It is folklore that the double-rooted complete binary tree is a spanning tree of the hypercube of the same size. Unfortunately, the usual construction of an embedding of a double-rooted complete binary tree into a hypercube does not provide any hint on how this embedding can be extended if each leaf
Efficient Dynamic Embeddings of Binary Trees into Hypercubes
β Scribed by Volker Heun; Ernst W. Mayr
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 255 KB
- Volume
- 43
- Category
- Article
- ISSN
- 0196-6774
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β¦ Synopsis
In this paper, a deterministic algorithm for dynamically embedding binary trees into hypercubes is presented. Because of a known lower bound, any such algorithm must use either randomization or migration, i.e., remapping of tree vertices, to obtain an embedding of trees into hypercubes with small dilation, load, and expansion simultaneously. Using migration of previously mapped tree vertices, the presented algorithm constructs a dynamic embedding which achieves dilation of at most 9, unit load, nearly optimal expansion, and constant edge-and nodecongestion. This is the first dynamic embedding that achieves these bounds simultaneously. Moreover, the embedding can be computed efficiently on the hypercube itself. The amortized time for each spawning step is bounded by O log 2 L , if in each step at most L new leaves are spawned. From this construction, a dynamic embedding of large binary trees into hypercubes is derived which achieves dilation of at most 6 and nearly optimal load. Similarly, this embedding can be constructed with nearly optimal load Ο on the hypercube itself in amortized time O Ο β’ log 2 L/Ο per spawning step, if in each step at most L new leaves are added.  2002 Elsevier Science (USA)
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The d-dimensional binary hypercube is a very popular model of parallel computation. On the other hand, the execution of many algorithms can be represented by binary trees, making it desirable to simulate binary trees on a hypercube. In this paper, we present a simple one-to-one embedding of arbitrar
In the next section we present basic definitions and notations where the criterion of optimality is defined and related to the concept of ''normal'' algorithms. In Section 3 we present an optimal embedding method that balances the processor loads. In Section 4 we present a nonoptimal embedding metho