40672752
The existence of hyper-L triple-loop networks
16940810
Vertex and Tree Arboricities of Graphs
This paper studies the following variations of arboricity of graphs. The vertex (respectively, tree) arboricity of a graph G is the minimum number va(G) (respectively, ta(G)) of subsets into which the vertices of G can be partitioned so that each subset induces a forest (respectively, tree). This paper studies the vertex and the tree arboricities on various classes of graphs for exact values, algorithms, bounds, hamiltonicity and NP-completeness. The graphs investigated in this paper include block-cactus graphs, series-parallel graphs, cographs and planar graphs.
204988011
A class of additive multiplicative graph functions
For a fixed graph G, the capacity function for G, Po, is defined by Pc(H)= lim,__,®[yo(H')] TM, where y6(H) is the maximum number of disjoint G's in H. In [2], Hsu proved that Pr2 can be viewed as a lower bound for multiplicative increasing graph functions. But it was not known whether Pr2 is multiplicative or not. In this paper, we prove that Pc is multiplicative and additive for some graphs G which include K2. Some properties of Pc are also discussed in this paper.
198001
Proper interval graphs and the guard problem
This paper is a study of the hamiltonicity of proper interval graphs with applications to the guard problem in spiral polygons. We prove that proper interval graphs with ?2 vertices have hamiltonian paths, those with ?3 vertices have hamiltonian cycles, and those with ?4 vertices are hamiltonian-connected if and only if they are, respectively, 1-, 2-, or 3-connected. We also study the guard problem in spiral polygons by connecting the class of nontrivial connected proper interval graphs with the class of stick-intersection graphs of spiral polygons.
27714693
Efficient Parallel Processing for K-Nearest-Neighbor Search in Spatial Databases
Even though the problem of k nearest neighbor (kNN) query is well-studied in serial environment, there is little prior work on parallel kNN search processing in parallel one. In this paper, we present the first Best-First based Parallel kNN (BFPkNN) query algorithm in a multi-disk setting, for efficient handling of kNN retrieval with arbitrary values of k by parallelization. The core of our method is to access more entries from multiple disks simultaneously and enable several effective pruning heuristics to discard non-qualifying entries. Extensive experiments with real and synthetic datasets confirm that BFPkNN significantly outperforms its competitors in both efficiency and scalability.
31774377
Factors in graphs with odd-cycle property
Abstract We present some conditions for the existence of a ( g , f )-factor or a ( g , f )-parity factor in a graph G with the odd-cycle property that any two odd cycles of G either have a vertex in common or are joined by an edge.
27005423
Binding number and toughness for matching extension
Abstract A graph G of even order is said to be k -extendable if every matching of size k in G can be extended to a 1-factor of G . Plummer (1988) showed that a graph G is k -extendable if tough ( G ) > k , and we here prove that G is also k -extendable if bind ( G ) > max {k, (7k + 13)/12}.
16331940
Optimal Parallel Routing in Star Networks
Star networks have recently been proposed as attractive alternatives to the popular hypercube for interconnecting processors on a parallel computer. In this paper, we present an efficient algorithm that constructs an optimal parallel routing in star networks. Our result improves previous results for the problem.
704070
Model-Guided Empirical Optimization for Multimedia Extension Architectures: A Case Study
Compiler technology for multimedia extensions must effectively utilize not only the SIMD compute engines but also the various levels of the memory hierarchy: superword registers, multi-level caches and TLB. In this paper, we describe a compiler that combines optimization across all levels of the memory hierarchy with automatic generation of SIMD code for multimedia extensions. At the high-level, model-guided empirical optimization is used to transform code to optimize for all levels of the memory hierarchy. This compiler interacts with a backend compiler exploiting superword-level parallelism that takes sequential code as input and produces SIMD code. This paper discusses how we have combined these technologies into a single framework. Through a case study with matrix multiply, we observe performance results that outperform the hand-tuned Intel MKL library, and achieve performance that is within 4% of the ATLAS self-tuning library with architectural defaults and more than 4X faster than the native Intel compiler.