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N ew a rchitectural design for no vel experimental domains

Nano5. N ew a rchitectural design for no vel experimental domains. Maria Grazia Pia INFN Genova INFN Commissione Nazionale V 17 settembre 2008. R&D on simulation methods, technology and architectural design for new experimental domains. Courtesy CMS Collaboration.

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N ew a rchitectural design for no vel experimental domains

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  1. Nano5 Newarchitectural design fornovel experimental domains Maria Grazia Pia INFN Genova INFN Commissione Nazionale V 17 settembre 2008 R&D on simulation methods, technology and architectural design for new experimental domains

  2. Courtesy CMS Collaboration Courtesy ATLAS Collaboration Courtesy H. Araujo and A. Howard, IC London Courtesy GATE Collaboration Courtesy R. Nartallo et al.,ESA Courtesy Borexino S. Agostinelli et al.GEANT4 - a simulation toolkitNIM A 506 (2003) 250-303 Most cited “Nuclear Science and Technology” publication! (>132000 papers) 3rd most cited INFN paper “Modern classic” Born from the requirements of large scale HEP experiments • Widely used also in • Space science and astrophysics • Medical physics, nuclear medicine • Radiation protection • Accelerator physics • Humanitarian projects, security • etc. Technology transfer to industry, hospitals… ZEPLIN III

  3. Background 1994 mid of LEP era GEANT 3successfully used in many experiments • Geant4 R&D phase: RD44 • 1994-1998 (Geant4 0: 15 December 1998) • Designed and built Geant4 • New software technology • GEANT 3 experience + some new ideas • Foundation of the current Geant4: dates back to the mid ’90s • Requirements for core capabilities • Software technology • Evolution: 1998-2008 • Consolidation, validation • Support to the experimental community • Refinement of existing capabilities • Extension of physics models, geometry tools etc. • Samecore capabilitiesandtechnologyas in themid ’90s Collected from the experimental community Object Oriented methods introduced in HEP

  4. The world changes… LHC SuperLHC? astrophysics nuclear power medical physics radiobiology nanotechnology detectors… Tevatron Start SPS 1976 W and Z observed 1983 Start LEP 1989 • New experimental domains • New requirements • New technology hardware, software, OS Grid 1998 WWW new R&D

  5. R&D • Motivated by scientific interestswithin INFN scope • Response to current limitations of Geant4 • of all major Monte Carlo systems, not only Geant4 • Address concrete experimental use cases • by going to the very core of Monte Carlo methods • Exploitnew software technology • in response to experimental issues • Build on existing experience • Domain knowledge: simulation in multi-disciplinary research • Software technology expertise • R&D • launched LowE Electromagnetic Physics in 1998 • new simulation capabilities and application domains for Geant4

  6. Topics of research R&D on complementary, co-working transport methods Condensed-random-walk scheme Discrete scheme Monte Carlo method Deterministic methods Nanotechnology detectors Radiation effects on components Radiobiology Plasma physics Material analysis etc. Nuclear power plants Radiotherapy Homeland security etc. Side topics (instrumental to the main objectives) Concerns (scattered and tangled) Built-in physics V&V-ability Physics configurability

  7. Simulation Condensed-random-walkDiscrete • Condensed-random-walk approximation • all general-purpose Monte Carlo codes (EGS, FLUKA, GEANT 3, Geant4, MCNP) • charged particle tracks divided into many steps, several interactions occur in a step • one energy loss and one deflection are calculated for each step • further simplification of Continuous Slowing Down Approximation: energy loss rate determined by stopping power • collisions are treated as binary processes • target electrons free and at rest (or binding accounted only in an approximated way) • adequate as long as the discrete energy loss events are » electronic binding energies • Discrete simulation • all collisions are explicitly simulated as single-scattering interactions • prohibitively time-consuming on large scale for charged particles (infrared divergence) • many “track structure” codes documented in literature • single-purpose, not public, maintenance not ensured, lack general functionality

  8. Two worlds… • Condensed-random-walkOR“discrete” régime • Characterizing choice in a Monte Carlo system • Limited exception: Penelope (switch to elastic scattering near boundaries) What does it mean in practice? ATLAS RADMON How do you link dosimetry to radiation biology? How do you estimate radiation effects on components exposed to LHC + detector environment? And what about the plasma facing materials in a fusion reactor? And nanotechnology-based detectors for HEP? And tracking in a gaseous detector? • Subtle consequences • e.g. X-ray fluorescence emission (PIXE) by impact ionisation has a dependence on secondary production cut introduced to handle infrared divergence! • can affect macroscopic applications: material analysis, precise dosimetry etc.

  9. R&D on co-working CRW-discrete • Scientific motivation • Address large-scale and nano-scale simulation in the same environment • Realistic model of the whole system • Accurate evaluation of radiation effects in small scale structures • Objective • Seamless transition of simulation régime • Capability of simulating complex multi-scale systems • Conceptual and software design challenges • Physics process adaptation to environment • Embedding “mutability” in Monte Carlo physics entities • Difficult • …otherwise it would have already been done

  10. How? • Re-think the design of Geant4 physics domain • Kernel: how processes interact with tracking • Processes: mutability “in the guts” • Particles: they also become mutable entities • e.g. ions (beyond effective charge scaling) • Multiple scattering, its relation to energy loss • New domain design exploiting new technology • In response to physics requirements • Configurability + performance • Side-by-side with conventional OO methods • Detangle the current spaghetti first • Problem domain analysis • Rigorous domain decomposition Lot of work Unavoidable

  11. Generic programming • Relatively new technology • Aka “programming with templates” • Aka “modern design”: post-Alexandrescu’s book era • C++ is capable of a Turing machine at two levels • Exploit both • Mix and match • Further step: generative programming • Extreme configurability • Bind configurability at compile time • Performance gain relevant to nano-scale simulation • Memory consumption “the hardest of hardcore template programming”

  12. Aspect Oriented Programming • Scattered concerns (+ tangled concerns) • e.g. atomic relaxation occurring in photoelectric effect (discrete), ionisation (continuous-discrete), Compton scattering, radioactive decay/photo-evaporation (Geant4 hadronic package) • R&D on Aspect Oriented Programming • Secondary priority: use only in support to prime objectives • Not so well supported in C++ as, for instance, in Java • Same design concept also suitable to native physics “testability” • V&V is today’s greatest concern of Geant4! • Geant4 does not have a test framework, nor a design supporting test processes • V&V left to individual efforts

  13. UP-based Tailored to the project(s) Mapped to ISO 15504 level 3 at least Software Process R&D at the very heart of Monte Carlo concepts and Geant4 architecture • Risk mitigation strategy • No perturbation to a system currently in production in LHC experiments and many other projects • Develop in parallelto Geant4 kernel • Iterative-incremental process: to mitigate“waterfall” risk • Frequent integration • a/b-releases for testing and application feedback • Transition to new kernel for production use when mature • Freedom to explore different solutions • Difficult problem • Iterations and intermediate benchmarks to identify optimal design • Sound confirmation from fully functional prototypes Not to be taken easily!

  14. Prototypes For risk mitigation New design affecting Monte Carlo (Geant4) core Would the proposed technology be a suitable solution? Can the software address a realistic experimental use case? Can it handle systems at macroscopic scale? Does it work at a realistically large scale? Fully functional nano-prototype Fully functional PIXE-prototype Figure courtesy of LLU Figure: G. Weidenspointner et al., Nature • “Conventional” PIXE • Elemental analysis • High-energy PIXE • Next generation X-ray astrophysics • Relevant to precision dosimetry too • Collaboration with MPI • PTB Monte Carlo models and data • >30 years’ experience! • Experimental set-up:nanodosimeter • Experimental validation • Collaboration: PTB+Hamburg, LLU

  15. Deterministic and Monte Carlo simulation • Deterministic methods are widely used in • Reactor physics calculations • Based on the concept of “neutron flux” • Medical physics • Treatment planning • Reactors: series of codes specialized in specific functions • Cumbersome… • Monte Carlo intrinsically more accurate • Model geometry and physics accurately • New trends • Monte Carlo group constant generation for deterministic codes • Conventional deterministic codes not well-suited to complex assembly designs, next generation reactors, advanced MOX technology etc. • Monte Carlo calculations Figure credit: A. Leppanen

  16. Simulation for nuclear power studies • New generation reactors • ITER? • of interest to INFN • INFN expertise in simulation methods and tools is useful to approach this new research domain • …but direct expertise in nuclear power plant simulation still to be built at INFN • Geant4 not widely used in nuclear power studies yet • MCNP is the “standard” Monte Carlo code… for standard problems • Deterministic codes play a major role in reactor calculations • Monte Carlo methods are prohibitively time consuming for some problems • MCNP is developed and maintained at LANL • INFN priorities are not necessarily LANL priorities… • R&D for nuclear power simulation with Geant4

  17. Co-working Monte Carlo - deterministic methods • One calculation environment • Use either transport method where it is best suited • Profit of set-up modelling facilities developed for general-purpose Monte Carlo simulation • Complex design problem in a new application domain • R&D needed • Plan to strengthen collaboration with ANS • Design solutions to be explored in Geant4 • Parallel worlds • Multiple geometries in the same simulation environment • Concept of “mutability” of transport

  18. Staged approach • Due to the complexity of the problem • And need to build new expertise not currently present at INFN • No “tradition” in deterministic transport methods nor in reactor simulation methods • 1st phase • Deterministic methods to calculate ingredients for biasing technique • Produce concrete deliverable • Build up expertise • Project: use discrete ordinatesadjoint function for automated variance reduction of Monte Carlo calculations • Concrete deliverable • Similar problem addressed with MCNP • Evaluation benchmarks of Geant4 for nuclear power studies • 2nd phase • Co-operation of the two approaches in the same environment

  19. Main deliverables CRW-discrete simulation Work Package • Nano-prototype • Requirements (or use case model) • Design model • Implementation (PTB-like models) • Performance and physics benchmarks • PIXE-prototype • Requirements (or use case model) • Design model • Implementation • Validation • Deterministic-Monte Carlo methods Work Package • Package for variance reduction calculation through deterministic methods • Benchmarks of Geant4 applicability to nuclear power simulation Include new Monte Carlo kernel Geant4 Nano5 Geant5…

  20. Milestones • CRW-discrete • Problem domain analysis, design model, “detangled” prototype: July 2009 • PIXE prototype: December 2009 • PTB Monte Carlo reengineered: July 2010 • Nanodosimeter prototype functional: end 2010 • Nanodosimeter prototype validation: mid 2011 • Transition phase: end 2011 • Deterministic-Monte Carlo methods • Use case model & analysis: end 2009 • Discrete ordinates adjoint function calculation: end 2010 • Variance reduction application: 2011 • Geant4 evaluation for nuclear power studies: end 2009

  21. Book on Simulation Techniques in Physics Invito da primaria casa editrice a pubblicare un libro su tecniche di simulazione in fisica NANO5 scaturisce da una lunga esperienza di simulazione…

  22. Acknowledgment Thanks to: • T. Evans (ORNL) • E. Gargioni (PTB) • S. Giani (CERN), RD44 Spokesman and Project Leader • B. Grosswendt (PTB) • L. Moneta (CERN) • A. Pfeiffer (CERN) • R. Schulte (LLU) • E. Smith (PNL) • G. Weidenspointner (MPI) • A. Wroe (LLU) • A. Zoglauer (LBL)

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