGrid, Bionets, SOA4ALL–GridCOMP (Scientific Coordinator)

•ProActive 4.0.1: Distributed and Parallel:

From Multi-cores to Enterprise+Science GRIDs

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Startup Company Born of INRIACo-developing, Providing support for Open Source ProActive Parallel Suite

Worldwide Customers (EU, Boston USA, etc.)

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OASIS Team Composition (35)Researchers (5):

D. Caromel (UNSA, Det. INRIA)E. Madelaine (INRIA)F. Baude (UNSA)F. Huet (UNSA)L. Henrio (CNRS)

PhDs (11): Antonio Cansado (INRIA, Conicyt)Brian Amedro (SCS-Agos) Cristian Ruz (INRIA, Conicyt) Elton Mathias (INRIA-Cordi) Imen Filali (SCS-Agos / FP7 SOA4All) Marcela Rivera (INRIA, Conicyt) Muhammad Khan (STIC-Asia) Paul Naoumenko (INRIA/Région

PACA) Viet Dung Doan (FP6 Bionets) Virginie Contes (SOA4ALL)Guilherme Pezzi (AGOS, CIFRE SCP)

+ Visitors + Interns

PostDoc (1):Regis Gascon (INRIA)

Engineers (10): Elaine Isnard (AGOS)

Fabien Viale (ANR OMD2, Renault )

Franca Perrina (AGOS)

Germain Sigety (INRIA)

Yu Feng (ETSI, FP6 EchoGrid)

Bastien Sauvan (ADT Galaxy)

Florin-Alexandru.Bratu (INRIA CPER)

Igor Smirnov (Microsoft)

Fabrice Fontenoy (AGOS)

Open position (Thales)

Trainee (2):Etienne Vallette d’Osia (Master 2 ISI)

Laurent Vanni (Master 2 ISI)

Assistants (2): Patricia Maleyran (INRIA)

Sandra Devauchelle (I3S)

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ProActive Contributors

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ProActive Parallel Suite:

Architecture

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ProActive Parallel Suite

9

Physical InfrastructurePhysical Infrastructure

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ProActive Parallel Suite

10

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2. Programming Modelsfor

Parallel & Distributed

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ProActive Parallel Suite

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ProActive Parallel Suite

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Distributed and Parallel

Active Objects

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A

ProActive : Active objects

Proxy

Java Object

A ag = newActive (“A”, […], VirtualNode)V v1 = ag.foo (param);V v2 = ag.bar (param);...v1.bar(); //Wait-By-Necessity

V

Wait-By-Necessity

is a

Dataflow

Synchronization

JVM

A

JVM

Active Object

Future Object Request

Req. Queue

Thread

v1v2 ag

WBN!

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First-Class Futures

Update

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Wait-By-Necessity: First Class Futures

ba

Futures are Global Single-Assignment Variables

V= b.bar ()

c

c

c.gee (V)

v

v

b

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Wait-By-Necessity: Eager Forward Based

ba

AO forwarding a future: will have to forward its value

V= b.bar ()

c

c

c.gee (V)

v

v

b

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Wait-By-Necessity: Eager Message Based

ba

AO receiving a future: send a message

V= b.bar ()

c

c

c.gee (V)

v

v

b

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Standard system at Runtime:No Sharing

NoC: Network On ChipProofs of Determinism

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(2) ASP: Asynchronous Sequential Processes

ASP Confluence and Determinacy

Future updates can occur at any time

Execution characterized by the order of request senders

Determinacy of programs communicating over trees, …

A strong guide for implementation,

Fault-Tolerance and checkpointing, Model-Checking, …

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No Sharing even for Multi-CoresRelated Talks at PDP 2009

SS6 Session, today at 16:00

Impact of the Memory Hierarchy on Shared Memory Architectures

in Multicore Programming Models

Rosa M. Badia, Josep M. Perez, Eduard Ayguade, and Jesus Labarta

Realities of Multi-Core CPU Chips and Memory Contention

David P. Barker

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TYPED

ASYNCHRONOUS

GROUPS

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A

Creating AO and Groups

Typed Group Java or Active Object

A ag = newActiveGroup (“A”, […], VirtualNode)V v = ag.foo(param);...v.bar(); //Wait-by-necessity

V

Group, Type, and Asynchrony

are crucial for Composition

JVM

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Broadcast and Scatter

JVM

JVM

JVM

JVM

agcg

ag.bar(cg); // broadcast cgProActive.setScatterGroup(cg);ag.bar(cg); // scatter cg

c1 c2c3c1 c2c3

c1 c2c3c1 c2c3

c1 c2c3

c1 c2c3

s

c1 c2c3

s

Broadcast is the default behavior Use a group as parameter, Scattered depends on rankings

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Static Dispatch Group

JVM

JVM

JVM

JVM

agcg

c1

c2

c3

c4

c5

c6

c7

c8c0

c9c1

c2

c3

c4

c5

c6

c7

c8c0

c9

c1

c2

c3

c4

c5

c6

c7

c8c0

c9

Slowest

Fastestemptyqueue

ag.bar(cg);

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Dynamic Dispatch Group

JVM

JVM

JVM

JVM

agcg

c1

c2

c3

c4

c5

c6

c7

c8c0

c9c1

c2

c3

c4

c5

c6

c7

c8c0

c9

c1

c2

c3

c4

c5

c6

c7

c8c0

c9

Slowest

Fastest

ag.bar(cg);

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Handling Group Failures (2)

JVM

JVM

JVM

JVM

agvg

V vg = ag.foo (param);Group groupV = PAG.getGroup(vg);el = groupV.getExceptionList();...vg.gee();

failure

Except.

Except.List

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Abstractions for Parallelism

The right Tool to execute the Task

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Object-Oriented SPMD

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OO SPMD A ag = newSPMDGroup (“A”, […], VirtualNode)

// In each member myGroup.barrier (“2D”); // Global Barrier myGroup.barrier (“vertical”); // Any Barrier myGroup.barrier (“north”,”south”,“east”,“west”);

A

Still,

not based on raw messages, but

Typed Method Calls

==> Components

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Object-Oriented SPMDSingle Program Multiple Data

MotivationUse Enterprise technology (Java, Eclipse, etc.) for Parallel Computing

Able to express in Java MPI’s Collective Communications:

broadcast reduce

scatter allscatter

gather allgatherTogether with

Barriers, Topologies.

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MPI Communication primitivesFor some (historical) reasons, MPI has many com. Primitives:

MPI_Send Std MPI_Recv Receive

MPI_Ssend Synchronous MPI_Irecv Immediate

MPI_Bsend Buffer … (any) source, (any) tag,

MPI_Rsend Ready

MPI_Isend Immediate, async/future

MPI_Ibsend, …

I’d rather put the burden on the implementation, not the Programmers !

How to do adaptive implementation in that context ?

Not talking about:the combinatory that occurs between send and receive

the semantic problems that occur in distributed implementations

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Application Semantics rather than

Low-Level Architecture-Based OptimizationMPI:

MPI_Send MPI_Recv MPI_Ssend MPI_Irecv MPI_Bsend MPI_Rsend MPI_Isend MPI_Ibsend

What we propose: High-level Information from Application Programmer (Experimented on 3D ElectroMagnetism, and Nasa Benchmarks)

Examples: ro.foo ( ForgetOnSend (params) );ActiveObject.exchange(…params );

Optimizations for Both Distributed & Multi-Core

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NAS Parallel Benchmarks

Designed by NASA to evaluate benefits of high performance systems

Strongly based on CFD

5 benchmarks (kernels) to test different aspects of a system

2 categories or focus variations:

• communication intensive and

• computation intensive

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Communication IntensiveCG Kernel (Conjugate Gradient)

Floating point operations

Eigen value computation

High number of unstructured communications

• 12000 calls/node

• 570 MB sent/node

• 1 min 32

• 65 % comms/WT

Message density distribution Data density distribution

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Communication IntensiveCG Kernel (Conjugate Gradient)

Comparable Performances

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Communication IntensiveMG Kernel (Multi Grid)

Floating point operations

Solving Poisson problem

Structured communications

• 600 calls/node

• 45 MB sent

• 1 min 32

• 80 % comms

Message density distribution Data density distribution

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Communication IntensiveMG Kernel (Multi Grid)

Pb. With high-ratecommunications

2D 3D matrixaccess

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Computation IntensiveEP Kernel (Embarrassingly Parallel)

Random numbers

generation

Almost no

communications

This is Java!!!

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Related Talk at PDP 2009

T4 Session, today at 14:00

NPB-MPJ: NAS Parallel Benchmarks Implementation for

Message Passing in Java

Damián A. Mallón, Guillermo L. Taboada, Juan Touriño, Ramón Doallo

Univ. Coruña, Spain

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Parallel Components

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GridCOMP Partners

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Objects to Distributed Components

Typed Group Java or Active Object

V

AExample of

component

instance

JVM

Truly

Distributed

Components

IoC:InversionOf Control(set in XML)

04/19/23

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GCMScopes and Objectives:

Grid Codes that Compose and Deploy

No programming, No Scripting, … No Pain

Innovation:

Abstract Deployment

Composite Components

Multicast and GatherCast

MultiCastMultiCast GatherCastGatherCast

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Optimizing MxN Operations

2+ composites can be involved in the Gather-

multicast

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Related Talk at PDP 2009

T1 Session, Yesterday at 11:30

Towards Hierarchical Management of Autonomic Components:

a Case Study

Marco Aldinucci, Marco Danelutto, and Peter Kilpatrick

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Skeleton

04/19/23

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Algorithmic Skeletons for ParallelismHigh Level Programming Model [Cole89]

Hides the complexity of parallel/distributed programming

Exploits nestable parallelism patterns

Task

Parallelism Patterns

for

farm

pipeif

while

mapfork

divide & conquerData

fork

seq(f1) seq(f

2)

pipe

seq(f3)

d&c(fb,f

d,f

c)

BLAST Skeleton Program

04/19/23

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Algorithmic Skeletons for ParallelismHigh Level Programming Model [Cole89]

Hides the complexity of parallel/distributed programming

Exploits nestable parallelism patterns

Task

Parallelism Patterns

for

farm

pipeif

while

mapfork

divide & conquerData

fork

seq(f1) seq(f

2)

pipe

seq(f3)

d&c(fb,f

d,f

c)

BLAST Skeleton Program

public boolean condition(BlastParams param){

File file = param.dbFile;

return file.length() > param.maxDBSize;}

public boolean condition(BlastParams param){

File file = param.dbFile;

return file.length() > param.maxDBSize;}

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3. Optimizing

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Programming & OptimizingMonitoring, Debugging, Optimizing

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Optimizing User Interface

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IC2D

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ChartIt

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Pies for Analysis and Optimization

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Video 1: IC2D OptimizingMonitoring, Debugging, Optimizing

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4. Deploying &

Scheduling

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Deploying

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Deploy on Various Kinds of Infrastructures

Internet

Clusters

ParallelMachine

LargeEquipment

Internet

Job management forembarrassingly parallel application (e.g. SETI)

Internet

Internet

Servlets EJBs Databases

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GCM StandardizationFractal Based Grid Component Model

4 Standards:1. GCM Interoperability Deployment2. GCM Application Description 3. GCM Fractal ADL4. GCM Management API

Overall, the standardization is supported by industrials: BT, FT-Orange, Nokia-Siemens, Telefonica,

NEC, Alcatel-Lucent, Huawei …

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Protocols and Scheduler inGCM Deployment Standard

Protocols:Rsh, sshOarsh, Gsissh

Scheduler, and Grids:GroupSSH, GroupRSH, GroupOARSHARC (NorduGrid), CGSP China Grid, EEGE gLITE, Fura/InnerGrid (GridSystem Inc.)GLOBUS, GridBusIBM Load Leveler, LSF, Microsoft CCS (Windows HPC Server 2008)Sun Grid Engine, OAR, PBS / Torque, PRUN

Soon available in stable release:Java EE

Amazon EC2

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Abstract Deployment Model

ProblemDifficulties and lack of flexibility in deployment Avoid scripting for configuration, getting nodes, connecting…

662009

A key principle: Virtual Node (VN)

Abstract Away from source code:Machines names

Creation/Connection Protocols

Lookup and Registry Protocols

Interface with various protocols and infrastructures:Cluster: LSF, PBS, SGE , OAR and PRUN (custom protocols)

Intranet P2P, LAN: intranet protocols: rsh, rlogin, ssh

Grid: Globus, Web services, ssh, gsissh

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Resource Virtualization

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ApplicationApplication

Deployment DescriptorDeployment Descriptor

Mapping

ConnectionsConnections

NodesNodes

AcquisitionAcquisition

CreationCreation

Infrastructure

VN

Runtime structured entities: 1 VN --> n Nodes in m JVMs on k Hosts

2009

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Resource Virtualization

68

VN1

VN2

GCM XMLDeployment Descriptor

node

node

HostJVM

node

JVM

Host

node

JVM

2009

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Virtualization resources

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VN1

VN2

node

node

HostJVM

node

JVM

Host

node

JVM

2009

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Multiple Deployments

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One Host Local Grid Distributed Grids

Internet

2009

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SchedulingMode

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Programming with flows of tasks

Program an application as an ordered set of tasksLogical flow : Tasks execution are orchestrated

Data flow : Results are forwarded from ancestor tasks to children as parameter

The task is the smallest execution unit

Two types of tasks: - Standard Java

- Native, i.e. any third party application (binary, scripts, etc.)

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Task 1(input 1) Task 2(input 2)

Task 3(res1,res2)

res1 res2

2009

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Defining and running jobs with ProActiveA workflow application is a job

a set of tasks which can be executed according to a

dependency tree

Rely on ProActive Scheduler

Java or XML interface

Dynamic job creation in Java

Static description in XML

Task failures are handled by the ProActive Scheduler

A task can be automatically re-started or not (with a user-defined bound)

Dependant tasks can be aborted or not

The finished job contains the cause exceptions as results if any

732009

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Scheduler / Resource Manager Overview

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• Multi-platform Graphical Client (RCP)

• File-based or LDAP authentication

• Static Workflow Job Scheduling, Native and Java tasks, Retry on Error, Priority Policy, Configuration Scripts,…

• Dynamic and Static node sources, Resource Selection by script, Monitoring and Control GUI,…

• ProActive Deployment capabilities : Desktops, Clusters, ProActive P2P,…

2009

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Scheduler: User Interface

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Scheduler: User Interface

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Video 2:

Scheduling Scheduler and Resource Manager:

See the video athttp://proactive.inria.fr/userfiles/media/video

s/Scheduler_RM_Short.mpg

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Summary

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Current Open Source Tools:

Acceleration Toolkit :Concurrency

+Parallelism +Distribution

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Conclusion

Applications:

3D Electromagnetism SPMD on 300 machines at once Groups of over 1000!Record: 4 000 Nodes

► Summary: Programming: OO

Asynchrony, First-Class Futures, No SharingHigher-level Abstractions (SPMD, Skel., …)

Composing: Hierarchical Components Optimizing: IC2D Eclipse GUI Deploying: ssh, Globus, LSF, PBS, …, WS

Our next target: 10 000 Core Applications

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Conclusion: Why does it scale?

Thanks to a few key features:

Connection-less, RMI+JMS unified

Messages rather than long-living interactions

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Conclusion: Why does it Compose?

Thanks to a few key features:

Because it Scales: asynchrony !

Because it is Typed: RMI with interfaces !

First-Class Futures: No unstructured

Call Backs and Ports

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Perspectives for Parallelism & Distribution• A need for several, coherent, Programming Models for different applications:

– Actors (Functional Parallelism) + Active Objects + Futures– OO SPMD: optimizations away from low-level optimizations– Parallel Component: Codes and Synchronizations:

• MultiCast GatherCast: Capturing // Behavior at Interfaces! – Adaptive Parallel Skeletons– Event Processing (Reactive Systems)

Efficient Implementations are needed to prove Ideas!

Proofs of Programming Model Properties Needed for Scalability!

Our Community never had a greatest Future!

Thank You for your attention!

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Extra Material

Grid + SOA

J2EE + Grid

Amazon EC2 ProActive Image and Deployment

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Perspective:

SOA + Grid

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SOA Integration:Web Services, BPEL Workflow

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Active Objects as Web Services

Why ?

Access Active Objects from any language

How ?

HTTP Server

SOAP Engine (Axis)

Usage:

ProActive.exposeAsWebService();

ProActive.unExposeAsWebService();

JVM

Web Service Client

Web Services

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ProActive + Services + Workflows

Principles:3 kinds of Parallel Services

3. Domain Specific Parallel Services (e.g. Monte Carlo Pricing)

2. Typical Parallel Computing Services (Parameter Sweeping, D&C, …)

1. Basic Job Scheduling Services (parallel execution on the Grid)

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3 kinds of Parallel Services

3. Domain Specific ParallelServices: providing business functionalities executed inparallel

2. Parallelization services:typical parallel computingpatterns (ParameterSweeping, D&C, …)

1. Job Scheduling service: Schedule and Run jobs in parallel on the Grid.

Parallel ServicesParallel Services Operational ServicesOperational Services…

1. Job Scheduling

Service

2. ParameterSweeping

Service

2. Divide & Conquer Service

OtherOperational

Service

…

Domain Specific Parallel Service

…Other

Basic Service

High level Business Process

GridGrid

3. Domain Specific Parallel

Service

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A sample pattern: Parameter Sweeping

I1I2

…In

O1O2

…On

ExecLogic

parameter sweeping

I1I2…In

O1O2…On

Parameter SweepingService, customized with an Exec logic X

ParamSweeping

Service

Process using parameter sweeping service

…

All the running instances of the Exec logic X are executed on the grid as a whole

PA Scheduler &PA Scheduler &PA Resource ManagerPA Resource Manager

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Video

SOA Integration:Web Services, BPEL Workflow

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6. J2EE IntegrationFlorin Alexandru Bratu

OASIS Team - INRIA

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J2EE Integration with Parallelism + Grids/Clouds

Performing Grid & Cloud Computing

From & In

an Application Servers

1. Delegating heavy computations outsides J2EE Applications

2. Using Deployed J2EE Nodes as Computational Resources

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ProActive – J2EE Integration (1)

1. Delegating heavy computations outsides J2EE Applications

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ProActive – J2EE Integration (2)

2. Using Deployed J2EE Nodes as Computational Resources

Objective Being able to deploy active objects inside the JVMs of application servers

Implementation Based on a Sun standard – Java Connector Architecture JSR112 Deployment module: resource adapter (RAR) Works with all J2EE-compliant Application Servers

Integration(2)

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Grids & Clouds:

Amazon EC2 Deployment

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Big Picture + Clouds

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Clouds: ProActive Amazon EC2 Deployment

Principles & Achievements:

ProActive Amazon Images (AMI) on EC2

So far up to 128 EC2 Instances

(Indeed the maximum on the EC2 platform, … ready to try 4 000 AMI)

Seamless Deployment:

no application change, no scripting, no pain

Open the road to : In house Enterprise Cluster and Grid + Scale out on EC2

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ProActive Deployment onAmazon EC2

Video

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P2P:Programming

Modelson

Overlay Networks

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