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

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

Transcript

1. CloudMC: A cloud computing map-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. Contents Introduction Radiotherapy Monte Carlo simulations for radiation transport Monte Carlo parallelization Clustering vs. Cloud Computing Cloud Computing for clinical radiation transport CloudMC DEMO START Architecture Map Reduce Elasticity How did Radarc help us? Results Is it reinventing the wheel? Roadmap DEMO RESULTS Questions & Answers
3. Introduction Héctor Miras del Río Department of Medical Physics, Virgen Macarena Hospital, Seville, Spain Rubén Jiménez Marrufo R&D Division, Icinetic TIC S.L., Seville, Spain Carlos Miras del Río R&D Division, Wedoit Innovacion Tecnologica, Seville, Spain Carles Gomà Centre for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
4. Introduction Monte Carlo Simulations Radiotherapy Cloud Computing
5. Radiotherapy Radiotherapy: is the medical use of ionizing radiation, generally as part of cancer treatment to control or 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. Monte Carlo simulations for radiation transport
7. Monte Carlo simulations for radiation transport
8. + Gold standard algorithms for radiation calculations - Extremely computationally intensive and very time- consuming. Monte Carlo simulation for radiation transport Monte Carlo Simulations:
9. Monte Carlo parallelization Parallelization: Execute simultaneously one simulation in several nodes and merge the results. Monte Carlo simulations are highly parallelizable since the primary events are independent.
10. Parallelization: Clustering vs. Cloud Computing
11. Cloud Computing for clinical radiation calculations 100 cores cluster ≈ 20 000 € Cost / plan 2 € tCPU = 100 h Number instances n = 100 T(n) = 1.44 h Extra- small 0.0142 € / h 1000 patients / year 160 years of computing time in an extra-small instance
12. CloudMC CloudMC offers an implementation of map/reduce over Windows Azure cloud computing platform, for the parallelization of MC simulations of radiation therapy dose distribution. Non-intrusive Multi-application:  Penelope  Geant4  EGSnrc Elasticity:  Resources are not reserved  1 hour simulation costs 1 hour
13. CloudMC: DEMO
14. CloudMC Architecture Worker Roles UI Service Management Simulation files Messages Queues Cloud Storage Cloud Hosted Services SQL Azure Users & Simulation Repositories Provisioning MapReduce Factory Entities Services
15. 1. New simulation 3. Parallel execution 4. Reduce 5. End of simulation 2. Map 5. End of Simulation - Finished simulation metadata is saved on SQL Azure. - Mail notices to the user of the end of the simulation to proceed to download the results. 2. Map - Generation of n initial independent seeds. - Mapper: Modification of simulation config to divide histories by n. - Provisioning of the n worker roles. - Sending of n messages of “start”. 1. New simulation - Simulation metadata is saved on SQL Azure. - Simulation files are uploaded to the Azure Storage. 4. Reduce - When the web role reads the n messages of end of simulation, Resolver merges the n results uploaded to the storage. - n-1 worker roles are scaled down. 3. Parallel Execution Every worker role: 1. Reads a message from the queue and downloads the simulation files. 2. Executes the “fragmented” simulation. 3. Sends the results to the storage. 4. Sends an “end of simulation” message. CloudMC: MapReduce Sequence of actions when carrying out a MC simulation on n instances:
16. CloudMC: Map Input A: Configuration Files • Simulation parameters • Histories count • Geometry & materials files • … • MapReduce Parameters Executable Histories: 1015 Input B Histories: 215 Executable Executable Executable Executable Mapped Executable Mapper: parametrized mapper to set histories number and seeds in the input files Most of MC applications for radiation transport simulation read the configuration from textual files.
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 Mapped Executable Executable Executable Executable Executable Dose distribution files Output Reducer: parametrized reducer to combine columns depending on the column type: - Magnitude column - Uncertainty column
18. CloudMC: MapReduce DSL CloudMC uses a MapReduce DSL to read parameters to adapt Mapper and Reducer to specific MC applications. Mapper parameters Reducer parameters
19. CloudMC: Elasticity Users choose the number of instances to use for each simulation. CloudMC scales up worker role to run simulation and scales down when 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. Worker Roles UI Service Management Simulation files Messages Queues User accounts Cloud Storage Cloud Hosted Services SQL Azure Users & Simulation Repositories Provisioning MapReduce Factory Entities Services Formula Azure ≃ 50% generated code: • ASP.Net MVC 3 UI • C# App Services • C# POCO Entities • EF CodeFirst • SQL Azure DB Focus on domain core: map/reduce, provisioning, fault tolerance, etc. CloudMC: How did Radarc help us?
21. CloudMC: Results Case 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. 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. CloudMC: Results Time vs number of instances study
23. CloudMC: Is it reinventing the wheel? http://stackoverflow.com/questions/1190520/is-it-possible-to-write-map- reduce-jobs-for-amazon-elastic-mapreduce-using-net Why not using Amazon Elastic MapReduce? (http://aws.amazon.com/es/elasticmapreduce) • Our mapper and reducer were written for .Net Why not using Hadoop On Azure? (http://www.hadooponazure.com) • First preview released on 2012. • The cluster size must be reserved.
24. Roadmap Testing 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 packages SDK to develop specific MapReduce implementation packages. • Visual Studio Templates could facilitate the development of CloudMC packages Enable multi-tenant environments • Concurrent simulations require scaling down of specific instances that is not possible on Windows Azure.
25. Questions
26. CloudMC soon available at: https://cloudmontecarlo.cloudapp.net Thank you for your attention … hector.miras@gmail.com @hmiras rjimenez@icinetic.com @rjimenez