“…As an example consider the tree of 11 nodes, labeled from 0 to 10 in Fig.1a in which the path 1 0 is (1,4,6,8,3,0). We obtain f 0 = 1,…”
Section: The Dandelion Codementioning
confidence: 99%
“…In our example, since f 0 = 1, f 1 = 8, and f 2 = 3, the algorithm set g [3] ← g [8] = 3, introducing a loop, and g [8] ← g [1] = 4, introducing a cycle. Fig.1d shows the resulting digraph: the associated codeword is W = [6,3,6,1,8,8,4,1,9].…”
Section: The Dandelion Codementioning
confidence: 99%
“…As an example of decoding consider the codeword W = [6,3,6,1,8,8,4,1,9]. It is easy to see that, according with the reconstruction described above, we initially obtain the functional digraph represented in Fig.1d.…”
Section: The Dandelion Codementioning
confidence: 99%
“…Thus we obtain f 0 = 1, f 1 = 8, f 2 = 3. Then we set g [1] ← g [8] = 4, g [8] ← g [3] = 3, and g [3] ← 0. Now G g is exactly the tree represented in Fig.1a. …”
Section: The Dandelion Codementioning
confidence: 99%
“…For example, they are used in fault dictionary storage [1], distributed spanning tree maintenance [2], generation of random trees [3], Genetic Algorithms [4]. In this paper we restrict our attention to bijective string-based codes in which the length of the string must be equal to n − 2 [5] (n is the number of nodes of the encoded tree).…”
Abstract. We consider the class of Dandelion-like codes, i.e., a class of bijective codes for coding labeled trees by means of strings of node labels. In the literature it is possible to find optimal sequential algorithms for codes belonging to this class, but, for the best of our knowledge, no parallel algorithm is reported. In this paper we present the first encoding and decoding parallel algorithms for Dandelion-like codes. Namely, we design a unique encoding algorithm and a unique decoding algorithm that, properly parametrized, can be used for all Dandelion-like codes. These algorithms are optimal in the sequential setting. The encoding algorithm implementation on an EREW PRAM is optimal, while the efficient implementation of the decoding algorithm requires concurrent reading.
“…As an example consider the tree of 11 nodes, labeled from 0 to 10 in Fig.1a in which the path 1 0 is (1,4,6,8,3,0). We obtain f 0 = 1,…”
Section: The Dandelion Codementioning
confidence: 99%
“…In our example, since f 0 = 1, f 1 = 8, and f 2 = 3, the algorithm set g [3] ← g [8] = 3, introducing a loop, and g [8] ← g [1] = 4, introducing a cycle. Fig.1d shows the resulting digraph: the associated codeword is W = [6,3,6,1,8,8,4,1,9].…”
Section: The Dandelion Codementioning
confidence: 99%
“…As an example of decoding consider the codeword W = [6,3,6,1,8,8,4,1,9]. It is easy to see that, according with the reconstruction described above, we initially obtain the functional digraph represented in Fig.1d.…”
Section: The Dandelion Codementioning
confidence: 99%
“…Thus we obtain f 0 = 1, f 1 = 8, f 2 = 3. Then we set g [1] ← g [8] = 4, g [8] ← g [3] = 3, and g [3] ← 0. Now G g is exactly the tree represented in Fig.1a. …”
Section: The Dandelion Codementioning
confidence: 99%
“…For example, they are used in fault dictionary storage [1], distributed spanning tree maintenance [2], generation of random trees [3], Genetic Algorithms [4]. In this paper we restrict our attention to bijective string-based codes in which the length of the string must be equal to n − 2 [5] (n is the number of nodes of the encoded tree).…”
Abstract. We consider the class of Dandelion-like codes, i.e., a class of bijective codes for coding labeled trees by means of strings of node labels. In the literature it is possible to find optimal sequential algorithms for codes belonging to this class, but, for the best of our knowledge, no parallel algorithm is reported. In this paper we present the first encoding and decoding parallel algorithms for Dandelion-like codes. Namely, we design a unique encoding algorithm and a unique decoding algorithm that, properly parametrized, can be used for all Dandelion-like codes. These algorithms are optimal in the sequential setting. The encoding algorithm implementation on an EREW PRAM is optimal, while the efficient implementation of the decoding algorithm requires concurrent reading.
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