M-Tree: A parallel abstract data type for block-irregular adaptive applications

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1300 LNCS, Issue , 1997, Pages 638-649

  Wu, Q ^a,b   Field, A J ^a   Kelly, P H J ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b CHAM Limited   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; BALANCING; FORESTRY; TREES (MATHEMATICS);

ADAPTIVE APPLICATION; ADAPTIVE MESH REFINEMENT; COMMUNICATION MINIMIZATION; COMPUTATIONAL PATTERNS; COMPUTATIONAL STRUCTURE; HIGH-LEVEL ABSTRACTION; PARALLEL ADAPTIVE COMPUTATION; SCIENCE AND ENGINEERING;

ABSTRACT DATA TYPES;

EID: 84882595664     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0002795     Document Type: Conference Paper

References (18)

1. J. Barnes and P. Hut. A hierarchical 0(N log N) force-calculation algorithm. Nature, 324(4), December 1986.
2. Andrew J. Bennett and Paul H. J. Kelly. Efficient shared-memory support for parallel graph reduction. Future Generation Computer Systems, 1997. To appear.
3. Simon Boothroyd. Galaxy simulation on the AP1000, 1996. MEng Dissertation, Department of Computing, Imperial College.
4. G. H. Botorog and H. Kuchen. Algorithmic skeletons for adaptive multigrid algorithms. In Proceedings of IRREGULAR'95, LNCS 980. Springer-Verlag, 1995.
5. G.F. Carey, M. Sharma, and K.C. Wang. A class of data structures for 2-d and 3-d adaptive mesh refinement. International Journal for numerical methods in Engineering, 26, 1988.
6. K.M. Chandy and C. Kesselman. Compositional C++: Compositional parallel programming. Technical report, California Institute of Technology, 1992. Technical Report Caltech CS-TR-92-13.
7. K.M. Chandy, R. Manohar, B.L. Massingill, and D.I. Meiron. Integrating task and data parallelism with the collective communication archetype. Technical report, California Institute of Technology, 1994. Technical Report Caltech CS-TR-94-08.
8. J. Darlington, A.J. Field, P.G. Harrison, P.H.J. Kelly, D.W.N. Sharp, Q. Wu, and R.L. While. Parallel programming using skeleton functions. In Parallel Architectures And Languages, Europe; PARLE 93. Springer-Verlag, 1993.
9. Department of Computer Science, Computer Sciences Laboratory, The Australian National University. MPI: User's Guide, 1994.
10. D.J. Edelsohn. Hierarchical tree-structures as adaptive meshes. Technical Report SCCS-193, Syracuse Center for Computational Science, NY, 1991.
11. Ian Foster, Robert Olson, and Steven Tuecke. Productive parallel programming: The PCN approach. Scientific Programming, 1(1), 1992.
12. Paul H.J. Kelly. Functional Programming for Loosely-coupled Multiprocessors. Pitman/MIT Press, 1989.
13. S. R. Kohn. A Parallel Software Infrastructure for Dynamic Block-Irregular Scientific Calculations. PhD thesis, Dept. of Computer Science and Engineering, Univ. of California, San Diego, 1995.
14. J. K. Salmon M. S. Warren. A parallel hashed oct-tree n-body algorithm. In Proceedings of SuperComputing 93, 1993.
15. M. Parashar and J. C. Browne. Dagh: A data management infrastructure for parallel adaptive mesh refinement techniques. Technical report, Dept. of Computer Science, Univ. of Texas at Austin, 1995. Premiminary Users Guide.
16. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C. Cambridge University Press, Cambridge, second edition, 1992.
17. H. Samet. The design and analysis of spatial data structures. MIT Press, 1990.
18. Mario Südholt. Data distribution algebras - a formal basis for programming using skeletons. In E.-R. Olderog, editor, Programming Concepts, Methods and Calculi, pages 19-38. North-Holland, June 1994.