Simulation in a Distributed Computing Environment S. Guatelli1, P. Mendez Lorenzo2, J. Moscicki2, M.G. Pia1 1INFN Genova, Italy 2CERN, Geneva, Switzerland IEEE Nuclear Science Symposium San Diego, 30 October – 4 November 2006

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 Typical use cases Semi-interactive response Detector design Optimisation Oncological radiotherapy Very long execution time High statistics simulation High precision simulation Fast simulation Variance reduction techniques (event biasing) Inverse Monte Carlo methods Parallelisation Methods for faster simulation response

Requirements Architectural requirements Transparent execution in sequential/parallel mode Transparent execution on a PC farm and on the Grid Semi-interactive simulation High statistics simulation e.g.Geant4 brachytherapy Execution time for 20 M events: 5 hours Goal: execution time ~ few minutes e.g. Geant4 medical_linac Execution time for 109 events: ~10 days Goal: execution time ~ few hours Reference: sequential mode on a Pentium IV, 3 GHz

Features of this study Geant4 in a distributed computing environment Architecture Implications on Geant4 simulation applications Environments PC farm GRID Real life use case Geant4 brachytherapy Advanced Example Test on a single CPU Test on a dedicated farm (60 CPUs) Test on a farm shared with other users Test on the GRID (LCG)

Parallel simulation execution: 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 local cluster Computing resources are used for parallel execution Input data for the job must be available on site Typically there is a shared file system and a queuing system Network is fast GRID computing: 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 local cluster

architectural pattern Strategy to minimise the cost of migrating a Geant4 simulation to a distributed environment Master-Worker architectural pattern DIANE http://cern.ch/DIANE DIANE is a layer which provides applications with a easy and convinient way of execution in the distributed, cluster environment. THE DESIGN GOALS: - easy customization and adaptability to different needs - hide details of underlying technology (allow for easy migration) - location independant – accessible from anywhere in the network - limited to Master-Worker model which covers most of the typical jobs and needs in HEP The Geant4 application developer is shielded from the complexity of underlying technology via DIANE

Distributed Simulation Interface class which binds together Geant4 application and Master-Worker framework UML Deployment Diagram for Geant4 applications Original Geant4 application source code unmodified G4Simulation class responsible of managing the simulation random number seeds Geant4 initialisation termination

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) Responsibilities Master steers the execution of job, splits the job and merges the results Worker initializes the Geant4 application and executes macros Client gets the results

Overhead at initialisation/termination Test on a single dedicated CPU (Intel ®, Pentium IV, 3.00 GHz) Study execution via DIANE w.r.t. sequential execution run 1 event 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 Overhead: ~ 5 s, negligible in a high statistics job

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

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 Worker number Time (seconds) Worker number Time (seconds) Example of a job that can be improved from a performance point of view Example of a good job balancing

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

Results in real life use case Required production of Brachytherapy: 20 M events 20 M events in sequential mode:16646 s (~ 4h 38’) on an Intel ® Pentium IV, 3.00 GHz The same simulation runs in 5’ in parallel on 56 CPUs appropriate for clinical usage

How the GRID load changes Execution time of Brachytherapy in two different conditions of the GRID Worker number Time (seconds) Very different result! 20 M events, 60 workers initialized, 360 tasks The load of the GRID changes quickly in time The conditions of the test are not reproducible

Test results (LCG) with/without DIANE Execution on the GRID, without DIANE Execution on the GRID through DIANE, 20 M events,180 tasks, 30 workers Worker number Time (seconds) Without DIANE: 2 jobs not successful due to set-up problems of workers Through DIANE: - All the tasks are executed successfully on 22 workers

Farm/GRID execution Preliminary indication Brachytherapy application, 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

Lessons learned DIANE as intermediate layer Load balancing 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 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 for critical usage yet Work in progress, details still to be understood quantitatively

Conclusions General solution for 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 results on-going work to understand details Acknowledgments to: M. Lamanna, L. Moneta, A. Pfeiffer (CERN) LCG teams at CERN Hurng-Chun Lee (ASGC, Taiwan) Regional Operation Centre Team of Taiwan