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T7/High Performance Computing

T7/High Performance Computing. K. Ko, R. Ryne, P. Spentzouris. Accelerators are Crucial to Scientific Discoveries in High Energy Physics, Nuclear Physics, Materials Science, Biological Science.

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T7/High Performance Computing

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  1. T7/High Performance Computing K. Ko, R. Ryne, P. Spentzouris

  2. Accelerators are Crucial to Scientific Discoveries in High Energy Physics, Nuclear Physics, Materials Science, Biological Science “Starting this fall, a machine called RHIC will collide gold nuclei with such force that they will melt into their primordial building blocks” “A new generation of accelerators capable of generating beams of exotic radioactive nuclei aims to simulate the element-building process in stars and shed light on nuclear structure” “Muon Experiment Challenges Reigning Model of Particles” “Violated particles reveal quirks of antimatter” “Biologists and other researchers are lining up at synchrotrons to probe materials and molecules with hard x-rays”

  3. Contributions of accelerators to applied science and tech have huge economic impact and greatly benefit society • Medical isotope production • Electron microscopy • Accelerator mass spectrometry • Medical irradiation therapy • Ion implantation • Beam lithography • Transmutation of waste • Accelerator-driven energy production

  4. Particle accelerators are among the largest, most complex, and most important scientific instruments in the world Particle accelerators helped enable some of the most remarkable discoveries of the 20th century (“century of physics”) We may now be on the brink of revolutionary advances in particle physics that will change how we view the universe Given the complexity and importance of accelerators, it is imperative that the most advanced HPC tools be brought to bear on their design, optimization, commissioning, and operation

  5. Objective of HPC Working Group • …understand the modeling needs of current and future accelerator technology • …identify HPC hardware and software technologies required for such modeling • …outline a plan for the development of these technologies

  6. T7 Sessions • Dedicated • HPC tools • HPC simulation of Beam Systems • HPC simulation of Electromagnetic Systems • Joint • Advanced Accelerators • Beam Dynamics • Proton Drivers • Muon-based Systems • Particle Sources

  7. HPC needs of present and next-generation accelerators

  8. High-Intensity Proton Drivers • Present codes inadequate for full 3D modeling • Extrapolation predicts ~year long jobs on 128 processors • Good progress from SNS effort • Important physics • 3D space charge • Long bunches • Impedances • e-p instability • Code development underway • Code comparison effort • Experiments TBD

  9. Linear Colliders • Electromagnetic modeling • NLC • Complicated 3D structures • High accuracy, large problems, unstructured grids • Beam Dynamics • Space-charge and collective effects in damping rings • Linac & beam delivery simulations needed with full bunch train(?) including component fluctuations + beam tuning and feedback systems • Damping ring  IP full simulation expected to take ~1yr/processor

  10. VLHC • HPC simulation of instabilities • Electron-cloud • Resistive wall, TMCI • HPC simulation of beam-beam effects • Dynamic aperture • Closed-orbit correction • Energy deposition • Full system simulations including component fluctuations + feedback

  11. Muon-Based Systems • Ionization cooling • Accurate modeling of muon/matter interactions • Energy loss, multiple scattering • Typically done with portions of HEP packages (need to parallelize) • Need for code interfacing • System optimization involving HPC software components • Space-charge effects in rings [we should be so lucky ] 1PE: 500 particles, 460 sec 128 PE: 500,000 particles, 4400 sec

  12. Summary: Application of HPC to Beam Phenomena • Space charge • Particle & field managers • Direct Vlasov • Beam-beam • Electron-cloud • Full dynamics of beam and electrons • CSR • Impedances/wakes • Multi-bunch effects (pipeline model) • Collisions • First-principles Fokker/Planck

  13. Application of HPC to Electromagnetic Modeling • Eigenmode • Complicated 3D structures • High-accuracy (individual cells) • Very large scale (EM system simulation) • Unstructured grids, micron-scale variation • Time-domain • Frequency response • Direct wakefield calcuations • Additional phenomena (surface physics,…) • Particle dynamics (dark current,…) • Trapped modes • Structure heating

  14. HPC Simulation of Advanced Accelerator Concepts • Multiple models • Fully explicit 3D Vlasov/Maxwell • Ponderomotive, quasi-static,… • Moving windows • Multiple species • Ionization • Multiple scales (lasers) • CPU estimates >= 100,000 hrs for a GeV plasma accelerator stage

  15. SciDAC(Scientific Discovery through Advanced Computing) DOE Office of Science initiative in advanced computing Includes DOE/HENP project to develop a comprehensive terascale capability in accelerator simulation

  16. HPC Tools • David Keyes, TOPS (Terascale Optimal PDE Solvers) • Lori Freitag, TSTT (Terascale Simulation Tools & Tech) • Phil Colella, Adaptive Mesh Refinement for PDEs • Viktor Decyk, Parallel Particle Simulation • Esmond Ng, Parallel Linear Algebra & Eigensolvers • Horst Simon, Future architectures • Performance Enhancement (ATLAS,…) • Code integration (Common Component Architecture,…) • Visualization

  17. Physical system Physical model Mathematical description Algorithms Software implementation Theory+Experiment+Computation { Physical Experiment (diagnostics!) Theory { Verification & Validation Computation (HPC) Computer Simulation

  18. Summary: HPC will play a major role • Present accelerators: Maximize investment by • optimizing performance • expanding operational envelopes • increasing reliability and availability • Next-generation accelerators • better designs • feasibility studies • Facilitate important design decisions • completion on schedule and within budget • Accelerator science and technology • help develop new methods of acceleration • explore beams under extreme conditions

  19. Let’s get back on the Livingston curve

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