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CloudMC: A cloud computingmap-reduce implementation     for radiotherapy Rubén Jiménez Marrufo Héctor Miras del Río Carlos...
ContentsIntroductionRadiotherapyMonte Carlo simulations for radiation transportMonte Carlo parallelizationClustering vs. C...
IntroductionHéctor Miras del RíoDepartment of Medical Physics,Virgen Macarena Hospital,Seville, SpainRubén Jiménez Marrufo...
Introduction       Monte Carlo       Simulations  CloudComputing       Radiotherapy
RadiotherapyRadiotherapy: is the medical useof ionizing radiation, generally aspart of cancer treatment to controlor kill ...
Monte Carlo simulations for       radiation transport
Monte Carlo simulations for       radiation transport
Monte Carlo simulation for                                         radiation transport Monte Carlo Simulations:+👍 Gold sta...
Monte Carlo parallelizationParallelization: Executesimultaneously onesimulation in several nodesand merge the results.Mont...
Parallelization: Clustering vs.             Cloud Computing
Cloud Computing for clinical                                                radiation calculations                   Numbe...
CloudMCCloudMC offers an implementation of map/reduce over Windows Azurecloud computing platform, for the parallelization ...
CloudMC: DEMO
CloudMC Architecture                                                  Service Management    UI Services              Provi...
CloudMC: MapReduceSequence of actions when carrying out a MC simulation on n instances:                           3. Paral...
CloudMC: Map    Most of MC applications for radiation transport simulation read the    configuration from textual files.  ...
CloudMC: Reduce  The result of MC applications for radiation transport simulation are  dose, energy or any magnitude distr...
CloudMC: MapReduce DSLCloudMC uses a MapReduce DSL to read parameters to adapt Mapperand Reducer to specific MC applicatio...
CloudMC: ElasticityUsers choose the number of instances to use for each simulation.CloudMC scales up worker role to run si...
CloudMC: How did Radarc help us?                                                                  Service Management      ...
CloudMC: ResultsCase Study:    Simulation: 125I seed in ophtalmic    applicator.    Number of histories: 3·109    MC Code:...
CloudMC: ResultsTime vs number of instances study                                    T(n): Clock time for 1 simulation in ...
CloudMC: Is it reinventing the wheel?Why not using Amazon Elastic MapReduce?(http://aws.amazon.com/es/elasticmapreduce) • ...
RoadmapTesting with more MC applications: Geant4, EGSnrc, etc.Support packages with specific MapReduce implementations • A...
Questions
Thank you for your attention …     CloudMC soon available at:https://cloudmontecarlo.cloudapp.net      hector.miras@gmail....
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CloudMC: A cloud computing map-reduce implementation for radiotherapy. RUBEN JIMENEZ & HECTOR MIRAS at Big Data Spain 2012

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Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/cloudMC-a-cloud-computing-map-reduce-implementation-for-radiotherapy/ruben-jimenez-and-hector-miras

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Transcript of "CloudMC: A cloud computing map-reduce implementation for radiotherapy. RUBEN JIMENEZ & HECTOR MIRAS at Big Data Spain 2012"

  1. 1. CloudMC: A cloud computingmap-reduce implementation for radiotherapy Rubén Jiménez Marrufo Héctor Miras del Río Carlos Miras del Río Carles Gomà Estadella Big Data Spain http://www.bigdataspain.org Madrid, November 16th, 2012
  2. 2. ContentsIntroductionRadiotherapyMonte Carlo simulations for radiation transportMonte Carlo parallelizationClustering vs. Cloud ComputingCloud Computing for clinical radiation transportCloudMC DEMO START Architecture Map Reduce Elasticity How did Radarc help us? Results Is it reinventing the wheel? Roadmap DEMO RESULTSQuestions & Answers
  3. 3. IntroductionHéctor Miras del RíoDepartment of Medical Physics,Virgen Macarena Hospital,Seville, SpainRubén Jiménez MarrufoR&D Division,Icinetic TIC S.L.,Seville, SpainCarlos Miras del RíoR&D Division,Wedoit Innovacion Tecnologica,Seville, SpainCarles GomàCentre for Proton Therapy,Paul Scherrer Institute,Villigen PSI, Switzerland
  4. 4. Introduction Monte Carlo Simulations CloudComputing Radiotherapy
  5. 5. RadiotherapyRadiotherapy: is the medical useof ionizing radiation, generally aspart of cancer treatment to controlor kill malignant cells. Radiotherapy treatment planning: is the process for calculating the radiation dose to be absorbed by an object to be irradiated, prior to radiotherapy.
  6. 6. Monte Carlo simulations for radiation transport
  7. 7. Monte Carlo simulations for radiation transport
  8. 8. Monte Carlo simulation for radiation transport Monte Carlo Simulations:+👍 Gold standard algorithms forradiation calculations- 👍 Extremely computationallyintensive and very time-consuming.
  9. 9. Monte Carlo parallelizationParallelization: Executesimultaneously onesimulation in several nodesand merge the results.Monte Carlo simulations arehighly parallelizable sincethe primary events areindependent.
  10. 10. Parallelization: Clustering vs. Cloud Computing
  11. 11. Cloud Computing for clinical radiation calculations NumbertCPU = instances 100 cores cluster ≈ 20 000 €100 h n = 100 160 years of computing time in Extra- T(n) = an extra-small instance small 1.44 h 0.0142 € / h 1000 Cost / plan patients / 2€ year
  12. 12. CloudMCCloudMC offers an implementation of map/reduce over Windows Azurecloud computing platform, for the parallelization of MC simulations ofradiation therapy dose distribution. Non-intrusive Multi-application:  Penelope  Geant4  EGSnrc Elasticity:  Resources are not reserved  1 hour simulation costs 1 hour
  13. 13. CloudMC: DEMO
  14. 14. CloudMC Architecture Service Management UI Services Provisioning MapReduce Entities Worker Roles Factory Repositories Cloud Hosted Services Users & SimulationSimulation Messages Queues filesSQL Azure Cloud Storage
  15. 15. CloudMC: MapReduceSequence of actions when carrying out a MC simulation on n instances: 3. Parallel Execution 4. Reduce 5. End 2. Map 1. New Simulation of Every worker role: simulation - When the web role reads the n 1. New - Generation end offromindependent messagesaof of n initial saved on Finished simulation metadata is Reads message simulation, 1.Simulation metadata is the queue and 3. Parallel 5. End of 2.- Map simulation seeds. on merges simulation files. Reduce Resolver SQL the the n results saveddownloads Azure. execution 4. simulation SQL Azure. 2.Mapper: tothe “fragmented” - Executes the storage. simulation uploaded Modification of simulation. confignotices tohistories by the end - Mail to divide the user of n. - Simulation files are uploaded to the 3. the simulation arenthe storage. -of Sends therolesthe proceed to n-1 worker results to worker roles. Provisioning of to scaled down. Azure Storage. of simulation” 4. Sends an “end -download themessages of “start”. Sending of n results. message.
  16. 16. CloudMC: Map Most of MC applications for radiation transport simulation read the configuration from textual files. Input A: Configuration Histories: 1015 Executable Files• Simulation parameters• Histories count• Geometry & materials files Mapper: parametrized mapper to set• … histories number and seeds in the input files• MapReduce Parameters Executable Executable Input B Executable Executable Mapped Histories: 215 Executable
  17. 17. CloudMC: Reduce The result of MC applications for radiation transport simulation are dose, energy or any magnitude distribution files formatted in columns. Executable Executable Executable Executable Executable Executable Dose Executable Mapped Executable distribution Executable filesReducer: parametrized reducer tocombine columns depending on thecolumn type:- Magnitude column Output- Uncertainty column
  18. 18. CloudMC: MapReduce DSLCloudMC uses a MapReduce DSL to read parameters to adapt Mapperand Reducer to specific MC applications. Mapper parameters Reducer parameters
  19. 19. CloudMC: ElasticityUsers choose the number of instances to use for each simulation.CloudMC scales up worker role to run simulation and scales downwhen it finishes.Windows Azure Service Management allows roles scaling: 👍 REST API 👍 Based on XML config files 👍 Minimum of 1 instance 👍 Impossible to scale down specific instances (Multi-tenant)
  20. 20. CloudMC: How did Radarc help us? Service Management UIFormula Azure Services Provisioning MapReduce Entities Worker Roles≃ 50% generated code: Factory Repositories • ASP.Net MVC 3 UI • C# App Services Cloud Hosted Services • C# POCO Entities • EF CodeFirst • SQL Azure DBFocus on domain core: Users & User Simulationmap/reduce, Simulation accounts Messages Queues filesprovisioning, faulttolerance, etc. SQL Azure Cloud Storage
  21. 21. CloudMC: ResultsCase Study: Simulation: 125I seed in ophtalmic applicator. Number of histories: 3·109 MC Code: PENELOPE, main program PenEasy.Results: Worker instances size: extra-small Clock time in 1 instance: 30 h Clock time in 64 instances: 48 min (speed up = 37x)
  22. 22. CloudMC: ResultsTime vs number of instances study T(n): Clock time for 1 simulation in n instances. tcpu: Overall time used only in the simulation of n histories. Dt0: Non-parallelizable time for 1 instance. a: Non-parallelizable part of time proportional to n.
  23. 23. CloudMC: Is it reinventing the wheel?Why not using Amazon Elastic MapReduce?(http://aws.amazon.com/es/elasticmapreduce) • Our mapper and reducer were written for .Nethttp://stackoverflow.com/questions/1190520/is-it-possible-to-write-map-reduce-jobs-for-amazon-elastic-mapreduce-using-netWhy not using Hadoop On Azure?(http://www.hadooponazure.com) • First preview released on 2012. • The cluster size must be reserved.
  24. 24. RoadmapTesting with more MC applications: Geant4, EGSnrc, etc.Support packages with specific MapReduce implementations • Application to different domains • Use of MEF to provide Mappers and Reducers in simulation packagesSDK to develop specific MapReduce implementation packages. • Visual Studio Templates could facilitate the development of CloudMC packagesEnable multi-tenant environments • Concurrent simulations require scaling down of specific instances that is not possible on Windows Azure.
  25. 25. Questions
  26. 26. Thank you for your attention … CloudMC soon available at:https://cloudmontecarlo.cloudapp.net hector.miras@gmail.com @hmiras rjimenez@icinetic.com @rjimenez
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