An interval temporal network is, informally speaking, a network whose links change with time. The term interval means that a link may exist for one or more time intervals, called availability intervals of the link, after which it does not exist (until, maybe, a further moment in time when it starts being available again). In this model, we consider continuous time and high-speed (instantaneous) information dissemination. An interval temporal network is connected during a period of time [Formula: see text], if it is connected for all time instances [Formula: see text] (instantaneous connectivity). In this work, we study instantaneous connectivity issues of interval temporal networks. We provide a polynomial-time algorithm that answers if a given interval temporal network is connected during a time period. If the network is not connected throughout the given time period, then we also give a polynomial-time algorithm that returns large components of the network that remain connected and remain large during [Formula: see text]; the algorithm also considers the components of the network that start as large at time [Formula: see text] but dis-connect into small components within the time interval [Formula: see text], and answers how long after time [Formula: see text] these components stay connected and large. Finally, we examine a case of interval temporal networks on tree graphs where the lifetimes of links and, thus, the failures in the connectivity of the network are not controlled by us; however, we can “feed” the network with extra edges that may re-connect it into a tree when a failure happens, so that its connectivity is maintained during a time period. We show that we can with high probability maintain the connectivity of the network for a long time period by making these extra edges available for re-connection using a randomized approach. Our approach also saves some cost in the design of availabilities of the edges; here, the cost is the sum, over all extra edges, of the length of their availability-to-reconnect interval.

Parallel Processing LettersVol. 28, No. 04, 1899001 (2018) Free AccessAuthor Index Volume 28https://doi.org/10.1142/S0129626418990013Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Vol. 28, No. 04 Metrics History PDF download

Porting scientific key algorithms to HPC architectures requires a thorough understanding of the subtle balance between gain in performance and introduced overhead. Here we continue the development of our recently proposed technique that uses plain execution times to predict the extent of parallel overhead. The focus here is on an analytic solution that takes into account as many data points as there are unknowns, i.e. model parameters. A test set of 9 applications frequently used in scientific computing can be well described by the suggested model even including atypical cases that were originally not considered part of the development. However, the choice about which particular set of explicit data points will lead to an optimal prediction cannot be made a-priori.

Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks. We profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically, asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes. We arrive at numerical results suggesting a correspondence between some physical, structural and functional properties. Our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties. We conclude that these methods, with their links to chemoinformatics via algorithmic, probability hold promise for future research.

Clustering is an effective technique that can be used to analyze and extract useful information from large biological networks. Popular clustering solutions often require user input for several algorithm options that can seem very arbitrary without experimentation. These algorithms can provide good results in a reasonable time period but they are not above improvements. We present a local search based clustering algorithm free of such required input that can be used to improve the cluster quality of a set of given clusters taken from any existing algorithm or clusters produced via any arbitrary assignment. We implement this local search using a modern GPU based approach to allow for efficient runtime. The proposed algorithm shows promising results for improving the quality of clusters. With already high quality input clusters we can achieve cluster rating improvements upto to 33%.

A parallel algorithm for prefix computation on [Formula: see text] data elements mapped on a Multi Mesh (MM) network of [Formula: see text] processing elements is presented here. The time required by the proposed algorithm is significantly less than that by any of the existing algorithms for prefix computation on mesh-like architectures due to the specific interconnection pattern used in the MM network. The proposed technique requires [Formula: see text] time for data communication and [Formula: see text] time for computation, when mapped on a MM network constituted by [Formula: see text] meshes, each of size [Formula: see text]. The data communication time in the proposed algorithm is less than the prefix sum algorithm proposed in extended Multi Mesh. To be precise, instead of [Formula: see text] communication time the proposed algorithm requires a data communication time of [Formula: see text] only. Moreover, the proposed parallel algorithm does not need any extra inter block links as used in the extended Multi Mesh.

Bulk-Synchronous Parallel (BSP) is a bridging model between abstract execution and concrete parallel architectures that retains enough information to model performance scalability. In order to model BSP program executions, Hains adapted the theory of finite automata to the BSP paradigm by introducing BSP automata [12]. In the initial description of the theory, BSP automata had to be explicitly defined as structured sets of states and transitions. The lack of a clean and efficient algorithm for generating them from regular expressions would have prevented this theory from being used in practice. To alleviate this problem we introduce in this paper an algorithm that generates a BSP automaton recognizing a BSP language defined by a BSP regular expression. The main practical use of BSP automata developed in this paper is the parallelization of finite state automata with an explicit distribution and a performance model, that enable parallel matching of regular expressions. Secondarily, BSP regular expressions provide a convenient structure for automatic code generation of imperative BSP programs that is also developed in this paper.

Testing parallel applications on a large number of processors is often impractical. Not only does it require access to scarce compute resources, but tracking down defects with the available debugging tools can often be very time consuming. Highly parallel codes should be testable on one processor at a time, so that a developer’s workstation is sufficient for executing and debugging test cases on millions of processes. Thanks to its supersteps, Bulk Synchronous Parallel programs are well suited for this kind of testing. This paper presents a mocking library for BSPlib which enables testing of fast and complex parallel algorithms at scale.

We propose a self-stabilizing algorithm for computing a maximal matching in an anonymous network. The complexity is O(2) moves with high probability, under the adversarial distributed daemon. Among all adversarial distributed daemons and with the anonymous assumption, our algorithm provides the best known complexity. Moreover, the previous best known algorithm working under the same daemon and using identity has a O(m) complexity leading to the same order of growth than our anonymous algorithm. Finally, we do not make the common assumption that a node can determine whether one of its neighbors points to it or to another node, and still we present a solution with the same asymptotic behavior.

This is an evaluation study of the expressiveness provided and the performance delivered by the coordination language S-NET in comparison to Intel’s Concurrent Collections (CnC). An S-NET application is a network of black-box compute components connected through anonymous data streams, with the standard input and output streams linking the application to the environment. Our case study is based on two applications: a face detection algorithm implemented as a pipeline of feature classifiers and a numerical algorithm from the linear algebra domain, namely Cholesky decomposition. The selected applications are representative and have been selected by Intel researchers as evaluation testbeds for CnC in the past. We implement various versions of both algorithms in S-NET and compare them with equivalent CnC implementations, both with and without tuning, previously published by the CnC community. Our experiments on a large-scale server system demonstrate that S-Net delivers very similar scalability and absolute performance on the studied examples as tuned CnC codes do, even without specific tuning. At the same time, S-Net does achieve a much more complete separation of concerns between compute and coordination layers than CnC even intends to.