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| O.H. Ibarra, T.-C. Pong, S.M. Sohn, "Parallel Recognition and Parsing on the Hypercube," IEEE Transactions on Computers, vol. 40, no. 6, pp. 764-770, June, 1991. | |||
| BibTex | x | ||
| @article{ 10.1109/12.90253, author = {O.H. Ibarra and T.-C. Pong and S.M. Sohn}, title = {Parallel Recognition and Parsing on the Hypercube}, journal ={IEEE Transactions on Computers}, volume = {40}, number = {6}, issn = {0018-9340}, year = {1991}, pages = {764-770}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.90253}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Computers TI - Parallel Recognition and Parsing on the Hypercube IS - 6 SN - 0018-9340 SP764 EP770 EPD - 764-770 A1 - O.H. Ibarra, A1 - T.-C. Pong, A1 - S.M. Sohn, PY - 1991 KW - parallel recognition; time bounds; parsing; hypercube; parallel algorithms; context-free languages; sequential dynamic programming algorithm; nonoverlapping interprocessor data transmissions; space bounds; 64-node NCUBE/7 MIMD hypercube machine; polygon triangulation problem; matrix product chain problem; context-free languages; grammars; parallel algorithms. VL - 40 JA - IEEE Transactions on Computers ER - | |||
The authors present parallel algorithms for recognizing and parsing context-free languages on the hypercube. This algorithm is both time-wise and space-wise optimal with respect to the usual sequential dynamic programming algorithm. Also, the number of nonoverlapping interprocessor data transmissions for the recognition phase is small. It is noted that this is desirable since communication cost in reality is a function of the number of transmissions as well as transmission length. The authors present another recognition algorithm that achieves the same time and space bounds but employs a dynamic loading balancing technique to increase processor utilization. The results of implementing these algorithms on a 64-node NCUBE/7 MIMD hypercube machine are also given. The experimental evidence indicates that, while both recognition algorithms exhibit acceptable speedups, using load balancing results in significantly better performance. The authors obtain parallel algorithms with the same time and space bounds as above for the polygon triangulation problem and the matrix product chain problem.
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