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A common data management infrastructure for adaptive algorithms for PDE solutions
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Source Conference on High Performance Networking and Computing archive
Proceedings of the 1997 ACM/IEEE conference on Supercomputing (CDROM) table of contents
San Jose, CA
Pages: 1 - 22  
Year of Publication: 1997
ISBN:0-89791-985-8
Authors
Manish Parashar  University of Texas at Austin, Austin, TX
James C. Browne  University of Texas at Austin, Austin, TX
Carter Edwards  University of Texas at Austin, Austin, TX
Kenneth Klimkowski  University of Texas at Austin, Austin, TX
Sponsors
IEEE-CS\DATC : IEEE Computer Society
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 4,   Downloads (12 Months): 18,   Citation Count: 11
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ABSTRACT

This paper presents the design, development and application of a computational infrastructure to support the implementation of parallel adaptive algorithms for the solution of sets of partial differential equations. The infrastructure is separated into multiple layers of abstraction. This paper is primarily concerned with the two lowest layersof this infrastructure: a layer which defines and implements dynamic distributed arrays (DDA), and a layer in which several dynamic data and programming abstractions are implemented in terms of the DDAs. The currently implemented abstractions are those needed for formulation of hierarchical adaptive finite difference methods, hp-adaptive finite element methods, and fast multipole method for solution of linear systems. Implementation of sample applications based on each of these methods are described and implementation issues and performance measurements are presented.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
M. Parashar and J. C. Browne, System Engineering for High Performance Computing Software: The HDDA/DAGH Infrastructure for Implementation of Parallel Structured Adaptive Mesh Refinement, to be published in Structured Adaptive Mesh Refinement Grid Methods, IMA Volumes in Mathematics and its Applications, Springer-Verlag, 1997.
 
2
Harold Carter Edwards,A Parallel Infrastructure for Scalable Adaptive Finite Element Methods and its Application to Least Squares C-infinity Collocation, PhD Thesis, The University of Texas at Austin, May 1997.
 
3
Hans Sagan, Space Filling Curves, Springer-Verlag, 1994.
 
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W. Litwin. Linear Hashing: a New Tool for File and Table Addressing, Proceedings of the 6th Conference on VLDB, Montreal, Canada, 1980.
 
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Marsha J. Berger, Joseph Oliger, Adaptive Mesh-Refinement for Hyperbolic Partial Differential Equations, Journal of Computational Physics, pp. 484-512, 1984.
 
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Manish Parashar and James C. Browne, Distributed Dynamic Data-Structures for Parallel Adaptive Mesh-Refinement, Proceedings of the International Conference for High Performance Computing, pp. 22-27, Dec. 1995.
 
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J. Masso and C. Bona, Hyperbolic System for Numerical Relativity, Physics Review Letters, 68(1097), 1992.
 
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Jürgen K. Singer, Parallel Implementation of the Fast Multipole Method with Periodic Boundary Conditions, East-West Journal on Numerical Mathematics, 3(3), October 1995.

CITED BY  11

Collaborative Colleagues:
Manish Parashar: colleagues
James C. Browne: colleagues
Carter Edwards: colleagues
Kenneth Klimkowski: colleagues