I?'!'-Comparability Graphs
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A Fast and Scalable Graph Coloring Algorithm for Multi-Core and Many-Core Architectures
A Fast and Scalable Graph Coloring Algorithm for Multi-core and Many-core Architectures Georgios Rokos1, Gerard Gorman2, and Paul H J Kelly1 1 Software Peroformance Optimisation Group, Department of Computing 2 Applied Modelling and Computation Group, Department of Earth Science and Engineering Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom, fgeorgios.rokos09,g.gorman,[email protected] Abstract. Irregular computations on unstructured data are an impor- tant class of problems for parallel programming. Graph coloring is often an important preprocessing step, e.g. as a way to perform dependency analysis for safe parallel execution. The total run time of a coloring algo- rithm adds to the overall parallel overhead of the application whereas the number of colors used determines the amount of exposed parallelism. A fast and scalable coloring algorithm using as few colors as possible is vi- tal for the overall parallel performance and scalability of many irregular applications that depend upon runtime dependency analysis. C¸ataly¨ureket al. have proposed a graph coloring algorithm which relies on speculative, local assignment of colors. In this paper we present an improved version which runs even more optimistically with less thread synchronization and reduced number of conflicts compared to C¸ataly¨urek et al.'s algorithm. We show that the new technique scales better on multi- core and many-core systems and performs up to 1.5x faster than its pre- decessor on graphs with high-degree vertices, while keeping the number of colors at the same near-optimal levels. Keywords: Graph Coloring; Greedy Coloring; First-Fit Coloring; Ir- regular Data; Parallel Graph Algorithms; Shared-Memory Parallelism; Optimistic Execution; Many-core Architectures; Intel®Xeon Phi™ 1 Introduction Many modern applications are built around algorithms which operate on irreg- ular data structures, usually in form of graphs. -
Lecture 10: April 20, 2005 Perfect Graphs
Re-revised notes 4-22-2005 10pm CMSC 27400-1/37200-1 Combinatorics and Probability Spring 2005 Lecture 10: April 20, 2005 Instructor: L´aszl´oBabai Scribe: Raghav Kulkarni TA SCHEDULE: TA sessions are held in Ryerson-255, Monday, Tuesday and Thursday 5:30{6:30pm. INSTRUCTOR'S EMAIL: [email protected] TA's EMAIL: [email protected], [email protected] IMPORTANT: Take-home test Friday, April 29, due Monday, May 2, before class. Perfect Graphs k 1=k Shannon capacity of a graph G is: Θ(G) := limk (α(G )) : !1 Exercise 10.1 Show that α(G) χ(G): (G is the complement of G:) ≤ Exercise 10.2 Show that χ(G H) χ(G)χ(H): · ≤ Exercise 10.3 Show that Θ(G) χ(G): ≤ So, α(G) Θ(G) χ(G): ≤ ≤ Definition: G is perfect if for all induced sugraphs H of G, α(H) = χ(H); i. e., the chromatic number is equal to the clique number. Theorem 10.4 (Lov´asz) G is perfect iff G is perfect. (This was open under the name \weak perfect graph conjecture.") Corollary 10.5 If G is perfect then Θ(G) = α(G) = χ(G): Exercise 10.6 (a) Kn is perfect. (b) All bipartite graphs are perfect. Exercise 10.7 Prove: If G is bipartite then G is perfect. Do not use Lov´asz'sTheorem (Theorem 10.4). 1 Lecture 10: April 20, 2005 2 The smallest imperfect (not perfect) graph is C5 : α(C5) = 2; χ(C5) = 3: For k 2, C2k+1 imperfect. -
Graph Orientations and Linear Extensions
GRAPH ORIENTATIONS AND LINEAR EXTENSIONS. BENJAMIN IRIARTE Abstract. Given an underlying undirected simple graph, we consider the set of all acyclic orientations of its edges. Each of these orientations induces a par- tial order on the vertices of our graph and, therefore, we can count the number of linear extensions of these posets. We want to know which choice of orien- tation maximizes the number of linear extensions of the corresponding poset, and this problem will be solved essentially for comparability graphs and odd cycles, presenting several proofs. The corresponding enumeration problem for arbitrary simple graphs will be studied, including the case of random graphs; this will culminate in 1) new bounds for the volume of the stable polytope and 2) strong concentration results for our main statistic and for the graph entropy, which hold true a:s: for random graphs. We will then argue that our problem springs up naturally in the theory of graphical arrangements and graphical zonotopes. 1. Introduction. Linear extensions of partially ordered sets have been the object of much attention and their uses and applications remain increasing. Their number is a fundamental statistic of posets, and they relate to ever-recurring problems in computer science due to their role in sorting problems. Still, many fundamental questions about linear extensions are unsolved, including the well-known 1=3-2=3 Conjecture. Efficiently enumerating linear extensions of certain posets is difficult, and the general problem has been found to be ]P-complete in Brightwell and Winkler (1991). Directed acyclic graphs, and similarly, acyclic orientations of simple undirected graphs, are closely related to posets, and their problem-modeling values in several disciplines, including the biological sciences, needs no introduction. -
Box Representations of Embedded Graphs
Box representations of embedded graphs Louis Esperet CNRS, Laboratoire G-SCOP, Grenoble, France S´eminairede G´eom´etrieAlgorithmique et Combinatoire, Paris March 2017 Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. Ecological/food chain networks Sociological/political networks Fleet maintenance Boxicity d-box: the cartesian product of d intervals [x1; y1] ::: [xd ; yd ] of R × × Definition (Roberts 1969) The boxicity of a graph G, denoted by box(G), is the smallest d such that G is the intersection graph of some d-boxes. -
Improved Ramsey-Type Results in Comparability Graphs
Improved Ramsey-type results in comparability graphs D´anielKor´andi ∗ Istv´anTomon ∗ Abstract Several discrete geometry problems are equivalent to estimating the size of the largest homogeneous sets in graphs that happen to be the union of few comparability graphs. An important observation for such results is that if G is an n-vertex graph that is the union of r comparability (or more generally, perfect) graphs, then either G or its complement contains a clique of size n1=(r+1). This bound is known to be tight for r = 1. The question whether it is optimal for r ≥ 2 was studied by Dumitrescu and T´oth.We prove that it is essentially best possible for r = 2, as well: we introduce a probabilistic construction of two comparability graphs on n vertices, whose union contains no clique or independent set of size n1=3+o(1). Using similar ideas, we can also construct a graph G that is the union of r comparability graphs, and neither G, nor its complement contains a complete bipartite graph with parts of size cn (log n)r . With this, we improve a result of Fox and Pach. 1 Introduction An old problem of Larman, Matouˇsek,Pach and T¨or}ocsik[11, 14] asks for the largest m = m(n) such that among any n convex sets in the plane, there are m that are pairwise disjoint or m that pairwise intersect. Considering the disjointness graph G, whose vertices correspond to the convex sets and edges correspond to disjoint pairs, this problem asks the Ramsey-type question of estimating the largest clique or independent set in G. -
Dessin De Triangulations: Algorithmes, Combinatoire, Et Analyse
Dessin de triangulations: algorithmes, combinatoire, et analyse Eric´ Fusy Projet ALGO, INRIA Rocquencourt et LIX, Ecole´ Polytechnique – p.1/53 Motivations Display of large structures on a planar surface – p.2/53 Planar maps • A planar map is obtained by embedding a planar graph in the plane without edge crossings. • A planar map is defined up to continuous deformation Planar map The same planar map Quadrangulation Triangulation – p.3/53 Planar maps • Planar maps are combinatorial objects • They can be encoded without dealing with coordinates 2 1 Planar map Choice of labels 6 7 4 3 5 Encoding: to each vertex is associated the (cyclic) list of its neighbours in clockwise order 1: (2, 4) 2: (1, 7, 6) 3: (4, 6, 5) 4: (1, 3) 5: (3, 7) 6: (3, 2, 7) 7: (2, 5, 6) – p.4/53 Combinatorics of maps Triangulations Quadrangulations Tetravalent 3 spanning trees 2 spanning trees eulerian orientation = 2(4n−3)! = 2(3n−3)! = 2·3n(2n)! |Tn| n!(3n−2)! |Qn| n!(2n−2)! |En| n!(n+2)! ⇒ Tutte, Schaeffer, Schnyder, De Fraysseix et al... – p.5/53 A particular family of triangulations • We consider triangulations of the 4-gon (the outer face is a quadrangle) • Each 3-cycle delimits a face (irreducibility) Forbidden Irreducible – p.6/53 Transversal structures We define a transversal structure using local conditions Inner edges are colored blue or red and oriented: Inner vertex ⇒ Border vertices ⇒ Example: ⇒ – p.7/53 Link with bipolar orientations bipolar orientation = acyclic orientation with a unique minimum and a unique maximum The blue (resp. -
P 4-Colorings and P 4-Bipartite Graphs Chinh T
P_4-Colorings and P_4-Bipartite Graphs Chinh T. Hoàng, van Bang Le To cite this version: Chinh T. Hoàng, van Bang Le. P_4-Colorings and P_4-Bipartite Graphs. Discrete Mathematics and Theoretical Computer Science, DMTCS, 2001, 4 (2), pp.109-122. hal-00958951 HAL Id: hal-00958951 https://hal.inria.fr/hal-00958951 Submitted on 13 Mar 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Discrete Mathematics and Theoretical Computer Science 4, 2001, 109–122 P4-Free Colorings and P4-Bipartite Graphs Ch´ınh T. Hoang` 1† and Van Bang Le2‡ 1Department of Physics and Computing, Wilfrid Laurier University, 75 University Ave. W., Waterloo, Ontario N2L 3C5, Canada 2Fachbereich Informatik, Universitat¨ Rostock, Albert-Einstein-Straße 21, D-18051 Rostock, Germany received May 19, 1999, revised November 25, 2000, accepted December 15, 2000. A vertex partition of a graph into disjoint subsets Vis is said to be a P4-free coloring if each color class Vi induces a subgraph without a chordless path on four vertices (denoted by P4). Examples of P4-free 2-colorable graphs (also called P4-bipartite graphs) include parity graphs and graphs with “few” P4s like P4-reducible and P4-sparse graphs. -
On Sources in Comparability Graphs, with Applications Stephan Olariu Old Dominion University
Old Dominion University ODU Digital Commons Computer Science Faculty Publications Computer Science 1992 On Sources in Comparability Graphs, With Applications Stephan Olariu Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/computerscience_fac_pubs Part of the Applied Mathematics Commons, and the Computer Sciences Commons Repository Citation Olariu, Stephan, "On Sources in Comparability Graphs, With Applications" (1992). Computer Science Faculty Publications. 128. https://digitalcommons.odu.edu/computerscience_fac_pubs/128 Original Publication Citation Olariu, S. (1992). On sources in comparability graphs, with applications. Discrete Mathematics, 110(1-3), 289-292. doi:10.1016/ 0012-365x(92)90721-q This Article is brought to you for free and open access by the Computer Science at ODU Digital Commons. It has been accepted for inclusion in Computer Science Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. Discrete Mathematics 110 (1992) 2X9-292 289 North-Holland Note On sources in comparability graphs, with applications S. Olariu Department of Computer Science, Old Dominion University, Norfolk, VA 235294162, USA Received 31 October 1989 Abstract Olariu, S., On sources in comparability graphs, with applications, Discrete Mathematics 110 (1992) 289-292. We characterize sources in comparability graphs and show that our result provides a unifying look at two recent results about interval graphs. An orientation 0 of a graph G is obtained by assigning unique directions to its edges. To simplify notation, we write xy E 0, whenever the edge xy receives the direction from x to y. A vertex w is called a source whenever VW E 0 for no vertex II in G. -
Acyclic Orientations of Graphsଁ Richard P
Discrete Mathematics 306 (2006) 905–909 www.elsevier.com/locate/disc Acyclic orientations of graphsଁ Richard P. Stanley Department of Mathematics, University of California, Berkeley, Calif. 94720, USA Abstract Let G be a finite graph with p vertices and its chromatic polynomial. A combinatorial interpretation is given to the positive p p integer (−1) (−), where is a positive integer, in terms of acyclic orientations of G. In particular, (−1) (−1) is the number of acyclic orientations of G. An application is given to the enumeration of labeled acyclic digraphs. An algebra of full binomial type, in the sense of Doubilet–Rota–Stanley, is constructed which yields the generating functions which occur in the above context. © 1973 Published by Elsevier B.V. 1. The chromatic polynomial with negative arguments Let G be a finite graph, which we assume to be without loops or multiple edges. Let V = V (G) denote the set of vertices of G and X = X(G) the set of edges. An edge e ∈ X is thought of as an unordered pair {u, v} of two distinct vertices. The integers p and q denote the cardinalities of V and X, respectively. An orientation of G is an assignment of a direction to each edge {u, v}, denoted by u → v or v → u, as the case may be. An orientation of G is said to be acyclic if it has no directed cycles. Let () = (G, ) denote the chromatic polynomial of G evaluated at ∈ C.If is a non-negative integer, then () has the following rather unorthodox interpretation. Proposition 1.1. -
Parameterized Mixed Graph Coloring
Parameterized mixed graph coloring Downloaded from: https://research.chalmers.se, 2021-09-26 10:22 UTC Citation for the original published paper (version of record): Damaschke, P. (2019) Parameterized mixed graph coloring Journal of Combinatorial Optimization, 38(2): 362-374 http://dx.doi.org/10.1007/s10878-019-00388-z N.B. When citing this work, cite the original published paper. research.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004. research.chalmers.se is administrated and maintained by Chalmers Library (article starts on next page) Journal of Combinatorial Optimization (2019) 38:362–374 https://doi.org/10.1007/s10878-019-00388-z Parameterized Mixed Graph Coloring Peter Damaschke1 Published online: 7 February 2019 © The Author(s) 2019 Abstract Coloring of mixed graphs that contain both directed arcs and undirected edges is relevant for scheduling of unit-length jobs with precedence constraints and conflicts. The classic GHRV theorem (attributed to Gallai, Hasse, Roy, and Vitaver) relates graph coloring to longest paths. It can be extended to mixed graphs. In the present paper we further extend the GHRV theorem to weighted mixed graphs. As a byproduct this yields a kernel and a parameterized algorithm (with the number of undirected edges as parameter) that is slightly faster than the brute-force algorithm. The parameter is natural since the directed version is polynomial whereas the undirected version is NP- complete. Furthermore we point out a new polynomial case where the edges form a clique. -
COLORING and DEGENERACY for DETERMINING VERY LARGE and SPARSE DERIVATIVE MATRICES ASHRAFUL HUQ SUNY Bachelor of Science, Chittag
COLORING AND DEGENERACY FOR DETERMINING VERY LARGE AND SPARSE DERIVATIVE MATRICES ASHRAFUL HUQ SUNY Bachelor of Science, Chittagong University of Engineering & Technology, 2013 A Thesis Submitted to the School of Graduate Studies of the University of Lethbridge in Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE Department of Mathematics and Computer Science University of Lethbridge LETHBRIDGE, ALBERTA, CANADA c Ashraful Huq Suny, 2016 COLORING AND DEGENERACY FOR DETERMINING VERY LARGE AND SPARSE DERIVATIVE MATRICES ASHRAFUL HUQ SUNY Date of Defence: December 19, 2016 Dr. Shahadat Hossain Supervisor Professor Ph.D. Dr. Daya Gaur Committee Member Professor Ph.D. Dr. Robert Benkoczi Committee Member Associate Professor Ph.D. Dr. Howard Cheng Chair, Thesis Examination Com- Associate Professor Ph.D. mittee Dedication To My Parents. iii Abstract Estimation of large sparse Jacobian matrix is a prerequisite for many scientific and engi- neering problems. It is known that determining the nonzero entries of a sparse matrix can be modeled as a graph coloring problem. To find out the optimal partitioning, we have proposed a new algorithm that combines existing exact and heuristic algorithms. We have introduced degeneracy and maximum k-core for sparse matrices to solve the problem in stages. Our combined approach produce better results in terms of partitioning than DSJM and for some test instances, we report optimal partitioning for the first time. iv Acknowledgments I would like to express my deep acknowledgment and profound sense of gratitude to my supervisor Dr. Shahadat Hossain, for his continuous guidance, support, cooperation, and persistent encouragement throughout the journey of my MSc program. -
The Number of Orientations Having No Fixed Tournament
The number of orientations having no fixed tournament Noga Alon ∗ Raphael Yuster y Abstract Let T be a fixed tournament on k vertices. Let D(n; T ) denote the maximum number of tk 1(n) orientations of an n-vertex graph that have no copy of T . We prove that D(n; T ) = 2 − for all sufficiently (very) large n, where tk 1(n) is the maximum possible number of edges of a − graph on n vertices with no Kk, (determined by Tur´an'sTheorem). The proof is based on a directed version of Szemer´edi'sregularity lemma together with some additional ideas and tools from Extremal Graph Theory, and provides an example of a precise result proved by applying this lemma. For the two possible tournaments with three vertices we obtain separate proofs that n2=4 avoid the use of the regularity lemma and therefore show that in these cases D(n; T ) = 2b c already holds for (relatively) small values of n. 1 Introduction All graphs considered here are finite and simple. For standard terminology on undirected and directed graphs the reader is referred to [4]. Let T be some fixed tournament. An orientation of an undirected graph G = (V; E) is called T -free if it does not contain T as a subgraph. Let D(G; T ) denote the number of orientations of G that are T -free. Let D(n; T ) denote the maximum possible value of D(G; T ) where G is an n-vertex graph. In this paper we determine D(n; T ) precisely for every fixed tournament T and all sufficiently large n.