30910035
A Parallel Implementation of the Everglades Landscape Fire Model in Networks of Workstations
This paper presents a low-communication overhead and high-performance data parallelism implementation of the Everglades Landscape Fire Model (ELFM) in a network of workstations (NOWs). Checkpointing and rollback techniques were used to handle the spread of fire which is a dynamic and irregular component of the model. A synchronous checkpointing mechanism was used in the parallel ELFM code using Message Passing Interface (MPI). The speedup and performance of the parallel program were also studied. Results show that the performance of ELFM using MPI is significantly enhanced by using the checkpointing and rollback mechanisms.
17371165
QoS Routing in Hypercube Multicomputers
In this paper, we present a coding method to capture QoS information in hypercube multicomputers. The coding method is based on a localized algorithm where each node interacts with its neighbors to gather QoS information. Specifically, each node maintains a QoS vector where the k-th element represents the guaranteed QoS performance to a destination that is k hops away. The localized algorithm exhibits desirable properties of self-stabilizing, self-optimizing, and self-healing. Simulation results show that this coding method provides a good approximation of the minimum QoS value to a k-hop destination and, at the same time, uses a relatively small number of packets to propagate a change in link state (QoS value) compared with the classical distributed Bellman-Ford method.
8943672
Enhanced Fibonacci Cubes
We propose the enhanced Fibonacci cube (EFC) structure for parallel systems. It is defined based on the sequence F n = 2F n-2 + 2F n-4 . We show that the enhanced Fibonacci cube contains the Fibonacci cube (FC) as a subgraph and maintains virtually all the desirable properties of the Fibonacci cube. In addition, it is a Hamiltonian graph. We can embed complete binary trees into enhanced Fibonacci cubes with dilation one and with a relatively small expansion. We also propose a series of enhanced Fibonacci cubes EFC (k) , where k is a series number. Each EFC (k) contains an FC of the same order as a subcube. Moreover, each EFC (k) in the series contains any other cube that precedes it as subcubes and the last one in the series is a hypercube of the corresponding order. This series of EFC (k) s provides us with more options for selecting cubes with various sizes. Because EFC is a subgraph of the hypercube, it may find applications in fault-tolerant computing for degraded hypercube computer systems. As an application of EFC, we show that the parallel prefix sum computation can be efficiently implemented on enhanced Fibonacci cubes.
41551452
Fault-Tolerant Adaptive and Minimal Routing in Mesh-Connected Multicomputers Using Extended Safety Levels
The minimal routing problem in mesh-connected multicomputers with faulty blocks is studied. Two-dimensional meshes are used to illustrate the approach. A sufficient condition for minimal routing in 2D meshes with faulty blocks is proposed. Unlike many traditional models that assume all the nodes know global fault distribution, our approach is based on the concept of an extended safety level, which is a special form of limited fault information. The extended safety level information is captured by a vector associated with each node. When the safety level of a node reaches a certain level (or meets certain conditions), a minimal path exists from this node to any nonfaulty nodes in 2D meshes. Specifically, we study the existence of minimal paths at a given source node, limited distribution of fault information, and minimal routing itself. We propose three fault-tolerant minimal routing algorithms which are adaptive to allow all messages to use any minimal path. We also provide some general ideas to extend our approaches to other low-dimensional mesh-connected multicomputers such as 2D tori and 3D meshes. Our approach is the first attempt to address adaptive and minimal routing in 2D meshes with faulty blocks using limited fault information.
33573218
Guest Editors' Introduction
16257632
XYZ: a scalable, partially centralized lookup service for large-scale peer-to-peer systems
Peer-to-peer (P2P) systems are characterized by direct access between peer computers, rather than through a centralized server. File sharing is the dominant P2P application on the Internet, allowing users to easily contribute, search and obtain content. P2P systems can be categorized by the degrees of centralization. For fully centralized systems, the lookup service will not be available when the central directory server is down. For purely decentralized and unstructured systems, since there is no information about which nodes are likely to have the relevant files, searching essentially amounts to random search. This makes the lookup service unscalable and unpredictable. The objective of this paper is to design a partially centralized, scalable and self-organizing lookup service (XYZ) for wide area P2P systems. A clustering method is used to create the system backbone by connecting the cluster heads together and a color clustering method is adopted to create color overlays and minimize the searching space. Simulations and analysis are also provided. Extensions are proposed to achieve better performance
59008197
A cycle-based rerouting scheme for wavelength-routed WDM networks
In this paper, we propose a cycle-based rerouting scheme (CRS) for link restoration in wavelength-routed WDM networks. The CRS is based on a scalable set of Pcycles covering a mesh network. Instead of using these Pcycles as enhanced self-healing rings as in traditional approaches, the CRS decomposes these P-cycles into rerouting paths for the links that the P-cycles cover. In this way, flexible and cost-effective link restoration is achieved. Simulation results show that the rerouting performance of the CRS can be very close to the theoretical upper bound.
1591692
Classifier Combination for Improved Lexical Disambiguation
One of the most exciting recent directions in machine learning is the discovery that the combination of multiple classifiers often results in significantly better performance than what can be achieved with a single classifier. In this paper, we first show that the errors made from three different state of the art part of speech taggers are strongly complementary. Next, we show how this complementatry behavior can be used to our advantage. By using contextual cues to guide tagger combination, we are able to derive a new tagger that achieves performance significantly greater than any of the individual taggers.
10351800
Identification of functional links between genes using phylogenetic profiles
MOTIVATION
Genes with identical patterns of occurrence across the phyla tend to function together in the same protein complexes or participate in the same biochemical pathway. However, the requirement that the profiles be identical (i) severely restricts the number of functional links that can be established by such phylogenetic profiling; (ii) limits detection to very strong functional links, failing to capture relations between genes that are not in the same pathway, but nevertheless subserve a common function and (iii) misses relations between analogous genes. Here we present and apply a method for relaxing the restriction, based on the probability that a given arbitrary degree of similarity between two profiles would occur by chance, with no biological pressure. Function is then inferred at any desired level of confidence.


RESULTS
We derive an expression for the probability distribution of a given number of chance co-occurrences of a pair of non-homologous orthologs across a set of genomes. The method is applied to 2905 clusters of orthologous genes (COGs) from 44 fully sequenced microbial genomes representing all three domains of life. Among the results are the following. (1) Of the 51 000 annotated intrapathway gene pairs, 8935 are linked at a level of significance of 0.01. This is over 30-fold greater than the 271 intrapathway pairs obtained at the same confidence level when identical profiles are used. (2) Of the 540 000 interpathway genes pairs, some 65 000 are linked at the 0.01 level of significance, some 12 standard deviations beyond the number expected by chance at this confidence level. We speculate that many of these links involve nearest-neighbor path, and discuss some examples. (3) The difference in the percentage of linked interpathway and intrapathway genes is highly significant, consistent with the intuitive expectation that genes in the same pathway are generally under greater selective pressure than those that are not. (4) The method appears to recover well metabolic networks. This is illustrated by the TCA cycle which is recovered as a highly connected, weighted edge network of 30 of its 31 COGs. (5) The fraction of pairs having a common pathway is a symmetric function of the Hamming distance between their profiles. This finding, that the functional correlation between profiles with near maximum Hamming distance is as large as between profiles with near zero Hamming distance, and as statistically significant, is plausibly explained if the former group represents analogous genes.
412675
Linear Asymmetric Classifier for cascade detectors
The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.
17480069
Similarity-based Web service matchmaking
With the increasing growth in popularity of Web services, matchmaking of relevant Web services becomes a significant challenge. Commonly, Web service is described by WSDL and published on UDDI registers. UDDI provides limited search facilities allowing only a keyword-based search of businesses, services, and the so called tModels based on names and identifiers. This category-based keyword-browsing method is clearly insufficient. Semantic Web service uses DAML-S instead of WSDL to represent capabilities of Web services. This improvement enables software agents or search engines to automatically find appropriate Web services via ontologies and reasoning algorithm enriched methods. However, the high cost of formally defining to the heavy and complicated services makes this improvement widespread adoption unlikely. To cope with these limitations, we have developed a suite of methods which assesses the similarity of Web services to achieve matchmaking. In particular, we present a conceptual model which classifies properties of Web services into four categories. For each category, a similarity assessment method has been given. In Web service matchmaking process, these similarity assessment methods can be used together or individually. Experiments highlight complementary contributions that our work makes to facilitate Web service matchmaking.