Acyclic orientations do not lead to optimal deadlock-free packet routing algorithms

Size: px
Start display at page:

Download "Acyclic orientations do not lead to optimal deadlock-free packet routing algorithms"

Transcription

1 Acyclic orientations do not lead to optimal deadloc-ree pacet routing algorithms Daniel Šteanovič 1 Department o Computer Science, Comenius University, Bratislava, Slovaia Abstract In this paper e consider the problem o designing deadloc-ree shortest-path routing algorithms. A design technique based on acyclic orientations has proven to be useul or many important topologies, e.g., meshes, tori, trees and hypercubes. It as not non hether this technique alays leads to algorithms using an asymptotically optimal number o buers. We sho this is not the case by presenting a graph o size N hich has a deadloc-ree shortest-path routing algorithm using O(1) buers, but every deadloc-ree shortest-path routing algorithm based on acyclic orientations requires (log N / log log N ) buers Elsevier Science B.V. All rights reserved. Keyords: Deadloc-ree pacet routing; Acyclic orientations; Interconnection netors 1. Introduction Devising deadloc-ree routing algorithms is an important problem in the netor design. A ide range o dierent algorithms and design techniques has been proposed [1,4 11]. In this paper e concentrate on an important class o deadloc-ree routing algorithms called buer-reservation algorithms [2, 3]. Given source, destination and current buer o a pacet a buer-reservation algorithm speciies a set o buers to hich the pacet may move. The pacet can move to any buer rom the speciied set provided that it is empty. It is non that or each netor there exists a deadloc-ree routing algorithm using only 2 buers This research has been partially supported by VEGA 1/4315/ steano@zeus.dcs.mph.uniba.s. per node [8]. Hoever this algorithm suers rom congestion, because it uses only a spanning tree o the netor. It is reasonable to consider routing algorithms using only shortest paths. For such algorithms it as shon in [2] that there exists a netor o size N hich requires (log log N ) buers per node. It is also non that or every netor o diameter D there exists a shortest-path deadloc-ree routing algorithm using D + 1 buers per node [9]. A technique or constructing deadloc-ree routing algorithms based on acyclic orientations has proven to be useul or many important topologies [9]. Moreover only e additional bits o data besides routing inormation are required by the algorithms designed using this technique. Thus a natural question is ho good the technique is in general. We present a graph o size N hich has deadloc-ree shortest-path routing algorithm using O(1) buers per node, but every deadloc-ree shortest-path routing algorithm based

2 on acyclic orientations requires (log N / log log N ) buers per node. The proos presented in this paper can be adapted to obtain the same result or the cubeconnected-cycles graph. 2. Preliminaries A communication netor is modeled by an undirected graph G = (V, E), here vertices in V represent nodes and edges in E represent bidirectional communication lins. Every edge is considered to comprise to opositely oriented arcs. Given source, destination and the buer currently holding the pacet a buer-reservation algorithm speciies a set o buers to hich the pacet may move. The pacet can move to any o the speciied buers, provided it is empty. A deadloc is a situation in hich a set o pacets can never reach their destination, because speciied buers o each pacet are occupied by other pacets rom the set. A buer reservation algorithm is called deadloc-ree i it does not allo a deadloc to occur. A routing algorithm is called shortest path i the pacets are alays routed along shortest paths. Given a graph G an all-to-all path system P is a collection o paths connecting every to vertices in G. Anorientation DG o G is a directed graph obtained rom G by replacing each undirected edge in G by an arc (i.e., {u, v} is replaced by either (u, v) or (v, u)). An orientation is called acyclic i it doesn t contain a cycle. An orientation cover o a path system P is a sequence o orientations DG 1,...,DG s such that every path p P can be ritten as a concatenation o s paths p 1,...,p s here p i is a path in DG i or i [s]. Anacyclic orientation cover is an orientation cover consisting o acyclic orientations. Let P be a shortest-path all-to-all path system o a graph G. I an acyclic orientation cover or P o size s exists, then there exists a shortest-path deadloc-ree routing algorithm using only s buers per vertex [9]. A routing algorithm designed using this technique is said to be based on acyclic orientations. I there is no restriction on ho a routing algorithm as designed then it is said to use plain buers. 3. A gap beteen acyclic orientations and plain buers In this section e present a graph hich has shortest-path deadloc-ree routing algorithm using only O(1) plain buers per vertex, but every shortestpath deadloc-ree routing algorithm based on acyclic orientations requires (log N / log log N ) buers per vertex. Consider the olloing machine M n : It has a oring tape ith n cells and a head hich can be positioned above any cell. Each cell contains one binary digit. In one step the head can change the content o the scanned cell (shule) or it can move to the let or to the right neighboring cell. The state o the machine M n is the position o the head and the content o the tape. Deinition 1. Let G n be the graph ith vertices corresponding to states o the machine M n and edges connecting to vertices i the corresponding states are reachable in one step o M n. Remar 2. I the tape o the machine as raparound the resulting graph ould be the cube-connectedcycles graph. It is possible to sho the same results or this graph ith slightly more complicated proos and orse constants Upper bound or plain buers Theorem 3. There exists a shortest-path deadlocree routing algorithm o the graph G n using only 8 buers per vertex. Proo. Let u, v be any to vertices o the graph G n. Moving along a shortest path rom u to v corresponds to changing the state u to the state v using the minimal number o steps. Let h u and h v be positions o the head in u and v. Moreover let l u,v and r u,v be positions o the letmost and the rightmost cell that have to be visited (every cell in hich tapes o u and v dier as ell as cells at positions h u and h v must be visited).

3 In every shortest path the head maes a movement in one o the olloing to orms: l u,v h u h v r u,v CASE 1 (h u h v ) l u,v h v h u r u,v CASE 2 (h u h v ) Thus according to the head movement each path can be divided into three phases (the irst and/or third phase can be empty) in hich the head moves in one direction. No e sho that eight buers per vertex suice. Each vertex has buers ith names 1, 2, 3, 4, 1, 2, 3, 4. A pacet moving along a path ith the head movement CASE 1 is initially put in the buer 1 and then it moves beteen buers according to these rules: let-arc (phase 1) any buer buer 1 right-arc (phase 2) any buer buer 2 let-arc (phase 3) any buer buer 3 shule-arc (all phases) buer a buer a No shortest path contains to consecutive shuleedges and thus the rule or the shule-arc is correct (a pacet that ants to move along a shule-arc cannot be in a buer ith a prime). A pacet moving along a path ith the head movement CASE 2 is initially put in buer 2 and moves beteen buers according to these rules: right-arc (phase 1) any buer buer 2 let-arc (phase 2) any buer buer 3 right-arc (phase 3) any buer buer 4 shule-arc (all phases) buer a buer a For the sae o contradiction suppose that there is a deadloc. Then there must be a sequence such that (v 1,b 1 ) (v 2,b 2 ) (v m,b m ) (v 1,b 1 ), here (u, a) (v, b) means that a pacet rom the buer a in the vertex u aits or the buer b in the vertex v. Observethat a pacetnevermovestoa buer ith smaller number. Thereore all buers b 1,...,b m must have the same number some a [4]. Whena pacet moves to a buer a it uses shule-arc and hen it moves to a buer a it uses a let-arc i a is odd and a right-arc i a is even. Thereore all buers b 1,...,b m must be a. Hoever a pacet never moves rom a to a, a contradiction Loer bound or acyclic orientations The vertex set o an n-dimensional hypercube consists o all binary strings o length n. To vertices are connected by an edge i they dier in exactly one bit. For any to vertices u, v a movement along path rom u to v corresponds to a sequence o changes o some bits. I the bits are changed in order rom let to right then the path is called monotone. The loer bound or acyclic orientations depends on the olloing lemma: Lemma 4. Let P be the all-to-all path system o an n-dimensional hypercube ith monotone paths. Every orientation cover or P has size (n/log n). Proo. Let DG 1,...,DG s be an orientation cover or P. We sho that the size o this cover must be large by simulating movement o pacets in the netor. Each vertex generates a pacet or every other vertex. The pacets then move along their paths according to the orientation DG 1, then according to DG 2 andsoonand inally ater moving according to DG s every pacet must be in its destination. We observe the state o the netor (positions o pacets) ater each orientation. Let d be a number 0 d n and v beavertex. By Sv d e denote the set o all vertices that can dier rom v only in the irst d bits and by Dv d the set o all vertices that can dier rom v only in the last n d bits. Given a state o the netor e call the vertex v active ith respect to d i or each vertex u Dv d there exists a pacet hich has to move to u through v. Clearly i v has not yet received some pacet p rom some vertex Sv d then v is active ith respect to d, because pacets rom to every vertex in Dv d travel together ith p until they reach v (the paths are monotone). A vertex hich is not active ith respect to d is called passive ith respect to d. Clearly a vertex passive ith respect to d must have already received all pacets rom all vertices in Sv d.

4 Let = log 2 n. We ill construct a sequence o hypercubes Q 0,...,Q n/ such that ater moving according to the ith orientation the ratio o passive vertices in Q i ith respect to i is at most i/2. This ould imply that, because i some vertices are active then some pacets are not in their destination. For each i the hypercube Q i ill have dimension n i and it ill consist o vertices starting ith some string rom {0, 1} i. Initially all vertices are active and thus e can tae Q 0 to be the hole hypercube. No e sho ho to construct Q i+1 rom Q i.letq i be an (n i)-dimensional sub-hypercube consisting o vertices starting ith some string x {0, 1} i and suppose that the netor is in state ater moving according to the ith orientation. Let M denote the number o passive vertices (rom Q i ) ith respect to i. Forany {0, 1} n (i+1) let A denote the set o active vertices (rom Q i ) ith respect to i such that they end ith. No e move the pacets along their paths according to the orientation DG i+1. Ho many vertices rom Q i ending ith can be passive ith respect to (i + 1) ater the movement? Let N denote the set o these vertices. Let l [] be such that 2 l N > 2 l 1 (the case N 1 is treated separately). By induction it is easy to sho that there exist distinct z 1,...,z l [] such that or each z j,j [l], there exist to vertices in N o the orm: u 0 : u 1 : x a 0 i z j b x a 1 i z j c n (i+1) n (i+1) here a,b,c are some binary strings. I there ere to vertices in A o the orm: v 0 : v 1 : x d 0 i z j x e 1 i z j n (i+1) n (i+1) here d,e, are some binary strings, then either a pacet rom v 0 to u 1 or a pacet rom v 1 to u 0 cannot move beteen source and destination in the orientation DG i+1, because these to pacets need to cross the edge beteen vertices: x a i z j n (i+1) in the opposite direction. This is a contradiction to our assumption that u 0 and u 1 ill be passive ater the movement and thus there cannot be such v 0,v 1 pair in A. Thereore or each j [l] the (i + z j )th bit o each vertex rom A is determined by bits (i + z j + 1),..., (i + 1). Thisimpliesthat A 2 l. For any l []: N ( 2 A ) 2 l l 1. The inequality holds also in case N 1. Summing the inequalities or each {0, 1} n (i+1) e obtain: N M 2 n (i+1), here N is the total number o vertices rom Q i passive ith respect to (i + 1) ater the movement. Clearly there exists a string q {0, 1} such that at most (M + 2 n (i+1) )/2 vertices starting ith xq are passive ith respect to (i + 1) ater the movement. Let Q i+1 be the hypercube consisting o vertices starting ith xq. The ratio o passive vertices increased rom R i = M 2 n i to R i+1 = (M + 2n (i+1) )/2 2 n (i+1) = R i Thereore i in Q i the ratio as at most i/2 then in Q i+1 the ratio ill be at most (i + 1)/2. Theorem 5. Any shortest-path deadloc-ree pacet routing algorithm o the graph G n based on acyclic orientations requires (n/log n) buers. Proo. Let S be the set o all vertices rom G n having the head on the letmost bit and D be the set o all vertices having the head on the rightmost bit. For every u S and v D there is a unique shortest path rom

5 u to v in G n. It corresponds to changing the bits in hich u and v dier hile moving the head to the right. Let P be the set o the shortest paths rom every u S to every v D. LetC = DG 1,...,DG s be an acyclic orientation cover used to implement shortestpath deadloc-ree pacet routing algorithm on G n. Clearly C is also an acyclic orientation cover or P. By merging the vertices having the same tape content e obtain the n-dimensional hypercube and the path system P becomes all-to-all path system o the n-dimensional hypercube ith monotone paths. The acyclic orientation cover C becomes orientation cover o the ne path system. Using Lemma 4 e obtain that the size o C must be (n/log n). A consequence o Theorems 3 and 5 is: Theorem 6. Graph G n o size N = n2 n has a deadloc-ree shortest-path routing algorithm using O(1) buers per node, but each deadloc-ree shortestpath routing algorithm based on acyclic orientations requires (log N / log log N ) buers per node. 4. Conclusion There are many results on buer-reservation algorithms or speciic topologies, but or general graphs very little is non. It ould be interesting to have better upper and loer bounds on the number o buers required or shortest-path deadloc-ree routing on general graphs. Concerning acyclic orientations it ould be interesting to no ho large the gap beteen routing algorithms based on acyclic orientations and routing algorithms using only plain buers can be. Acnoledgement Thans are due to Ivona Bezáová, Rastislav Kráľovič, Branislav Rovan, Peter Ružiča and Richard Tan or helpul discussions and or careully reading the drat o this paper. Reerences [1] B. Aerbuch, S. Kutten, D. Peleg, Eicient deadloc-ree routing, in: Proc. 10th Annual ACM Symposium on Principles o Distributed Computing, 1991, pp [2] R. Cypher, Minimal, deadloc-ree routing in hypercubic and arbitrary netors, in: Proc. 7th IEEE Symposium on Parallel and Distributed Processing, [3] R. Cypher, L. Gravano, Requirements or deadloc-ree, adaptive pacet routing, in: Proc. 14th Annual ACM Symposium on Principles o Distributed Computing, 1992, pp [4] W.J. Dally, C. Seitz, Deadloc-ree message routing in multiprocessor interconnection netors, IEEE Trans. Comput. 36 (5) (1987) [5] J. Duato, Deadloc-ree adaptive routing algorithms or multicomputers: Evaluation o a ne algorithm, in: Proc. 3rd IEEE Symposium on Parallel and Distributed Processing, [6] I.S. Gopal, Prevention o store-and-orard deadloc in computer netors, IEEE Trans. Commun. 33 (12) (1985) [7] K.D. Günther, Prevention o deadlocs in pacet-sitched data transport systems, IEEE Trans. Commun. 29 (4) (1981) [8] P.M. Merlin, P.J. Scheitzer, Deadloc avoidance in store-andorard netors, IEEE Trans. Commun. 28 (3) (1980) [9] G. Tel, Deadloc-ree pacet routing, in: Introduction to Distributed Algorithms, Cambridge University Press, Cambridge, UK, 1994, Chapter 5. [10] S. Toueg, K. Steiglitz, Some complexity results in the design o deadloc-ree pacet sitching netors, SIAM J. Comput. 10 (4) (1981) [11] S. Toueg, J.D. Ullman, Deadloc-ree pacet sitching netors, SIAM J. Comput. 10 (3) (1981)

Routing and Wavelength Assignment in Multifiber WDM Networks with Non-Uniform Fiber Cost

Routing and Wavelength Assignment in Multifiber WDM Networks with Non-Uniform Fiber Cost Routing and Wavelength Assignment in Multiiber WDM Netorks ith Non-Uniorm Fiber Cost Christos Nomikos a, Aris Pagourtzis b, Katerina Potika b and Stathis Zachos b a Department o Computer Science, University

More information

On the Complexity of Multi-Dimensional Interval Routing Schemes

On the Complexity of Multi-Dimensional Interval Routing Schemes On the Complexity of Multi-Dimensional Interval Routing Schemes Abstract Multi-dimensional interval routing schemes (MIRS) introduced in [4] are an extension of interval routing schemes (IRS). We give

More information

PARALLEL LAGRANGE INTERPOLATION ON EXTENDED FIBONACCI CUBES

PARALLEL LAGRANGE INTERPOLATION ON EXTENDED FIBONACCI CUBES STUDIA UNIV. BABEŞ BOLYAI, INFORMATICA, Volume L, Number 1, 2005 PARALLEL LAGRANGE INTERPOLATION ON EXTENDED FIBONACCI CUBES IOANA ZELINA Abstract. In this paper is presented a parallel algorithm for computing

More information

Universal Turing Machine Chomsky Hierarchy Decidability Reducibility Uncomputable Functions Rice s Theorem Decidability Continued

Universal Turing Machine Chomsky Hierarchy Decidability Reducibility Uncomputable Functions Rice s Theorem Decidability Continued CD5080 AUBER odels of Computation, anguages and Automata ecture 14 älardalen University Content Universal Turing achine Chomsky Hierarchy Decidability Reducibility Uncomputable Functions Rice s Decidability

More information

CS 161: Design and Analysis of Algorithms

CS 161: Design and Analysis of Algorithms CS 161: Design and Analysis o Algorithms Announcements Homework 3, problem 3 removed Greedy Algorithms 4: Human Encoding/Set Cover Human Encoding Set Cover Alphabets and Strings Alphabet = inite set o

More information

WHEN DO THREE LONGEST PATHS HAVE A COMMON VERTEX?

WHEN DO THREE LONGEST PATHS HAVE A COMMON VERTEX? WHEN DO THREE LONGEST PATHS HAVE A COMMON VERTEX? MARIA AXENOVICH Abstract. It is well known that any two longest paths in a connected graph share a vertex. It is also known that there are connected graphs

More information

SDSU CS 662 Theory of Parallel Algorithms Networks part 2

SDSU CS 662 Theory of Parallel Algorithms Networks part 2 SDSU CS 662 Theory of Parallel Algorithms Networks part 2 ---------- [To Lecture Notes Index] San Diego State University -- This page last updated April 16, 1996 Contents of Networks part 2 Lecture 1.

More information

CSE 331: Introduction to Algorithm Analysis and Design Graphs

CSE 331: Introduction to Algorithm Analysis and Design Graphs CSE 331: Introduction to Algorithm Analysis and Design Graphs 1 Graph Definitions Graph: A graph consists of a set of verticies V and a set of edges E such that: G = (V, E) V = {v 0, v 1,..., v n 1 } E

More information

Sparse Hypercube 3-Spanners

Sparse Hypercube 3-Spanners Sparse Hypercube 3-Spanners W. Duckworth and M. Zito Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria 3052, Australia Department of Computer Science, University of

More information

Broadcast on Clusters of SMPs with Optimal Concurrency

Broadcast on Clusters of SMPs with Optimal Concurrency Broadcast on Clusters of SMPs ith Optimal Concurrency Yuzhong Sun +, David Bader +, and X. Lin * + Department of Electrical and Computer Engineering University of Ne Mexico * Department of Electrical and

More information

The Postal Network: A Versatile Interconnection Topology

The Postal Network: A Versatile Interconnection Topology The Postal Network: A Versatile Interconnection Topology Jie Wu Yuanyuan Yang Dept. of Computer Sci. and Eng. Dept. of Computer Science Florida Atlantic University University of Vermont Boca Raton, FL

More information

K d (490) models characterisation Issue 1 revision 0 Gilbert Barrot, ACRI-ST 05/jul/2006

K d (490) models characterisation Issue 1 revision 0 Gilbert Barrot, ACRI-ST 05/jul/2006 K d (490) models characterisation Issue 1 revision 0 Gilbert Barrot, ACRI-ST 05/jul/2006 K d (490) is the diffuse attenuation coefficient at 490 nm. It is one indicator of the turbidity of the ater column.

More information

Lecture 22 Tuesday, April 10

Lecture 22 Tuesday, April 10 CIS 160 - Spring 2018 (instructor Val Tannen) Lecture 22 Tuesday, April 10 GRAPH THEORY Directed Graphs Directed graphs (a.k.a. digraphs) are an important mathematical modeling tool in Computer Science,

More information

1. [1 pt] What is the solution to the recurrence T(n) = 2T(n-1) + 1, T(1) = 1

1. [1 pt] What is the solution to the recurrence T(n) = 2T(n-1) + 1, T(1) = 1 Asymptotics, Recurrence and Basic Algorithms 1. [1 pt] What is the solution to the recurrence T(n) = 2T(n-1) + 1, T(1) = 1 1. O(logn) 2. O(n) 3. O(nlogn) 4. O(n 2 ) 5. O(2 n ) 2. [1 pt] What is the solution

More information

On Time versus Size for Monotone Dynamic Monopolies in Regular Topologies 1

On Time versus Size for Monotone Dynamic Monopolies in Regular Topologies 1 On Time versus Size for Monotone Dynamic Monopolies in Regular Topologies 1 Paola Flocchini, School of Information Technology and Engineering, University of Ottawa (flocchin@site.uottawa.ca Rastislav Královič,

More information

22 Elementary Graph Algorithms. There are two standard ways to represent a

22 Elementary Graph Algorithms. There are two standard ways to represent a VI Graph Algorithms Elementary Graph Algorithms Minimum Spanning Trees Single-Source Shortest Paths All-Pairs Shortest Paths 22 Elementary Graph Algorithms There are two standard ways to represent a graph

More information

MC 302 GRAPH THEORY 10/1/13 Solutions to HW #2 50 points + 6 XC points

MC 302 GRAPH THEORY 10/1/13 Solutions to HW #2 50 points + 6 XC points MC 0 GRAPH THEORY 0// Solutions to HW # 0 points + XC points ) [CH] p.,..7. This problem introduces an important class of graphs called the hypercubes or k-cubes, Q, Q, Q, etc. I suggest that before you

More information

Automatic Deployment and Formation Control of Decentralized Multi-Agent Networks

Automatic Deployment and Formation Control of Decentralized Multi-Agent Networks Automatic Deployment and Formation Control of Decentralized Multi-Agent Netorks Brian S. Smith, Magnus Egerstedt, and Ayanna Hoard Abstract Novel tools are needed to deploy multi-agent netorks in applications

More information

CS473-Algorithms I. Lecture 13-A. Graphs. Cevdet Aykanat - Bilkent University Computer Engineering Department

CS473-Algorithms I. Lecture 13-A. Graphs. Cevdet Aykanat - Bilkent University Computer Engineering Department CS473-Algorithms I Lecture 3-A Graphs Graphs A directed graph (or digraph) G is a pair (V, E), where V is a finite set, and E is a binary relation on V The set V: Vertex set of G The set E: Edge set of

More information

10.2 Single-Slit Diffraction

10.2 Single-Slit Diffraction 10. Single-Slit Diffraction If you shine a beam of light through a ide-enough opening, you might expect the beam to pass through ith very little diffraction. Hoever, hen light passes through a progressively

More information

POWER CONSUMPTION OPTIMIZATION AND DELAY BASED ON ANT COLONY ALGORITHM IN NETWORK-ON-CHIP

POWER CONSUMPTION OPTIMIZATION AND DELAY BASED ON ANT COLONY ALGORITHM IN NETWORK-ON-CHIP Engineering Revie Vol. 33, Issue 3, 219-225, 2013. 219 POWER CONSUMPTION OPTIMIZATION AND DELAY BASED ON ANT COLONY ALGORITHM IN NETWORK-ON-CHIP T. He 1* Y. Guo 1 1 National Digital Sitching System Engineering

More information

Graph Learning with a Nearest Neighbor Approach

Graph Learning with a Nearest Neighbor Approach Graph Learning ith a Nearest Neighbor Approach Sven Koenig and Yury Smirnov School of Computer Science, Carnegie Mellon University Pittsburgh, PA 53-389, USA skoenig@cs.cmu.edu, smir@cs.cmu.edu Abstract

More information

22 Elementary Graph Algorithms. There are two standard ways to represent a

22 Elementary Graph Algorithms. There are two standard ways to represent a VI Graph Algorithms Elementary Graph Algorithms Minimum Spanning Trees Single-Source Shortest Paths All-Pairs Shortest Paths 22 Elementary Graph Algorithms There are two standard ways to represent a graph

More information

Problem Set 2 Solutions

Problem Set 2 Solutions Design and Analysis of Algorithms February, 01 Massachusetts Institute of Technology 6.046J/18.410J Profs. Dana Moshkovitz and Bruce Tidor Handout 8 Problem Set Solutions This problem set is due at 9:00pm

More information

DO NOT RE-DISTRIBUTE THIS SOLUTION FILE

DO NOT RE-DISTRIBUTE THIS SOLUTION FILE Professor Kindred Math 104, Graph Theory Homework 2 Solutions February 7, 2013 Introduction to Graph Theory, West Section 1.2: 26, 38, 42 Section 1.3: 14, 18 Section 2.1: 26, 29, 30 DO NOT RE-DISTRIBUTE

More information

Node-to-Set Vertex Disjoint Paths in Hypercube Networks

Node-to-Set Vertex Disjoint Paths in Hypercube Networks Computer Science Technical Report Node-to-Set Vertex Disjoint Paths in Hypercube Networks Shahram Latifi y, Hyosun Ko z, and Pradip K Srimani x Technical Report CS-98-7 Computer Science Department Colorado

More information

Fault-Tolerant Routing Algorithm in Meshes with Solid Faults

Fault-Tolerant Routing Algorithm in Meshes with Solid Faults Fault-Tolerant Routing Algorithm in Meshes with Solid Faults Jong-Hoon Youn Bella Bose Seungjin Park Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science Oregon State University

More information

arxiv:cs/ v1 [cs.ds] 18 May 2002

arxiv:cs/ v1 [cs.ds] 18 May 2002 arxiv:cs/0205041v1 [cs.ds] 18 May 2002 Faster Parametric Shortest Path and Minimum Balance Algorithms Neal E. Young Computer Science Department, Princeton University, Princeton, Ne Jersey, 08544 Robert

More information

Number Theory and Graph Theory

Number Theory and Graph Theory 1 Number Theory and Graph Theory Chapter 6 Basic concepts and definitions of graph theory By A. Satyanarayana Reddy Department of Mathematics Shiv Nadar University Uttar Pradesh, India E-mail: satya8118@gmail.com

More information

Characterizations of Trees

Characterizations of Trees Characterizations of Trees Lemma Every tree with at least two vertices has at least two leaves. Proof. 1. A connected graph with at least two vertices has an edge. 2. In an acyclic graph, an end point

More information

Fast and Scalable Conflict Detection for Packet Classifiers

Fast and Scalable Conflict Detection for Packet Classifiers Fast and Scalable Conflict Detection for Packet Classifiers Florin Baboescu, George Varghese Dept. of Computer Science and Engineering University of California, San Diego 95 Gilman Drive La Jolla, CA9293-4

More information

Extended Graph Rotation Systems as a Model for Cyclic Weaving on Orientable Surfaces

Extended Graph Rotation Systems as a Model for Cyclic Weaving on Orientable Surfaces Extended Graph Rotation Systems as a Model for Cyclic Weaving on Orientable Surfaces ERGUN AKLEMAN, JIANER CHEN Texas A&M University, College Station, Texas and JONATHAN L. GROSS Columbia University, Ne

More information

Data Communication and Parallel Computing on Twisted Hypercubes

Data Communication and Parallel Computing on Twisted Hypercubes Data Communication and Parallel Computing on Twisted Hypercubes E. Abuelrub, Department of Computer Science, Zarqa Private University, Jordan Abstract- Massively parallel distributed-memory architectures

More information

A Distributed Formation of Orthogonal Convex Polygons in Mesh-Connected Multicomputers

A Distributed Formation of Orthogonal Convex Polygons in Mesh-Connected Multicomputers A Distributed Formation of Orthogonal Convex Polygons in Mesh-Connected Multicomputers Jie Wu Department of Computer Science and Engineering Florida Atlantic University Boca Raton, FL 3343 Abstract The

More information

Characterization of Networks Supporting Multi-dimensional Linear Interval Routing Scheme

Characterization of Networks Supporting Multi-dimensional Linear Interval Routing Scheme Characterization of Networks Supporting Multi-dimensional Linear Interval Routing Scheme YASHAR GANJALI Department of Computer Science University of Waterloo Canada Abstract An Interval routing scheme

More information

A Robust Method of Facial Feature Tracking for Moving Images

A Robust Method of Facial Feature Tracking for Moving Images A Robust Method of Facial Feature Tracking for Moving Images Yuka Nomura* Graduate School of Interdisciplinary Information Studies, The University of Tokyo Takayuki Itoh Graduate School of Humanitics and

More information

Shortest Path Problem

Shortest Path Problem Shortest Path Problem CLRS Chapters 24.1 3, 24.5, 25.2 Shortest path problem Shortest path problem (and variants) Properties of shortest paths Algorithmic framework Bellman-Ford algorithm Shortest paths

More information

A New Theory of Deadlock-Free Adaptive Multicast Routing in. Wormhole Networks. J. Duato. Facultad de Informatica. Universidad Politecnica de Valencia

A New Theory of Deadlock-Free Adaptive Multicast Routing in. Wormhole Networks. J. Duato. Facultad de Informatica. Universidad Politecnica de Valencia A New Theory of Deadlock-Free Adaptive Multicast Routing in Wormhole Networks J. Duato Facultad de Informatica Universidad Politecnica de Valencia P.O.B. 22012, 46071 - Valencia, SPAIN E-mail: jduato@aii.upv.es

More information

Localized Construction of Bounded Degree and Planar Spanner for Wireless Ad Hoc Networks

Localized Construction of Bounded Degree and Planar Spanner for Wireless Ad Hoc Networks 1 Localized Construction of Bounded Degree and Planar Spanner for Wireless Ad Hoc Netorks Yu Wang Xiang-Yang Li Department of Computer Science, University of North Carolina at Charlotte, 9201 University

More information

Discrete Mathematics

Discrete Mathematics Discrete Mathematics 310 (2010) 2769 2775 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: www.elsevier.com/locate/disc Optimal acyclic edge colouring of grid like graphs

More information

Generic Methodologies for Deadlock-Free Routing

Generic Methodologies for Deadlock-Free Routing Generic Methodologies for Deadlock-Free Routing Hyunmin Park Dharma P. Agrawal Department of Computer Engineering Electrical & Computer Engineering, Box 7911 Myongji University North Carolina State University

More information

Scheduling in Multihop Wireless Networks without Back-pressure

Scheduling in Multihop Wireless Networks without Back-pressure Forty-Eighth Annual Allerton Conerence Allerton House, UIUC, Illinois, USA September 29 - October 1, 2010 Scheduling in Multihop Wireless Networks without Back-pressure Shihuan Liu, Eylem Ekici, and Lei

More information

{ 1} Definitions. 10. Extremal graph theory. Problem definition Paths and cycles Complete subgraphs

{ 1} Definitions. 10. Extremal graph theory. Problem definition Paths and cycles Complete subgraphs Problem definition Paths and cycles Complete subgraphs 10. Extremal graph theory 10.1. Definitions Let us examine the following forbidden subgraph problems: At most how many edges are in a graph of order

More information

Communication Networks I December 4, 2001 Agenda Graph theory notation Trees Shortest path algorithms Distributed, asynchronous algorithms Page 1

Communication Networks I December 4, 2001 Agenda Graph theory notation Trees Shortest path algorithms Distributed, asynchronous algorithms Page 1 Communication Networks I December, Agenda Graph theory notation Trees Shortest path algorithms Distributed, asynchronous algorithms Page Communication Networks I December, Notation G = (V,E) denotes a

More information

Optimal Topology for Distributed Shared-Memory. Multiprocessors: Hypercubes Again? Jose Duato and M.P. Malumbres

Optimal Topology for Distributed Shared-Memory. Multiprocessors: Hypercubes Again? Jose Duato and M.P. Malumbres Optimal Topology for Distributed Shared-Memory Multiprocessors: Hypercubes Again? Jose Duato and M.P. Malumbres Facultad de Informatica, Universidad Politecnica de Valencia P.O.B. 22012, 46071 - Valencia,

More information

Lecture 12 March 16, 2010

Lecture 12 March 16, 2010 6.851: Advanced Data Structures Spring 010 Prof. Erik Demaine Lecture 1 March 16, 010 1 Overvie In the last lecture e covered the round elimination technique and loer bounds on the static predecessor problem.

More information

Constant Queue Routing on a Mesh

Constant Queue Routing on a Mesh Constant Queue Routing on a Mesh Sanguthevar Rajasekaran Richard Overholt Dept. of Computer and Information Science Univ. of Pennsylvania, Philadelphia, PA 19104 ABSTRACT Packet routing is an important

More information

Introduction to Packet Switching

Introduction to Packet Switching S 5 esign and nalysis of ing Systems onathan Turner Introduction to Pacet ing Review Questions. Let be the cost of a lin between two points in a networ and let n+ n be the cost of a switch or router with

More information

Department of Computer Science. a vertex can communicate with a particular neighbor. succeeds if it shares no edge with other calls during

Department of Computer Science. a vertex can communicate with a particular neighbor. succeeds if it shares no edge with other calls during Sparse Hypercube A Minimal k-line Broadcast Graph Satoshi Fujita Department of Electrical Engineering Hiroshima University Email: fujita@se.hiroshima-u.ac.jp Arthur M. Farley Department of Computer Science

More information

Mixture models and clustering

Mixture models and clustering 1 Lecture topics: Miture models and clustering, k-means Distance and clustering Miture models and clustering We have so far used miture models as fleible ays of constructing probability models for prediction

More information

Application of neural networks to model catamaran type powerboats

Application of neural networks to model catamaran type powerboats Application of eural etors to model Catamaran Type Poerboats Application of neural netors to model catamaran type poerboats Garron Fish Mie Dempsey Claytex Services Ltd Edmund House, Rugby Road, Leamington

More information

Internet Technology. 08. Routing. Paul Krzyzanowski. Rutgers University. Spring CS Paul Krzyzanowski

Internet Technology. 08. Routing. Paul Krzyzanowski. Rutgers University. Spring CS Paul Krzyzanowski Internet Technolog 08. Routing Paul Kranoski Rutgers Universit Spring 06 March, 06 CS 0-06 Paul Kranoski Routing algorithm goal st hop router = source router last hop router = destination router router

More information

A Reduction of Conway s Thrackle Conjecture

A Reduction of Conway s Thrackle Conjecture A Reduction of Conway s Thrackle Conjecture Wei Li, Karen Daniels, and Konstantin Rybnikov Department of Computer Science and Department of Mathematical Sciences University of Massachusetts, Lowell 01854

More information

Global Constraints. Combinatorial Problem Solving (CPS) Enric Rodríguez-Carbonell (based on materials by Javier Larrosa) February 22, 2019

Global Constraints. Combinatorial Problem Solving (CPS) Enric Rodríguez-Carbonell (based on materials by Javier Larrosa) February 22, 2019 Global Constraints Combinatorial Problem Solving (CPS) Enric Rodríguez-Carbonell (based on materials by Javier Larrosa) February 22, 2019 Global Constraints Global constraints are classes o constraints

More information

Today. Types of graphs. Complete Graphs. Trees. Hypercubes.

Today. Types of graphs. Complete Graphs. Trees. Hypercubes. Today. Types of graphs. Complete Graphs. Trees. Hypercubes. Complete Graph. K n complete graph on n vertices. All edges are present. Everyone is my neighbor. Each vertex is adjacent to every other vertex.

More information

Constructions of hamiltonian graphs with bounded degree and diameter O(log n)

Constructions of hamiltonian graphs with bounded degree and diameter O(log n) Constructions of hamiltonian graphs with bounded degree and diameter O(log n) Aleksandar Ilić Faculty of Sciences and Mathematics, University of Niš, Serbia e-mail: aleksandari@gmail.com Dragan Stevanović

More information

The Prediction of Real estate Price Index based on Improved Neural Network Algorithm

The Prediction of Real estate Price Index based on Improved Neural Network Algorithm , pp.0-5 http://dx.doi.org/0.457/astl.05.8.03 The Prediction of Real estate Price Index based on Improved Neural Netor Algorithm Huan Ma, Ming Chen and Jianei Zhang Softare Engineering College, Zhengzhou

More information

Discrete mathematics II. - Graphs

Discrete mathematics II. - Graphs Emil Vatai April 25, 2018 Basic definitions Definition of an undirected graph Definition (Undirected graph) An undirected graph or (just) a graph is a triplet G = (ϕ, E, V ), where V is the set of vertices,

More information

Hyper-Butterfly Network: A Scalable Optimally Fault Tolerant Architecture

Hyper-Butterfly Network: A Scalable Optimally Fault Tolerant Architecture Hyper-Butterfly Network: A Scalable Optimally Fault Tolerant Architecture Wei Shi and Pradip K Srimani Department of Computer Science Colorado State University Ft. Collins, CO 80523 Abstract Bounded degree

More information

Sources for this lecture 2. Shortest paths and minimum spanning trees

Sources for this lecture 2. Shortest paths and minimum spanning trees S-72.2420 / T-79.5203 Shortest paths and minimum spanning trees 1 S-72.2420 / T-79.5203 Shortest paths and minimum spanning trees 3 Sources for this lecture 2. Shortest paths and minimum spanning trees

More information

Decreasing the Diameter of Bounded Degree Graphs

Decreasing the Diameter of Bounded Degree Graphs Decreasing the Diameter of Bounded Degree Graphs Noga Alon András Gyárfás Miklós Ruszinkó February, 00 To the memory of Paul Erdős Abstract Let f d (G) denote the minimum number of edges that have to be

More information

Lecture 2 - Graph Theory Fundamentals - Reachability and Exploration 1

Lecture 2 - Graph Theory Fundamentals - Reachability and Exploration 1 CME 305: Discrete Mathematics and Algorithms Instructor: Professor Aaron Sidford (sidford@stanford.edu) January 11, 2018 Lecture 2 - Graph Theory Fundamentals - Reachability and Exploration 1 In this lecture

More information

PARTICLE SWARM OPTIMIZATION (PSO) [1] is an

PARTICLE SWARM OPTIMIZATION (PSO) [1] is an Proceedings of International Joint Conference on Neural Netorks, Atlanta, Georgia, USA, June -9, 9 Netork-Structured Particle Sarm Optimizer Considering Neighborhood Relationships Haruna Matsushita and

More information

CPS 102: Discrete Mathematics. Quiz 3 Date: Wednesday November 30, Instructor: Bruce Maggs NAME: Prob # Score. Total 60

CPS 102: Discrete Mathematics. Quiz 3 Date: Wednesday November 30, Instructor: Bruce Maggs NAME: Prob # Score. Total 60 CPS 102: Discrete Mathematics Instructor: Bruce Maggs Quiz 3 Date: Wednesday November 30, 2011 NAME: Prob # Score Max Score 1 10 2 10 3 10 4 10 5 10 6 10 Total 60 1 Problem 1 [10 points] Find a minimum-cost

More information

Paths, Flowers and Vertex Cover

Paths, Flowers and Vertex Cover Paths, Flowers and Vertex Cover Venkatesh Raman, M.S. Ramanujan, and Saket Saurabh Presenting: Hen Sender 1 Introduction 2 Abstract. It is well known that in a bipartite (and more generally in a Konig)

More information

Management of Secret Keys: Dynamic Key Handling

Management of Secret Keys: Dynamic Key Handling Management of Secret Keys: Dynamic Key Handling Joan Daemen Banksys Haachtesteeneg 1442 B-1130 Brussel, Belgium Daemen.J@banksys.be Abstract. In this paper e describe mechanisms for the management of secret

More information

Prefix Computation and Sorting in Dual-Cube

Prefix Computation and Sorting in Dual-Cube Prefix Computation and Sorting in Dual-Cube Yamin Li and Shietung Peng Department of Computer Science Hosei University Tokyo - Japan {yamin, speng}@k.hosei.ac.jp Wanming Chu Department of Computer Hardware

More information

Embedding Large Complete Binary Trees in Hypercubes with Load Balancing

Embedding Large Complete Binary Trees in Hypercubes with Load Balancing JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 35, 104 109 (1996) ARTICLE NO. 0073 Embedding Large Complete Binary Trees in Hypercubes with Load Balancing KEMAL EFE Center for Advanced Computer Studies,

More information

Finding a -regular Supergraph of Minimum Order

Finding a -regular Supergraph of Minimum Order Finding a -regular Supergraph of Minimum Order Hans L. Bodlaender a, Richard B. Tan a,b and Jan van Leeuwen a a Department of Computer Science Utrecht University Padualaan 14, 3584 CH Utrecht The Netherlands

More information

Integrality Gap of the Hypergraphic Relaxation of Steiner Trees: a short proof of a 1.55 upper bound

Integrality Gap of the Hypergraphic Relaxation of Steiner Trees: a short proof of a 1.55 upper bound Integrality Gap o the Hypergraphic Relaxation o Steiner Trees: a short proo o a.55 upper ound Deeparna Chakraarty University o Pennsylvania Jochen önemann University o Waterloo David Pritchard École Polytechnique

More information

Pebbling on Directed Graphs

Pebbling on Directed Graphs Pebbling on Directed Graphs Gayatri Gunda E-mail: gundagay@notes.udayton.edu Dr. Aparna Higgins E-mail: Aparna.Higgins@notes.udayton.edu University of Dayton Dayton, OH 45469 Submitted January 25 th, 2004

More information

Optimal Oblivious Path Selection on the Mesh

Optimal Oblivious Path Selection on the Mesh Optimal Oblivious Path Selection on the Mesh Costas Busch, Member, IEEE, Malik Magdon-Ismail, Member, IEEE, Jing Xi 1 Abstract In the oblivious path selection problem, each packet in the network independently

More information

Heap-on-Top Priority Queues. March Abstract. We introduce the heap-on-top (hot) priority queue data structure that combines the

Heap-on-Top Priority Queues. March Abstract. We introduce the heap-on-top (hot) priority queue data structure that combines the Heap-on-Top Priority Queues Boris V. Cherkassky Central Economics and Mathematics Institute Krasikova St. 32 117418, Moscow, Russia cher@cemi.msk.su Andrew V. Goldberg NEC Research Institute 4 Independence

More information

Assignment 4 Solutions of graph problems

Assignment 4 Solutions of graph problems Assignment 4 Solutions of graph problems 1. Let us assume that G is not a cycle. Consider the maximal path in the graph. Let the end points of the path be denoted as v 1, v k respectively. If either of

More information

Extended Graph Rotation Systems as a Model for Cyclic Weaving on Orientable Surfaces

Extended Graph Rotation Systems as a Model for Cyclic Weaving on Orientable Surfaces Extended Graph Rotation Systems as a Model for Cyclic Weaving on Orientable Surfaces ERGUN AKLEMAN, JIANER CHEN Texas A&M University, College Station, Texas JONATHAN L. GROSS Columbia University, Ne York

More information

Basic Graph Theory with Applications to Economics

Basic Graph Theory with Applications to Economics Basic Graph Theory with Applications to Economics Debasis Mishra February, 0 What is a Graph? Let N = {,..., n} be a finite set. Let E be a collection of ordered or unordered pairs of distinct elements

More information

Theorem 2.9: nearest addition algorithm

Theorem 2.9: nearest addition algorithm There are severe limits on our ability to compute near-optimal tours It is NP-complete to decide whether a given undirected =(,)has a Hamiltonian cycle An approximation algorithm for the TSP can be used

More information

Shortest Path Routing Communications networks as graphs Graph terminology Breadth-first search in a graph Properties of breadth-first search

Shortest Path Routing Communications networks as graphs Graph terminology Breadth-first search in a graph Properties of breadth-first search Shortest Path Routing Communications networks as graphs Graph terminology Breadth-first search in a graph Properties of breadth-first search 6.082 Fall 2006 Shortest Path Routing, Slide 1 Routing in an

More information

Restricted Delivery Problems on a Network. December 17, Abstract

Restricted Delivery Problems on a Network. December 17, Abstract Restricted Delivery Problems on a Network Esther M. Arkin y, Refael Hassin z and Limor Klein x December 17, 1996 Abstract We consider a delivery problem on a network one is given a network in which nodes

More information

Algorithm Design and Analysis

Algorithm Design and Analysis Algorithm Design and Analysis LECTURE 4 Graphs Definitions Traversals Adam Smith 9/8/10 Exercise How can you simulate an array with two unbounded stacks and a small amount of memory? (Hint: think of a

More information

Problem Set 2 Solutions

Problem Set 2 Solutions Problem Set 2 Solutions Graph Theory 2016 EPFL Frank de Zeeuw & Claudiu Valculescu 1. Prove that the following statements about a graph G are equivalent. - G is a tree; - G is minimally connected (it is

More information

Maximal Monochromatic Geodesics in an Antipodal Coloring of Hypercube

Maximal Monochromatic Geodesics in an Antipodal Coloring of Hypercube Maximal Monochromatic Geodesics in an Antipodal Coloring of Hypercube Kavish Gandhi April 4, 2015 Abstract A geodesic in the hypercube is the shortest possible path between two vertices. Leader and Long

More information

Larger K-maps. So far we have only discussed 2 and 3-variable K-maps. We can now create a 4-variable map in the

Larger K-maps. So far we have only discussed 2 and 3-variable K-maps. We can now create a 4-variable map in the EET 3 Chapter 3 7/3/2 PAGE - 23 Larger K-maps The -variable K-map So ar we have only discussed 2 and 3-variable K-maps. We can now create a -variable map in the same way that we created the 3-variable

More information

Copyright 2000, Kevin Wayne 1

Copyright 2000, Kevin Wayne 1 Chapter 3 - Graphs Undirected Graphs Undirected graph. G = (V, E) V = nodes. E = edges between pairs of nodes. Captures pairwise relationship between objects. Graph size parameters: n = V, m = E. Directed

More information

From Static to Dynamic Routing: Efficient Transformations of Store-and-Forward Protocols

From Static to Dynamic Routing: Efficient Transformations of Store-and-Forward Protocols SIAM Journal on Computing to appear From Static to Dynamic Routing: Efficient Transformations of StoreandForward Protocols Christian Scheideler Berthold Vöcking Abstract We investigate how static storeandforward

More information

4.5 Routing Algorithms

4.5 Routing Algorithms 4.5 ROUTING ALGORITHMS 363 to hosts enjo the securit services provided b IPsec. On the sending side, the transport laer passes a segment to IPsec. IPsec then encrpts the segment, appends additional securit

More information

A dynamic programming algorithm for perceptually consistent stereo

A dynamic programming algorithm for perceptually consistent stereo A dynamic programming algorithm for perceptually consistent stereo The Harvard community has made this article openly available. Please share ho this access benefits you. Your story matters. Citation Accessed

More information

3.1 Basic Definitions and Applications

3.1 Basic Definitions and Applications Graphs hapter hapter Graphs. Basic efinitions and Applications Graph. G = (V, ) n V = nodes. n = edges between pairs of nodes. n aptures pairwise relationship between objects: Undirected graph represents

More information

Component Connectivity of Generalized Petersen Graphs

Component Connectivity of Generalized Petersen Graphs March 11, 01 International Journal of Computer Mathematics FeHa0 11 01 To appear in the International Journal of Computer Mathematics Vol. 00, No. 00, Month 01X, 1 1 Component Connectivity of Generalized

More information

7.3 Spanning trees Spanning trees [ ] 61

7.3 Spanning trees Spanning trees [ ] 61 7.3. Spanning trees [161211-1348 ] 61 7.3 Spanning trees We know that trees are connected graphs with the minimal number of edges. Hence trees become very useful in applications where our goal is to connect

More information

Non-Adaptive Data Structure Bounds for Dynamic Predecessor Search

Non-Adaptive Data Structure Bounds for Dynamic Predecessor Search Non-Adaptive Data Structure Bounds for Dynamic Predecessor Search Joseph Boninger 1, Joshua Brody 2, and Oen Kephart 3 1 Sarthmore College, Sarthmore, PA, USA jboning1@sarthmore.edu 2 Sarthmore College,

More information

ACO Comprehensive Exam March 19 and 20, Computability, Complexity and Algorithms

ACO Comprehensive Exam March 19 and 20, Computability, Complexity and Algorithms 1. Computability, Complexity and Algorithms Bottleneck edges in a flow network: Consider a flow network on a directed graph G = (V,E) with capacities c e > 0 for e E. An edge e E is called a bottleneck

More information

Improved upper and lower bounds on the feedback vertex numbers of grids and butterflies

Improved upper and lower bounds on the feedback vertex numbers of grids and butterflies Improved upper and lower bounds on the feedback vertex numbers of grids and butterflies Florent R. Madelaine and Iain A. Stewart Department of Computer Science, Durham University, Science Labs, South Road,

More information

5 Graphs

5 Graphs 5 Graphs jacques@ucsd.edu Some of the putnam problems are to do with graphs. They do not assume more than a basic familiarity with the definitions and terminology of graph theory. 5.1 Basic definitions

More information

Efficient Bufferless Packet Switching on Trees and Leveled Networks

Efficient Bufferless Packet Switching on Trees and Leveled Networks Efficient Bufferless Packet Switching on Trees and Leveled Networks Costas Busch Malik Magdon-Ismail Marios Mavronicolas Abstract In bufferless networks the packets cannot be buffered while they are in

More information

Computing minimum distortion embeddings into a path for bipartite permutation graphs and threshold graphs

Computing minimum distortion embeddings into a path for bipartite permutation graphs and threshold graphs Computing minimum distortion embeddings into a path for bipartite permutation graphs and threshold graphs Pinar Heggernes Daniel Meister Andrzej Proskurowski Abstract The problem of computing minimum distortion

More information

The Fibonacci hypercube

The Fibonacci hypercube AUSTRALASIAN JOURNAL OF COMBINATORICS Volume 40 (2008), Pages 187 196 The Fibonacci hypercube Fred J. Rispoli Department of Mathematics and Computer Science Dowling College, Oakdale, NY 11769 U.S.A. Steven

More information

Rubik's Shells.

Rubik's Shells. Ruik's Shells Ruik's Shells is a puzzle that consists of 4 intersecting rings, coloured heels ith 8 alls each, hich can rotat The heels are in to pairs; to axes ith a pair of heels on each, and the to

More information

Real-Time Multiprocessor Locks with Nesting: Optimizing the Common Case

Real-Time Multiprocessor Locks with Nesting: Optimizing the Common Case Real-Time Multiprocessor Locks ith Nesting: Optimizing the Common Case Catherine E. Nemitz, Tanya Amert, and James H. Anderson Department of Computer Science, The University of North Carolina at Chapel

More information

Multicasting in the Hypercube, Chord and Binomial Graphs

Multicasting in the Hypercube, Chord and Binomial Graphs Multicasting in the Hypercube, Chord and Binomial Graphs Christopher C. Cipriano and Teofilo F. Gonzalez Department of Computer Science University of California, Santa Barbara, CA, 93106 E-mail: {ccc,teo}@cs.ucsb.edu

More information