Efficient parallel and sequential algorithms for 4-coloring perfect planar graphs

We present an efficient algorithm for 4-coloring perfect planar graphs. The best previously known algorithm for this problem takesO(n ^3/2) sequential time, orO(log⁴ n) parallel time withO(n³) processors. The sequential implementation of our algorithm takesO(n logn) time. The parallel implementation of our algorithm takesO(log³ n) time withO(n) processors on a PRAM.     

Efficient parallel algorithms for path problems in directed graphs

In this paper we describe a technique for finding efficient parallel algorithms for problems on directed graphs that involve checking the existence of certain kinds of paths in the graph. This technique provides efficient algorithms for finding dominators in flow graphs, performing interval and loop analysis on reducible flow graphs, and finding the feedback vertices of a digraph. Each of these algorithms takesO(log² n) time using the same number of processors needed for fast matrix multiplication. All of these bounds are for an EREW PRAM.     

Efficient parallel algorithms for path problems in directed graphs

In this paper we describe a technique for finding efficient parallel algorithms for problems on directed graphs that involve checking the existence of certain kinds of paths in the graph. This technique provides efficient algorithms for finding dominators in flow graphs, performing interval and loop analysis on reducible flow graphs, and finding the feedback vertices of a digraph. Each of these algorithms takesO(log² n) time using the same number of processors needed for fast matrix multiplication. All of these bounds are for an EREW PRAM.     

Efficient algorithms for single- and two-layer linear placement of parallel graphs

This paper outlines an algorithm for optimum linear ordering (OLO) of a weighted parallel graph with O(n log k) worst-case time complexity, and O(n + k log(n/k) log k) expected-case time complexity, where n is the total number of nodes and k is the number of chains in the parallel graph. Next, the two-layer OLO problem is considered, where the goal is to place the nodes linearly in two routing layers minimizing the total wire length. The two-layer problem is shown to subsume the maxcut problem and a befitting heuristic algorithm is proposed. Experimental results on randomly generated samples show that the heuristic algorithm runs very fast and outputs optimum solutions in more than 90% instances.     

Local 7-coloring for planar subgraphs of unit disk graphs

We study the problem of computing locally a coloring of an arbitrary planar subgraph of a unit disk graph. Each vertex knows its coordinates in the plane and can communicate directly with all its neighbors within unit distance. Using this setting, first a simple algorithm is given whereby each vertex can compute its color in a 9-coloring of the planar graph using only information on the subgraph located within at most 9 hops away from it in the original unit disk graph. A more complicated algorithm is then presented whereby each vertex can compute its color in a 7-coloring of the planar graph using only information on the subgraph located within a constant number (201, to be exact) of hops away from it.     

Efficient parallel algorithms for breadth first spanning forests of general graphs

Ghosh and Bhattacharjee propose [2] (Intern. J. Computer Math., 1984, Vol. 15, pp. 255-268) an algorithm of determining breadth first spanning trees for graphs, which requires that the input graphs contain some vertices, from which every other vertex in the input graph can be reached. These vertices are called starting vertices. The complexity of the GB algorithm is O(log² n) using O{n ³) processors. In this paper an algorithm, named BREADTH, also computing breadth first spanning trees, is proposed. The complexity is O(log² n) using O{n ³/logn) processors. Then an efficient parallel algorithm, named- BREADTHFOREST, is proposed, which generalizes algorithm BREADTH. The output of applying BREADTHFOREST to a general graph, which may not contain any starting vertices, is a breadth first spanning forest of the input graph. The complexity of BREADTHFOREST is the same as BREADTH.     

Efficient parallel algorithms for computing all pair shortest paths in directed graphs

We present parallel algorithms for computing all pair shortest paths in directed graphs. Our algorithm has time complexityO(f(n)/p+I(n)logn) on the PRAM usingp processors, whereI(n) is logn on the EREW PRAM, log logn on the CCRW PRAM,f(n) iso(n ³). On the randomized CRCW PRAM we are able to achieve time complexityO(n ³/p+logn) usingp processors. A preliminary version of this paper was presented at the 4th Annual ACM Symposium on Parallel Algorithms and Architectures, June 1992. Support by NSF Grant CCR 90-20690 and PSC CUNY Awards #661340 and #662478.     

On linear-time algorithms for five-coloring planar graphs

Certain properties of planar graphs are established in a particularly straightforward fashion. These properties assure good performance in two linear-time algorithms for five-coloring planar graphs. A new linear-time algorithm, based on a third property, is also presented.     

Efficient parallel algorithms for numerical simulation

COUPL+ is a programming environment for applications using unstructured and hybrid grids for numerical simulations. It automates parallelization by handling the partitioning of data and dependent data and maintaining halo interfaces and copy coherency. We explore some algorithms behind this package. A multi-level partitioning method is described which is effective in the presence of skewed data, solving the multi-set median-finding problem. Partitioning elements over a set of pre-partitioned nodes is explored and a novel method is suggested for reducing communication in the resulting distribution.     

Efficient parallel algorithms for graph problems

We present an efficient technique for parallel manipulation of data structures that avoids memory access conflicts. That is, this technique works on the Exclusive Read/Exclusive Write (EREW) model of computation, which is the weakest shared memory, MIMD machine model. It is used in a new parallel radix sort algorithm that is optimal for keys whose values are over a small range. Using the radix sort and known results for parallel prefix on linked lists, we develop parallel algorithms that efficiently solve various computations on trees and “unicycular graphs.” Finally, we develop parallel algorithms for connected components, spanning trees, minimum spanning trees, and other graph problems. All of the graph algorithms achieve linear speedup for all but the sparsest graphs.     

Efficient parallel algorithms for graph problems

We present an efficient technique for parallel manipulation of data structures that avoids memory access conflicts. That is, this technique works on the Exclusive Read/Exclusive Write (EREW) model of computation, which is the weakest shared memory, MIMD machine model. It is used in a new parallel radix sort algorithm that is optimal for keys whose values are over a small range. Using the radix sort and known results for parallel prefix on linked lists, we develop parallel algorithms that efficiently solve various computations on trees and unicycular graphs. Finally, we develop parallel algorithms for connected components, spanning trees, minimum spanning trees, and other graph problems. All of the graph algorithms achieve linear speedup for all but the sparsest graphs.Part of this work was done while the first author was at the University of Illinois, Urbana-Champaign, the second author was at Carnegie-Mellon University, and the third author was at the Hebrew University and the Courant Institute of Mathematical Sciences, New York University. A preliminary version of this work was presented at the 1986 International Conference on Parallel Processing.     

Efficient parallel hierarchical clustering algorithms

Clustering of data has numerous applications and has been studied extensively. Though most of the algorithms in the literature are sequential, many parallel algorithms have also been designed. In this paper, we present parallel algorithms with better performance than known algorithms. We consider algorithms that work well in the worst case as well as algorithms with good expected performance.     

Efficient algorithms for parallel sorting on mesh multicomputers

We present two new parallel algorithms QSP1 and QSP2 based on sequential quicksort for sorting data on a mesh multicomputer, and analyze their scalability using the isoefficiency metric. We show that QSP2 matches the lower bound on the isoefficiency function for mesh multicomputers, while QSP1 is fairly close to optimal. Langet al. ⁽¹⁾ and Schnorret al. ⁽²⁾ have developed parallel sorting algorithms for the mesh architecture that have either optimal (Schnorr) or close to optimal (Lang) run-time complexity for the one-element-perprocessor case. Both QSP1 and QSP2 have better scalability than the scaled-down variants of these algorithms (for the case in which there are more elements than processors). We also analyze a different variant of Lang's sort which is as scalable as QSP2. We briefly discuss another metric called resource consumption. According to this metric, both QSP1 and QSP2 are superior to variants of Lang's sort.     

Efficient MapReduce algorithms for triangle listing in billion-scale graphs

This paper addresses the classical triangle listing problem, which aims at enumerating all the tuples of three vertices connected with each other by edges. This problem has been intensively studied in internal and external memory, but it is still an urgent challenge in distributed environment where multiple machines across the network can be utilized to achieve good performance and scalability. As one of the de facto computing methodologies in distributed environment, MapReduce has been used in some of existing triangle listing algorithms. However, these algorithms usually need to shuffle a huge amount of intermediate data, which seriously hinders their scalability on large scale graphs. In this paper, we propose a new triangle listing algorithm in MapReduce, FTL, which utilizes a light weight data structure to substantially reduce the intermediate data transferred during the shuffle stage, and also is equipped with multiple-round techniques to ease the burden on memory and network bandwidth when dealing with graphs at billion scale. We prove that the size of the intermediate data can be well bounded near to the number of triangles in the graph. To further reduce the shuffle size and memory cost, we also propose improved algorithms based on a compact data structure, and present several optimization techniques to accelerate the computation and reduce the memory consumption. The extensive experimental results show that our algorithms outperform existing competitors by several times on both synthetic graphs and real world graphs.     

Efficient algorithms for (3, 1) graphs

In this paper, we define a class of graphs which are referred to as (3, 1) graphs. A graph is a member of this class if it has the property that within each set of three vertices, there is at least one edge. We derive a lower bound for the size of a maximum clique in a (3, 1) graph as well as an upper bound for the size of a minimum clique covering. In addition, we show that there exists a linear algorithm for constructing a Hamiltonian circuit in a connected (3, 1) graph and an n⁴-algorithm for finding a minimum coloring in a (3, 1) graph.     

Efficient approximation algorithms for shortest cycles in undirected graphs

We describe a simple combinatorial approximation algorithm for finding a shortest (simple) cycle in an undirected graph. Given an adjacency-list representation of an undirected graph G with n vertices and unknown girth k, our algorithm returns with high probability a cycle of length at most 2k for even k and 2k+2 for odd k, in time . Thus, in general, it yields a approximation. For a weighted, undirected graph, with non-negative edge weights in the range {1,2,…,M}, we present a simple combinatorial 2-approximation algorithm for a minimum weight (simple) cycle that runs in time O(n²logn(logn+logM)).     

Fast massively parallel algorithms for shortest path within planar figures

s and t within a given planar figure F is considered. The approach contains basic methodology developed for any parallel or distributed system. The 2D scene or the edge of F are represented in the n Cartesian coordinate system (n-CCS). Several algorithms for the shortest path are given, each one to be applied in specified circumstances depending on the exact machine model or on additional information concerning geometrical properties of the figure. If these algorithms are implemented in a parallel depth search machine (PDSM), then the shortest path can be computed in time O(1). The maximum number of processors used is 0(n). The given methodology can also be adapted for producing an approximate solution when the shortest path is approximated by polygonal lines.