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Toward Automatic Parallel Adaptive Mesh Refinement

Toward Automatic Parallel Adaptive Mesh Refinement. Scott H. Hawley*, Matthew W. Choptuik* U *University of Texas at Austin * U University of British Columbia shawley@einstein.ph.utexas.edu. Credits: Manish Parashar , James C. Browne, Paul Walker,

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Toward Automatic Parallel Adaptive Mesh Refinement

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  1. Toward Automatic Parallel Adaptive Mesh Refinement Scott H. Hawley*, Matthew W. Choptuik*U *University of Texas at Austin *UUniversity of British Columbia shawley@einstein.ph.utexas.edu Credits: Manish Parashar, James C. Browne, Paul Walker, Shyamal Mitra, Robert Marsa, Mijan Huq, Dae-Il Choi

  2. Motivation When we model physical phenomena using finite-difference approximations of partial differential equations… • For fixed local accuracy, required resolution may vary widely in space and time • Resolution requirements may not be known a priori • Adaptive Mesh Refinement (AMR) Even with the utility AMR provides, a code must be parallelizable to take advantage of modern computing machinery

  3. Motivation, cont’d • AMR and parallel processing are desirable, but both present challenges which may be prohibitive for many researchers • Investigate environments in which AMR and parallelism are provided automatically

  4. Paradigm • Almost all details of AMR and parallelism hidden from user • Provide unigrid routines • Specify • maximum # of levels • truncation error tolerance for regridding • clustering efficiency • Entire AMR driver generated automatically • User selects “AMR: On” (someday soon)

  5. Build Around GrACE GrACE provides structures for AMR and parallelization Goal: Make GrACE features easily accessible to end user Provide: • Generic Driver (“Your code here”) • Output support • Supplemental Documentation (“How to…”) • Link to RNPL

  6. Rapid Numerical Prototyping Language (RNPL) Marsa & Choptuik • Minimal development time • Specify: • Initial Data • Boundary Conditions • Finite Difference Equations • Examples: • Pedagogy: Scalar wave in IEF coordinates • Boson star simulations • Generate framework for fluid codes • Easily used to write Cactus Thorns

  7. Coincident Goals Both this effort and Cactus seek automatic, parallel AMR in the very near future • Cactus needs to deliver AMR • Generic GrACE driver could be run as a Cactus Thorn • Maybe “The” Cactus AMR Thorn • Problems my group want to solve • My dissertation: Accretion disk theory within IMSO (Scott needs to land a job…) • Others: Gamma-ray burst models, Multi-D critical phenomena, and much more! David Neilsen, Jason Ventrella, Ethan Honda, Scott Noble • Cactus better connected with a wide variety of computing support than we can provide on our own

  8. Needs • Visualization of AMR data • Interactive tool for daily use • Not necessarily “flashy” or “high-performance” • Inexpensive • Curvilinear coordinate systems • Animation • Grid-grid operations (+,-,*,/,etc) • Easy for user to add new functionality (filters, parameters) • Efficient Collaboration • Daily e-mail is not interactive enough to achieve short turn-around

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