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Cactus Workshop - NCSA Sep 27 - Oct 1 1999 Scott H. Hawley*, Matthew W. Choptuik* *University of Texas at Austin * University of British Columbia [email protected] Toward Automatic Parallel Adaptive Mesh Refinement Credits: Manish Parashar, James C. Browne, Paul Walker, Shyamal Mitra, Robert Marsa, Mijan Huq, Dae-Il Choi

Toward Automatic Parallel Adaptive Mesh Refinement

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Toward Automatic Parallel Adaptive Mesh Refinement. Scott H. Hawley*, Matthew W. Choptuik* U *University of Texas at Austin * U University of British Columbia [email protected]. Credits: Manish Parashar , James C. Browne, Paul Walker, - PowerPoint PPT Presentation

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Page 1: Toward Automatic Parallel Adaptive Mesh Refinement

Cactus Workshop - NCSA Sep 27 - Oct 1 1999

Scott H. Hawley*, Matthew W. Choptuik*

*University of Texas at Austin

*University of British Columbia

[email protected]

Toward Automatic Parallel Adaptive Mesh Refinement

Credits: Manish Parashar, James C. Browne, Paul Walker, Shyamal Mitra, Robert Marsa, Mijan Huq, Dae-Il Choi

Page 2: Toward Automatic Parallel Adaptive Mesh Refinement

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

MotivationMotivation

Page 3: Toward Automatic Parallel Adaptive Mesh Refinement

Motivation, cont’dMotivation, 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

Page 4: Toward Automatic Parallel Adaptive Mesh Refinement

ParadigmParadigm

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)

Page 5: Toward Automatic Parallel Adaptive Mesh Refinement

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

Build Around GrACEBuild Around GrACE

Page 6: Toward Automatic Parallel Adaptive Mesh Refinement

Rapid Numerical Prototyping Language (RNPL)Rapid Numerical Prototyping Language (RNPL)

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

Marsa & Choptuik

Page 7: Toward Automatic Parallel Adaptive Mesh Refinement

Coincident GoalsCoincident 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

Page 8: Toward Automatic Parallel Adaptive Mesh Refinement

NeedsNeeds

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

Page 9: Toward Automatic Parallel Adaptive Mesh Refinement