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M. Angelaccio, M. Colajanni, "Unifying and Optimizing Parallel Linear Algebra Algorithms," IEEE Transactions on Parallel and Distributed Systems, vol. 4, no. 12, pp. 13821397, December, 1993.  
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@article{ 10.1109/71.250119, author = {M. Angelaccio and M. Colajanni}, title = {Unifying and Optimizing Parallel Linear Algebra Algorithms}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {4}, number = {12}, issn = {10459219}, year = {1993}, pages = {13821397}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.250119}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  Unifying and Optimizing Parallel Linear Algebra Algorithms IS  12 SN  10459219 SP1382 EP1397 EPD  13821397 A1  M. Angelaccio, A1  M. Colajanni, PY  1993 KW  Index Termsparallel linear algebra algorithms; linear algebra algorithms; multicomputers; parallelimplementations; parallel programs; subcube matrix decomposition; metaalgorithm;communication; computation; decompositionindependent definition; optimization; linearalgebra; matrix algebra; optimisation; parallel algorithms VL  4 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Two issues in linear algebra algorithms for multicomputers are addressed. First, how tounify parallel implementations of the same algorithm in a decompositionindependent way. Second, how to optimize naive parallel programs maintaining the decompositionindependence. Several matrix decompositions are viewed as instances of a more generalallocation function called subcube matrix decomposition. By this metadecomposition, aprogramming environment characterized by general primitives that allow one to designmetaalgorithms independently of a particular decomposition. The authors apply such aframework to the parallel solution of dense matrices. This demonstrates that most of theexisting algorithms can be derived by suitably setting the primitives used in themetaalgorithm. A further application of this programming style concerns the optimization of parallel algorithms. The idea to overlap communication and computation has been extended from 1D decompositions to 2D decompositions. Thus, a first attempt towards a decompositionindependent definition of such optimization strategies is provided.
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