Analuse Globalisée des Données d ’Imagerie Radiologique Déploiement de workflows de traitement...

Analuse Globalisée des Données d ’Imagerie Radiologique

Déploiement de workflows de

traitement d’images médicales sur

une grille de calcul

Tristan Glatard - Johan Montagnat - Xavier Pennec

CNRS I3S / INRIA Sophia-Antipolis

AGIR – Paristic’06 – LORIA – 23 novembre 2006 2www.aci-agir.org

Application

• Application: statistical comparison of medical imaging

algorithms

• Constrains/needs:

– Sharing algorithms from different institutes

– Sharing the data between the algorithms

– Computing power

• Solutions:

– Grid execution to share data and answer computing needs

– Service Oriented Architecture to share algorithms

– Workflow of services to describe the application


Messages

Service Oriented Architectures (SOA)

• 3 basic roles:

• A service satisfies 3 properties:

(1) The interface of the service is platform-independent

(2) The service can be dynamically located and invoked

(3) A service does not call another service (loosely coupling)


Service-based workflow

• Graph description:

– Input/output of the application

– Data dependencies between services inputs/outputs

– Iteration strategy between services inputs

– Data synchronization barriers

• Instantiation on data at execution time

Set 0 Set 1

I0

J0

I1

J1

I2

J2

Set 0 Set 1

I0

J0

I1

J1

I2

J2

One-to-oneOne-to-one All-to-allAll-to-all

Service 1

Service 2

Service 3

Service 4

Input 1 Input 2

Output 1


• 3 kinds of parallelism can be exploited:

• Data and service parallelism are intrinsic in task graphs:

S2

S3

S1

D0, D

1

Parallelism in service workflows

S2

S3

S1

D0, D

1

S2

S3

S1

D0, D

1

S2

S3

D

1

D

0

D1

D0

Workflow parallelism Data parallelism Services parallelism

D1,S

1

D1,S

2D

1,S

3

D0,S

1

D0,S

2D

0,S

3


Iteration strategies in a parallel WF

• One-to-one operators assume ordered data set

• No problem if:

– Data parallelism is not present (order is preserved)

– Service parallelism is not present

• One to one operator in a data+service parallel execution:

– Keep track of the data graph

– Two data segments are composed iif they have a common

ancestor

– Groups have to be defined between the workflow inputs

Set 0 Set 1

I0

J0

I1

J1

I2

J2

One-to-oneOne-to-one


Data handling

• Data segments have to be stored within a tree:

• This data representation allows to:

– Retrieve results provenance

– Handle one-to-one iterations strategies if data segments are puzzled

Services representation Data representation


Grid execution

workflowmanager

Input 0

Service B

Output 0

Input 0 Input 1

Service A

Output 0

Data 0

Img Ref 0

Img Ref 0Img Ref 0 Img Ref 0

Img Ref 0Data 1

Img Ref 1

Img Ref 1

Img Ref 1Img Ref 1

Img Ref 1

Img Ref 1Img Ref 1

Img Ref 1Data 2

Img Ref 2

Img Ref 2Img Ref 1Img Ref 2Img Ref 2

Img Ref 2

• The workflow manager is isolated from the grid:

• Prototyping on Grid’5000

• Production on EGEE

Grid resourcesGrid

interface


Latency (s)

Grid5000-Grenoble (20 nodes) 0.48

Grid5000- Sophia (105 nodes) 8.25

EGEE-biomed VO (3000 nodes) 351.4

Performance analysis on EGEE

• Performance results are worse than expected:

– High latency: one measure comparing EGEE to clusters of G5K:

– Variable latencies among the jobs

• Model of the makespan of the application:

– The latency is modeled by a random variable (R)

on the services of the

critical path

on the data segments


Impact of the variability of the latency

• The variability of the latency leads to a factor 2

performance drop


Job Grouping Experiments

• Medical imaging application Sub-workflow– 6 services – 2 grouped pairs - 4 services – 3 grouped pairs

– 4 job submissions/input data set - 1 job submission/input data set

• Tested on 12, 66 and 126 input data sets


The grouping rule

• Let A be a service of the workflow and {B0,...B

n} its children

• For grouping A and Bi0: no parallelism loss <=> (1) & (2)

– (1) Bi0 is an ancestor of every B

j

– (2) Every ancestor of Bi0 is an ancestor of A (or A itself)

• No parallelism loss => (1) & (2)– ¬(1) => parallelism between B

j and B

i0 is broken

– ¬(2) => parallelism between A and C is broken

• (1) & (2) => no parallelism loss

• This rule is recursively applied on the workflow graph

A

Bj

Bi

0

A CBi

0


Performance results

• Speed-ups given by job grouping w.r.t classical

wrapping:


Grouping jobs of the same service

• Optimization the tasks granularity

• Trade-off between parallelism and probability to face

high latencies

• Model and notations:

– Total CPU time of the task to execute: w

– Split into n jobs

– Random latency: R

– Makespan :

= max (R)+w/ni=1..n

increases w.r.t n decreases w.r.t n


Uniform distribution of R (a,b)

• Two behaviors of the expectation of the makespan are

observed:

w>(b-a)

low variability of the latencyw<(b-a)

high variability of the latency

Mak

espa

n (s

)

Number of submitted jobs n

Tot

al e

xecu

tion

tim

e (s

)M

akes

pan

(s)

Number of submitted jobs n


Impact on the performances

• Experiment: submission of a 2000s task on EGEE

with two partitioning strategies:

– Brute force strategy: n is maximal (n=30)

– Improved strategy: ň=min(EH(n))

• 30% drop in the number of submitted jobs with the

improved strategy

• Improved – brute force strategy (seconds):

n=0..30


Conclusions

• Current work on the variability of the latency :

– Timeout optimization

– Dynamic estimation of the distribution of the latency on EGEE

• Implementation of MOTEUR available at:

http://www.i3s.unice.fr/~glatard

Analuse Globalisée des Données d ’Imagerie Radiologique Déploiement de workflows de traitement...

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