1. 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

2. AGIR – Paristic’06 – LORIA – 23 novembre 2006 2 www.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

3. AGIR – Paristic’06 – LORIA – 23 novembre 2006 3 www.aci-agir.org 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)

4. AGIR – Paristic’06 – LORIA – 23 novembre 2006 4 www.aci-agir.org 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 I 0 J 0 I 1 J 1 I 2 J 2 Set 0 Set 1 I 0 J 0 I 1 J 1 I 2 J 2 One-to-one All-to-all Service 1 Service 2 Service 3 Service 4 Input 1Input 2 Output 1

5. AGIR – Paristic’06 – LORIA – 23 novembre 2006 5 www.aci-agir.org 3 kinds of parallelism can be exploited: Data and service parallelism are intrinsic in task graphs: S2S2 S3S3 S1S1 D 0, D 1 Parallelism in service workflows S2S2 S3S3 S1S1 D 0, D 1 S2S2 S3S3 S1S1 S2S2 S3S3 D1D1 D0D0 D1D1 D0D0 Workflow parallelism Data parallelism Services parallelism D 1,S 1 D 1,S 2 D 1,S 3 D 0,S 1 D 0,S 2 D 0,S 3

6. AGIR – Paristic’06 – LORIA – 23 novembre 2006 6 www.aci-agir.org 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 I 0 J 0 I 1 J 1 I 2 J 2 One-to-one

7. AGIR – Paristic’06 – LORIA – 23 novembre 2006 7 www.aci-agir.org 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

8. AGIR – Paristic’06 – LORIA – 23 novembre 2006 8 www.aci-agir.org Grid execution workflow manager Input 0 Service B Output 0 Input 0 Input 1 Service A Output 0 Data 0 Img Ref 0 Data 1 Img Ref 1 Data 2 Img Ref 2 Img Ref 1 Img Ref 2 The workflow manager is isolated from the grid: Prototyping on Grid’5000 Production on EGEE Grid resources Grid interface

9. AGIR – Paristic’06 – LORIA – 23 novembre 2006 9 www.aci-agir.org 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

10. AGIR – Paristic’06 – LORIA – 23 novembre 2006 10 www.aci-agir.org Impact of the variability of the latency The variability of the latency leads to a factor 2 performance drop

11. AGIR – Paristic’06 – LORIA – 23 novembre 2006 11 www.aci-agir.org Job Grouping Experiments Medical imaging applicationSub-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

12. AGIR – Paristic’06 – LORIA – 23 novembre 2006 12 www.aci-agir.org The grouping rule Let A be a service of the workflow and {B 0,...B n } its children For grouping A and B i0 : no parallelism loss (1) & (2) –(1) B i0 is an ancestor of every B j –(2) Every ancestor of B i0 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 BjBj Bi0Bi0 A C B i0

13. AGIR – Paristic’06 – LORIA – 23 novembre 2006 13 www.aci-agir.org Performance results Speed-ups given by job grouping w.r.t classical wrapping:

14. AGIR – Paristic’06 – LORIA – 23 novembre 2006 14 www.aci-agir.org 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 S: S = max (R)+w/n i=1..n increases w.r.t n decreases w.r.t n

15. AGIR – Paristic’06 – LORIA – 23 novembre 2006 15 www.aci-agir.org Uniform distribution of R (a,b) Two behaviors of the expectation of the makespan are observed: w>(b-a) low variability of the latency w<(b-a) high variability of the latency Makespan (s) Number of submitted jobs n Total execution time (s)Makespan (s) Number of submitted jobs n

16. AGIR – Paristic’06 – LORIA – 23 novembre 2006 16 www.aci-agir.org 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(E H (n)) 30% drop in the number of submitted jobs with the improved strategy Improved – brute force strategy (seconds): n=0..30

17. AGIR – Paristic’06 – LORIA – 23 novembre 2006 17 www.aci-agir.org 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