Improved compressions of cube-connected cycles networks

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    Improved Compressions ofCube-Connected Cycles Networks

    Ralf Klasing

    AbstractWe present a new technique for the embedding of large cube-connected cycles networks (CCC) into smaller ones, aproblem that arises when algorithms designed for an architecture of an ideal size are to be executed on an existing architecture of afixed size. Using the new embedding strategy, we show that the CCC of dimension l can be embedded into the CCC of dimension kwith dilation 1 and optimum load for any k, l N, k 8, such 53

    4 3

    3 22, 2 3+ < =


    c ck k klk

    k , thus improving known results. Our

    embedding technique also leads to improved dilation-1 embeddings in the case 3253< +

    lk ck .

    Index TermsParallel computations, parallel architectures, interconnection networks, graph embedding, network simulation, cube-connected cycles network.


    1 INTRODUCTIONVER the past few years, a lot of research has been donein the field of interconnection networks for parallel

    computer architectures (for an overview, cf. [19]). Much ofthe work has been focused on the capability of certain net-works to simulate other network or algorithm structures inorder to execute parallel algorithms of a special structureefficiently on different processor networks (see, e.g., [5],[17], [25]). One problem that is of specific interest in thiscontext is that many existing algorithms are designed forarbitrarily large networks (see, e.g., [19]), whereas, in prac-tice, the processor network will be fixed and of smaller size.Thus, the larger network must be simulated in an efficientway on the smaller target network. There is an enormousamount of literature on this problem (see, e.g., [3], [8], [14],[15], [21], [23], [24], [26], [30]).

    Customarily, the simulation problem is formalized as theembedding problem of one graph in another (for a formaldefinition of the embedding problem, see Section 2). Thequality of an embedding is measured by the parametersload, dilation, and congestion. The importance of the differentparameters becomes apparent through the following result.

    PROPOSITION 1 [20]. If there is an embedding of G into H withload ,, dilation d, and congestion c, then there is a simula-tion of G by H with slowdown O(, + d+ c).

    As a consequence, the load ,, dilation d, and congestion chave been investigated for embeddings between manycommon network structures like hypercubes, binary trees,meshes, shuffle-exchange networks, deBruijn networks,cube-connected cycles, butterfly networks, etc. Most ofthe work was done on one-to-one embeddings (for anoverview, see, e.g., [25], [29]), but results on many-to-one

    embeddings can also be found (see, e.g., [2], [6], [7], [9],[12], [13], [16], [18], [22], [26], [27]). In this paper, we focuson many-to-one embeddings of the cube-connected cyclesnetwork (CCC). The CCC was introduced as a network forparallel processing in [28]. It has fixed degree, small di-ameter, and good routing capabilities [19]. It can executethe important class of normal hypercube algorithms veryefficiently (see, e.g., [19]). In addition, there is also a strongstructural relationship to the deBruijn, shuffle-exchange,and butterfly networks [1], [10]. Hence, the efficient imple-mentation of algorithms on CCC networks (of fixed size) isof importance. According to Proposition 1, one way of exe-cuting algorithms designed for a CCC network of arbitrarysize efficiently on a CCC network of realistic (fixed) size, is tofind embeddings of large CCCs into small CCCs minimizingthe parameters load, dilation, and congestion. In this paper, wefocus on load and dilation. Using our embedding strategy,many important algorithms for large CCCs can be imple-mented very efficiently on a CCC network of realistic size.

    Many-to-one embeddings of the CCC network have beeninvestigated in [2], [6], [12], [16], [27]. In [6], [12], [27], em-beddings with optimum dilation and load are presented inthe case of embedding CCCs of dimension l into k wherek|l. The authors also restrict themselves to special kinds ofembeddings of a very regular structure, like coverings [6],homogeneous emulations [12], and homomorphisms [27].Because of the very restricted nature, Bodlaender [6] andPeine [27] are also able to classify their embeddings com-pletely. In [2], a general procedure is described for mappingparallel algorithms into parallel architectures. This proce-dure is applied to the CCC network achieving dilation 1,but very high load. Also, only special kinds of embeddings,so-called contractions, are considered. In [16], the embed-ding problem for CCCs is investigated, taking into accountgeneral embedding functions and any possible networkdimension. More precisely, it is proved that the cube-connected cycles network of dimension l, CCC(l), can be em-bedded into CCC(k), l > k, with

    1045-9219/98/$10.00 1998 IEEE

    The author is with the Department of Computer Science, University ofWarwick, Coventry CV4 7AL, England.E-mail:

    Manuscript received 20 May 1997.For information on obtaining reprints of this article, please send e-mail, and reference IEEECS Log Number 105085.



    1)dilation 2 and optimum load lkl k 2 ,

    2)dilation 1 and load



    pp p




    l k

    l k






    2 2

    2 12 2 3

    2 31

    2 1


    for such that


    , , .K< A

    In this paper, we present a new technique for the em-bedding of large cube-connected cycles networks intosmaller ones. Using the new embedding strategy, we show:

    Let k, l N, k 8, such that 53 2+ < cklk , ck


    +4 3

    3 22 3.

    Then, there is a dilation 1 embedding of CCC(l) into CCC(k)

    with load lkl k 2 .

    This is optimal, and improves the results from [16]. Ourembedding technique also leads to improved dilation 1embeddings in the case 32

    53< +

    lk kc .

    The general strategy of the embeddings is the same as in[16], namely to map 2l-k cycles in CCC(l) of length l ontoone cycle in CCC(k) of length k and to allocate the nodes ofthe guest cycles in as balanced a manner as possible on thehost cycle. But, in order to improve the results from [16], acompletely different way of allocating the guest nodes onthe host cycle is introduced.

    The paper is organized as follows. Section 2 contains thedefinitions of the terms used in the paper. Section 3 pres-ents the new embedding strategy. Section 4 presents thederived results. The conclusion gives an outlook on furtherconsequences of the new embedding technique.

    2 DEFINITIONS(Most of the terminology is taken from [19], [25].) For anygraph G = (V, E), let V(G) = V denote the set of vertices of G,and E(G) = E denote the set of edges of G. Let a denote the

    binary complement of a {0, 1}. For a = {0, 1}m,

    let ( )i a a a a ai i i m= + 0 1 1 1K K .

    2.1 Cube-Connected Cycles NetworkThe (wrapped) cube-connected cycles network of dimension m,denoted by CCC(m), has vertex-set Vm = {0, 1, ..., m - 1} {0, 1}m, where {0, 1}m denotes the set of length-m binarystrings. For each vertex v = (i, a) Vm, i {0, 1, ..., m - 1},a {0, 1}m, we call i the level and a the position-within-level(PWL) string of v. The edges of CCC(m) are of two types: For

    each i {0, 1, ..., m - 1} and each a = {0, 1}m, the

    vertex (i, a) on level i of CCC(m) is connected

    by a cycle-edge with vertex ((i + 1) mod m, a) on level(i + 1) mod m and

    by a cross-edge with vertex (i, a(i)) on level i.

    For each a {0, 1}m, the cycle

    (0, a) (1, a) (m - 1, a) (0, a)

    of length m will be denoted by Ca(m) or Ca.

    CCC(m) has m2m nodes, 3m2m-1 edges, and degree 3. Anillustration of CCC(3) is shown in Fig. 1.

    2.2 Graph EmbeddingsLet G and H be finite undirected graphs. An embedding of Ginto H is a mapping f from the nodes of G to the nodes of H.G is called the guest graph and H is called the host graph ofthe embedding f. The load of the embedding f is the maxi-mum number of vertices of the guest graph G that aremapped to the same host graph vertex. (The optimum loadachievable is the ratio |V(G)|/|V(H)| of the number ofnodes in G and H.) The dilation of the embedding f is themaximum distance in the host between the images of adja-cent guest nodes. A routing is a mapping r of Gs edges topaths in H, r(v1, v2) = a path from f(v1) to f(v2) in H. The con-gestion of the embedding f is the maximum number ofedges that are routed through a single edge of H.

    2.3 Lexicographic OrderingsLet Lex : {0, , m - 1} {0, 1}n N0, Lex(i, a0 an-1) = i2

    n +a02

    n-1 + a12n-2 + + an-12

    0. Then, the lexicographic order on{0, 1, , m - 1} {0, 1}n is defined by

    i j i j, , , , 0 5 1 6 0 5 1 6<


    (i, b) according to the lexicographic order on {a1, ,b1} {0, 1}


    b ab ar b a

    b ard j1 1

    2 2

    1 1

    2 2


    1 112 1 2

    + +

    + + ( ) for all j {a2,

    , b2}.

    (Note that such an allocation function d can always be con-structed for the parameters a1, b1, a2, b2, r as above.)

    3 THE GENERAL EMBEDDING STRATEGYThe basic idea of the embeddings presented here is to map

    2l-k cycles C C Cl k 1 2 2

    , , ,K

    in CCC(l) of length l onto one

    cycle Cb of length k in CCC(k) and to allocate the l 2l-k nodes

    of C Cl k 1 2

    , ,K

    appropriately among the k nodes of Cb .