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Maria Grazia Pia Simulation in a Distributed Computing Environment Simulation in a Distributed Computing Environment S. Guatelli 1, A. Mantero 1, P. Mendez.

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Presentation on theme: "Maria Grazia Pia Simulation in a Distributed Computing Environment Simulation in a Distributed Computing Environment S. Guatelli 1, A. Mantero 1, P. Mendez."— Presentation transcript:

1 Maria Grazia Pia Simulation in a Distributed Computing Environment Simulation in a Distributed Computing Environment S. Guatelli 1, A. Mantero 1, P. Mendez Lorenzo 2, J. Moscicki 2, M.G. Pia 1 1 INFN Genova, Italy 2 CERN, Geneva, Switzerland CHEP 2006 Mumbai, 13-17 February 2006

2 Maria Grazia Pia Speed of Monte Carlo simulation Speed of execution is often a concern in Monte Carlo simulation Often a trade-off between precision of the simulation and speed of execution Fast simulation Variance reduction techniques (event biasing) Inverse Monte Carlo methods Parallelisation Methods for faster simulation response Semi-interactive response Detector design Optimisation Oncological radiotherapy Very long execution time High statistics simulation High precision simulation Typical use cases

3 Maria Grazia Pia Features of this study Geant4 application in a distributed computing environment –Architecture –Implications on simulation applications Environments –PC farm –GRID Two use cases: Geant4 Advanced Examples –semi-interactive response (brachytherapy) –high statistics (medical_linac) By-product:Geant4 medical application By-product: results for Geant4 medical application (technology transfer) Quantitative study –results to be submitted for publication

4 Maria Grazia Pia Requirements Transparent execution in sequential/parallel mode Transparent execution on a PC farm and on the Grid brachytherapy Geant4 brachytherapy Execution time for 20 M events: 5 hours Goal: execution time ~ few minutes Architectural requirements High statistics simulation Semi-interactive simulation medical_linac Geant4 medical_linac Execution time for 10 9 events: ~10 days Goal: execution time ~ few hours Reference: sequential mode on a Pentium IV, 3 GHz

5 Maria Grazia Pia Parallel mode: local cluster / GRID Both applications have the same computing model –a job consists of a number of independent tasks which may be executed in parallel –result of each task is a small data packet (few kb), which is merged as the job runs In a cluster –computing resources are used for parallel execution –user connects to a possibly remote cluster –input data for the job must be available on the site –typically there is a shared file system and a queuing system –network is fast GRID computing uses resources from multiple computing centres –typically there is no shared file system –(parts of) input data must be replicated in remote sites –network connection is slower than within a cluster

6 Maria Grazia Pia Overview Architectural issues –DIANE –How to dianize a Geant4 application Performance tests –On a single CPU –On clusters –On the GRID Conclusions –Lessons learned –Outlook Quantitative, documented results Publicly distributed: DIANE Geant4 application code

7 Maria Grazia Pia DIANE R&D project –started in 2001 in CERN/IT with very limited resources –collaboration with Geant4 groups at CERN, INFN, ESA –succesful prototypes running on LSF and EDG Parallel cluster processing – make fine tuning and customisation easy – transparently using GRID technology – application independent Developed by J. Moscicki, CERN/IT Master-Worker architectural pattern prototype for an intermediate layer between applications and the GRID Hide complex details of underlying technology

8 Maria Grazia Pia Practical example: Geant4 simulation with analysis Each task produces a file with histograms The job result is the sum of histograms produced by tasks Master-worker model – client starts a job – workers perform tasks and produce histograms – master integrates the results Distributed Processing for Geant4 Applications –task = N events –job = M tasks –tasks may be executed in parallel –tasks produce histograms/ntuples –task output is automatically combined (add histograms, append ntuples) Master-Worker Model –Master steers the execution of job, automatically splits the job and merges the results –Worker initializes the Geant4 application and executes macros –Client gets the results

9 Maria Grazia Pia UML Deployment Diagram for Geant4 applications Completely transparent to the user: same Geant4 application code G4Simulation class is responsible of managing the simulation –manage random number seeds –Geant4 initialisation –macros to be executed in batch mode –termination simulation with DIANE

10 Maria Grazia Pia Development costs Strategy to minimise the cost of migrating a Geant4 simulation to a distributed environment DIANE Active Workflow framework –provides automatic communication/synchronization mechanisms –application is glued to the framework using a small Python module –in most cases no code changes to the original application are required –load balancing and error recovery policies may be plugged in form of simple python functions Transparent adaptation for Clusters/GRIDs, shared/local file systems, shared/private queues Development/modification of application code –original source code unmodified –addition of an interface class which binds together application and M-W framework The application developer is shielded from the complexity of underlying technology via DIANE

11 Maria Grazia Pia Test results Performance of the execution of the dianized Brachytherapy example Test on a single CPU Test on a dedicated farm (60 CPUs) Test on a farm shared with other users (LSF, CERN) Test on the GRID (LCG) Tools and libraries: Simulation toolkit: Geant4 7.0.p01 Analysis tools: AIDA 3.2.1 and PI 1.3.3 DIANE: DIANE 1.4.2 CLHEP: G4EMLOW 2.3

12 Maria Grazia Pia Overhead at initialisation/termination Standalone application 4.6 0.2 s Application via DIANE, simulation only 8.8 0.8 s Application via DIANE, with analysis integration 9.5 0.5 s Test on a single dedicated CPU (Intel ®, Pentium IV, 3.00 GHz) Study execution via DIANE w.r.t. sequential execution – run 1 event Overhead: ~ 5 s, negligible in a high statistics job

13 Maria Grazia Pia Overhead due to DIANE with respect to the number of events Test on a single dedicated CPU (Intel ®, Pentium IV, 3.00 GHz) Study execution via DIANE w.r.t. sequential execution Ratio = Execution time vs. number of events in the job The overhead of DIANE is negligible in high statistics jobs

14 Maria Grazia Pia Farm: execution time and efficiency Dedicated farm : 30 identical bi-processors (Pentium IV, 3 GHz) –Thanks to Regional Operation Centre (ROC) Team, Taiwan –Thanks to Hurng-Chun Lee (Academia Sinica Grid Computing Center, Taiwan) Load balancing: optimisation of the number of tasks and workers

15 Maria Grazia Pia Optimizing the number of tasks The job ends when all the tasks are executed in the workers If the job is split into a higher number of tasks, the chance that the workers finish the tasks at the same time is a higher Note: the overall time of the job is determined by the last worker to finish the last task Example of a good job balancingExample of a job that can be improved from a performance point of view Worker number Time (seconds) Worker number Time (seconds)

16 Maria Grazia Pia Farm shared with other users Preliminary! Real-life case: farm shared with other users Execution in parallel mode on 5 workers of CERN LSF DIANE used as intermediate layer The load of the cluster changes quickly in time The conditions of the test are not reproducible Highly variable performance

17 Maria Grazia Pia Parallel execution in a PC farm Required production of Brachytherapy: 20 M events 20 M events in sequential mode : 16646 s (~ 4h and 38 minutes) on a a Intel ®, Pentium IV, 3.00 GHz The same simulation runs in 5 minutes in parallel on 56 CPUs –appropriate for clinical usage Similar results for Geant4 medical_linac Advanced Example –production can become compatible with usage for the verification of IMRT treatment planning –sequential execution requires ~ 10 days to obtain significant results

18 Maria Grazia Pia Running on the Grid (LCG) G4Brachy executed on the GRID (LCG) –nodes located in Spain, Russia, Italy, Germany, Switzerland Conditions of the test The load of the GRID changes quickly in time The conditions of the test are not reproducible Efficiency The evaluation of the efficiency with the same criterion as in a dedicated farm does not make much sense in this context Study the efficiency of DIANE as automated job management w.r.t. manual submission through simple scripts

19 Maria Grazia Pia Test results Execution on the GRID through DIANE, 20 M events,180 tasks, 30 workers Execution on the GRID, without DIANE Without DIANE: - 2 jobs not successfully executed due to set-up problems of the workers Through DIANE: - All the tasks are executed successfully on 22 workers - Not all the workers are initialized and used: on-going investigation Worker number Time (seconds) Worker number Time (seconds)

20 Maria Grazia Pia How the GRID load changes Execution time of Brachytherapy in two different conditions of the GRID DIANE used as intermediate layer Worker number Time (seconds) Worker number Time (seconds) 20 M events, 60 workers initialized, 360 tasks Very different result!

21 Maria Grazia Pia Farm/GRID execution Brachy, 20 M events, 180 tasks Taipei cluster: 29 machines, 734 s ~ 12 minutes GRID: 27 machines, 1517 s ~ 25 minutes Preliminary indication The conditions are not reproducible

22 Maria Grazia Pia Lessons learned DIANE as intermediate layer –Transparency –Good separation of the subsystems –Good management of CPU resources –Negligible overhead Load balancing –A relatively large number of tasks increases the efficiency of parallel execution in a farm –Trade-off between optimisation of task splitting and overhead introduced Controlled and real life situation is quite different in a farm –need dedicated farm for critical usage (i.e. hospital) Grid –highly variable environment –not mature yet for critical usage –automated management through a smart system is mandatory –work in progress, details still to be understood quantitatively

23 Maria Grazia Pia Outlook Work in progress –A quantitative analysis of the all the performance results is still on-going Generalize job splitting optimization Better characterize the performance on the Grid quantitatively Improve DIANE To be submitted for publication in IEEE Trans. Nucl. Sci.

24 Maria Grazia Pia Conclusions General approach to the execution of Geant4 simulation in a distributed computing environment –transparent sequential/parallel application –transparent execution on a local farm or on the Grid –user code is the same Quantitative, documented results –reference for users and for further improvement –on-going work to understand details Acknowledgments to: –M. Lamanna (CERN), Hurng-Chun Lee (ASGC, Taiwan), L. Moneta (CERN), A. Pfeiffer (CERN) –the LCG teams at CERN and the Regional Operation Centre Team of Taiwan –no support from INFN GRID team

25 Maria Grazia Pia IEEE Transactions on Nuclear Science Prime journal on technology in particle/nuclear physics Review process reorganized about one year ago Associate Editor dedicated to computing papers Various papers associated to CHEP 2004 published on IEEE TNS Papers associated to CHEP 2006 are welcome Manuscript submission: Papers submitted for publication will be subject to the regular review process Publications on refereed journals are beneficial not only to authors, but to the whole community of computing-oriented physicists Our hardware colleagues have better established publication habits… Further info:

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