1998
DOI: 10.1016/s0020-0190(98)00142-2
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Parallel dictionaries with local rules on AVL and brother trees

Abstract: We present a set of local rules to deal with dictionaries. Their main advantage is that they can be scheduled in a highly synchronized way to get parallel dictionaries on AVL trees. Up to now trees used in massively parallel dictionaries needed to have all the leaves at the same depth, such as 2{3 trees. Therefore, it was possible (in insertions and deletions) the bottom-up reconstruction of the tree in a very regular fashion, as a pipeline of plane waves moving up. On AVL trees the situation looks di erent be… Show more

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Cited by 2 publications
(1 citation statement)
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“…One is intended to allow several reads to share nodes with a writer process, the other is based on the first one and combines parallelization techniques to implement concurrency for writes. Joaquim et al [6] got parallel dictionaries on AVL trees for insertions and deletions, by taking advantage of virtual plane waves which allows it to develop an EREW dictionary for k keys with k processors and time complexity O(logn + logk). Muralidhar Medidi and Narsingh Deo also presented such a parallel dictionary on AVL trees [7] by developing an explicit processor scheduling to avoid simultaneous reads while performing k searches.…”
Section: Related Workmentioning
confidence: 99%
“…One is intended to allow several reads to share nodes with a writer process, the other is based on the first one and combines parallelization techniques to implement concurrency for writes. Joaquim et al [6] got parallel dictionaries on AVL trees for insertions and deletions, by taking advantage of virtual plane waves which allows it to develop an EREW dictionary for k keys with k processors and time complexity O(logn + logk). Muralidhar Medidi and Narsingh Deo also presented such a parallel dictionary on AVL trees [7] by developing an explicit processor scheduling to avoid simultaneous reads while performing k searches.…”
Section: Related Workmentioning
confidence: 99%