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Key Objectives Parallel Programming Model and Tools

desesperatly needed for the masses (New Scientist, New SME) for new architectures (Multi-cores)

As Effective as possible: Efficient However Programmer/User Productivity is first Key

For both Multi-cores and Distributed Actually the way around

Some Handling of ``Large-scale’’ (Grid, Clouds)

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Intech, Jeudi 2 juillet 2009

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D. Caromel, et al.

Overview of Cloud, Parallel Computingand ProActive PACA Grid

Speed: Application + Development: Productivity

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1. Background: OASIS team2. Cloud Computing3. ProActive Parallel Suite: Programming, Optimizing Scheduling4. CPER ProActive PACA GRID5. Use Cases & Demos

Agenda

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1. BackgroundParallel & Distributed

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OASIS Team, INRIA-UNSA-I3S/CNRS A joint team, about 35 persons Parallelism and Distribution, Proof, Verification ProActive Parallel Suite

From Multi-cores to Enterprise GRIDs

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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): Sylvie Lelaidier (INRIA) Sandra Devauchelle (I3S)

An international team with about 10 nationalities

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2. Cloud Computing

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Clouds: Basic Definition

Dynamically scalable, often virtualized resources Provided as a service over the Internet Users need not have knowledge of, expertise in,

or control over the technology infrastructure

Software as a service (SaaS), CRM, ERP Platform as a service (PaaS), Google App Engine Infrastructure as a service (IaaS), Amazon EC2

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Clouds in Picture

From Joseph Kent Langley

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From Grids to Clouds

Grid Computing Several administrative Domains Virtual Organizations Trading not based on Currency

(Too) Hard Still a strong need for Sharing:

Under used machines, Green IT pressure TCO, Electric Bill Services: Accessing a Hosted Software

Distributed, //, & Grid Technologies for Clouds

Multi-Core Push

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Symetrical Multi-Core: 8-ways Niagara II

8 cores 4 Native

threads per core

Linux see 32 cores!

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Multi-Cores: A Few Key Points Moore’s Law rephrased:

Nb. of Cores double every 18 to 24 months

Key expected Milestones: Cores per Chips (OTS) 2010: 32 to 64 2012: 64 to 128 2014: 128 to 256

1 Million Cores Parallel Machines in 2012 100 M cores coming in 2020

Multi-Cores are NUMA, and turning Heterogeneous (GPU) They are turning into SoC with NoC: NOT SMP!

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ProActive PACA Grid in Cloud Context

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3. ProActive Parallel Suite

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Parallel Acceleration Toolkit in Java:

Parallelism:

Multi-Core+Distributed

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2222

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!

2323

Standard system at Runtime: No Sharing

NoC: Network On ChipProofs of Determinism

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TYPED ASYNCHRONOUS GROUPS

2525

Broadcast and Scatter

JVM

JVM

JVM

JVM

agcg

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

c1 c2 c3c1 c2 c3

c1 c2 c3c1 c2 c3c1 c2 c3

c1 c2 c3

s

c1 c2 c3

s

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

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Optimizing

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IC2D

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IC2D

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ChartIt

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Pies for Analysis and Optimization

Video 1: IC2D OptimizingMonitoring, Debugging, Optimizing

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Scheduling

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Scheduler: User Interface

Video 2:Scheduler, Resource Manager

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4. ProActive PACA GRID

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The ProActive PACA Grid Platform (1)

Dell Blades 160 cores DELL PowerEdge LAME

BLADE 19552 Intel Xeon E5335 2.0 Ghz quad core 2×4 Mo16GB 667MHZ FBD2 hdd 73Go SAS 15Krpm

Linux fedora Core 7Kernel 2.6.23.17-88

Storage server: Dell PowerEdge P29502 Intel Xeon E5345 2.33 Ghz quad core 2×4 Mo6×500Go SATA 7.2Krpm RAID0

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The ProActive PACA Grid Platform (2)

HP HPCS Windows 64 cores

8 nodes :HP ProLiant BL460c2 Intel Xeon E5320 quad core 1.86 GHZ 8 Mo8GB 667MHZ FBD2 hdd 72Go hot plug 10Krpm RAID 0

Windows HPC 2008 64 bits

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The ProActive PACA Grid Platform (3)

Dell Blades 384 cores

Total: 608 Cores available

Today

Potential Extension:Grid 5000

Video 3:CPER ProActive PACA Grid

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Use Cases & Demos Downstairs AGOS, SOA, BPEL processes in parallel on the

Grid, Franca Perrina

Life technologies: Genomic, Transcriptome Parallel Analysis, IPMC, Emil Salageanu

Price-It Excel, Finance, Vladimir Bodnartchouk

IC2D : An Eclipse GUI to Debug and Optimize your ProActive Application, Brian Amedro

CPER ProActive Paca Grid, Germain Sigety Web Start for accessing ProActive Paca Grid,

Florin Alexandru-Bratu

Pro

Act

ive

PAC

A G

RID

Vis

u,D

ebug

3 D

emos

App

licat

ives

Summary

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Conclusion: Available in PACA Grid

Future Developments:Multi-Core + Distributed

The Future

ProActive PACA Grid + Coeur Interactive + Mesocentre (OCA) + Clouds

For: Science Labs and Local Industries (Large and SME)

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Intech, Jeudi 2 juillet 2009

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ProActive PACA Grid in Context

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4. Use Case: IPMC

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Use case: SOLiD and ProActive SOLiD from Applied Biosystems (USA)

As part of a project with the IPMC research institute, the SOLiD Corona Lite software has been upgraded by integrating ProActive to enable the distribution of parallel tasks on lab desktops in order to accelerate the processing

At the moment, only the first pipeline, Matching, has been upgraded by distributing the Mapreads function

Constraints Requirements set by IPMC: keep the current

software architecture ProActive has been integrated on top of PBS

Matching

Pairing

SNP/Consensus calling

ProActive

PBS

Resource Manager

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Resources set upEnvironment

16 nodes

Additional external nodes can be easily and dynamically added!

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Mapreads optimization

The Reads are split into smaller files

Each Reads subset is compared to one chromosome

The resulting files are merged

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Optimized Mapreads Performances The distributed version with ProActive of Mapreads has been tested on the INRIA cluster

with two settings: the Reads file is split in either 30 or 10 slices

Use case: matching 31 millions sequences with the human genome (M=2, L=25)

Reference point with 16 cores(same as in SOLiD machine)

4 Time faster from 20 to 100Speed Up of

80 / Th. Sequential50 Hours 35 Minutes

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Key benefits of this solution Higher throughput

Reduced execution time

Scalable Depending on the input and reference data size, the user can chose to increase or reduce

the number of extra resources used Solution is ready for next generation reads file

Flexible The run can be paused or resumed by the user when needed Priorities between jobs can be easily set by the users Easy nodes acquisition and hot plugging

Simplified maintenance ProActive directly supports common schedulers like PBS, LSF, SGE, and W HPCS 08:

time consuming adaptations of Corona Lite software are no longer needed

Reduced costs for Applied Biosystems customers Optimizing available hardware resources Free use of ProActive Parallel Suite® Easy to install and use: save time

Supported by experts in parallel computing

Accelerate & Scale up withProActive Parallel Suite®

02/07/2009

Presentation overview

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Thank you!

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Co-developing, Support for ProActive Parallel Suite Worldwide Customers: Fr, UK, Boston USA

Startup Company Born of INRIA & UNSA