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Optimally scaling permutation routing on reconfigurable linear arrays with optical buses

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2000年第9期60卷 1125-1136页

作者： Trahan, JL Bourgeois, AG Pan, Y Vaidyanathan, R Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

We present an optimal and scalable permutation routing algorithm for three reconfigurable models based on linear arrays that arrow pipelining of information through an optical bus. Specifically: for any P less than or equal to N, our algorithm routes any permutation of N elements on a P-processor model optimally in O(N/P) steps. This algorithm extends naturally to one for routing h-relations optimally in O(h) steps. We also establish the equivalence of the three models: linear array with a reconfigurable pipelined bus system, linear pipelined bus, and pipelined optical bus. This implies an automatic translation of algorithms (without loss of speed or efficiency) among these models. (C) 2000 Academic Press.

关键词： reconfigurable models optical buses permutation routing model simulations scalable algorithms h-relations

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Apache Spark-based scalable feature extraction approaches for protein sequence and their clustering performance analysis

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2023年第4期15卷 359-378页

作者： Jha, Preeti Tiwari, Aruna Bharill, Neha Ratnaparkhe, Milind Patel, Om Prakash Harshith, Nilagiri Mounika, Mukkamalla Nagendra, Neha Indian Inst Technol Indore India Mahindra Univ Hyderabad India ICAR Indian Inst Soybean Res Indore India

Genome sequencing projects are rapidly contributing to the rise of high-dimensional protein sequence datasets. Extracting features from a high-dimensional protein sequence dataset poses many challenges. However, many features extraction methods exist, but extracting features from millions of protein sequences becomes impractical because these approaches are not scalable. Therefore, to design an efficient scalable feature extraction approach that extracts significant features, we have proposed two Apache Spark-based scalable feature extraction approaches that extracts significantly important features based on statistical properties from huge protein sequences, which are termed 60d-SPF (60-dimensional scalable Protein Feature) and 6d-SCPSF (6-dimensional scalable Co-occurrence-based Probability-Specific Feature). The proposed 60d-SPF and 6d-SCPSF approaches capture the statistical properties of amino acids to create a fixed-length numeric feature vector that represents each protein sequence in terms of 60-dimensional and 6-dimensional features, respectively. The preprocessed huge protein sequences are used as an input in four clustering algorithms, i.e., scalable random sampling with iterative optimization fuzzy c-means (SRSIO-FCM), scalable literal fuzzy c-means (SLFCM), kernelized SRSIO-FCM (KSRSIO-FCM), and kernelized SLFCM (KSLFCM) for clustering. We have conducted extensive experiments on various soybean protein datasets to demonstrate the effectiveness of the proposed feature extraction methods, 60d-SPF, 6d-SCPSF, and existing feature extraction methods on SRSIO-FCM, SLFCM, KSRSIO-FCM, and KSLFCM clustering algorithms. The reported results in terms of the Silhouette index and the Davies-Bouldin index show that the proposed 60d-SPF extraction method on SRSIO-FCM, SLFCM, KSRSIO-FCM, and KSLFCM clustering algorithms achieve significantly better results than the proposed 6d-SCPSF and existing feature extraction approaches.

关键词： Apache Spark cluster Big Data Feature extraction Fuzzy clustering Huge protein sequences scalable algorithms

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Computing Personalized PageRank Quickly by Exploiting Graph Structures

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第12期7卷 1023-1034页

作者： Maehara, Takanori Akiba, Takuya Iwata, Yoichi Kawarabayashi, Ken-ichi Natl Inst Informat Tokyo Japan Univ Tokyo Tokyo Japan JST ERATO Kawarabayashi Large Graph Project Tokyo Japan

We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR;however, it requires many iterations. Thus reducing the number of iterations is the main challenge. We achieve this by exploiting graph structures of web graphs and social networks. The convergence of our algorithm is very fast. In fact, it requires up to 7.5 times fewer iterations than the Power method and is up to five times faster in actual computation time. To the best of our knowledge, this is the first time to use graph structures explicitly to solve PPR quickly. Our contributions can be summarized as follows. 1. We provide an algorithm for computing a tree decomposition, which is more efficient and scalable than any previous algorithm. 2. Using the above algorithm, we can obtain a cor e-tree decomposition of any web graph and social network. This allows us to decompose a web graph and a social network into (1) the core, which behaves like an expander graph, and (2) a small tree-width graph, which behaves like a tree in an algorithmic sense. 3. We apply a direct method to the small tree-width graph to construct an LU decomposition. 4. Building on the LU decomposition and using it as preconditoner,we apply GMRES method (a state-of-theart advanced iterative method) to compute PPR for whole web graphs and social networks.

关键词： Trees (mathematics) Forestry Graphic methods Iterative methods Social networking (online) Computation time Graph structures Lu decomposition Number of iterations Personalized PageRank scalable algorithms State of the art algorithms Tree decomposition

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Distributed Consensus with Multiple Equilibria in Continuous-Time Multi-Agent Networks under Undirected Topologies

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IFAC-PapersOnLine 2015年第24期48卷 225-230页

作者： Erkan, O. Feyza Akar, Mehmet Department of Electrical and Electronics Engineering Bogazici University Istanbul34342 Turkey

As the networks get more complex nowadays with many interconnected components, it is necessary to develop distributed scalable algorithms so as to minimize the computation required in decision making in such large scale systems. In this context, this paper addresses the problem of multiple equilibrium consensus of multi-agent systems in undirected networks having fixed or switching topologies. The convergence analysis is based upon algebraic graph theory and switched system theory. Finally, some numerical examples are given to illustrate the theoretical results. © 2015, IFFCA (Internaitional Fedreation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multi agent systems Computation theory Continuous time systems Decision making Distributed computer systems Graph theory Large scale systems Optimization Switching networks Algebraic graph theory Convergence analysis Distributed consensus Matrix theory Multiagent networks Multiple equilibrium scalable algorithms Undirected network

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A scalable divide-and-conquer algorithm combining coarse and fine-grain parallelization

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THEORETICAL CHEMISTRY ACCOUNTS 1998年第3期99卷 197-206页

作者： Goh, SK Sosa, CP St-Amant, A Univ Ottawa Dept Chem Ottawa ON K1N 6N5 Canada Cray Res Inc Silicon Graph Inc Eagan MN 55121 USA

We describe an efficient algorithm for carrying out a "divide-and-conquer" fit of a molecule's electronic density on massively parallel computers. Near linear speedups are achieved with up to 48 processors on a Gray T3E, and our results indicate that similar efficiencies could be attained on an even greater number of processors. To achieve optimum efficiency, the algorithm combines coarse and fine-grain parallelization and adapts itself to the existing ratio of processors to subsystems. The subsystems employed in our divide-and-conquer approach can also be made smaller or bigger, depending on the number of processors available. This allows us to further reduce the wallclock time and improve the method's overall efficiency. The strategies implemented in this paper can be extended to any other divide-and-conquer method used within an ab initio, density functional, or semi-empirical quantum mechanical program.

关键词： scalable algorithms supercomputing quantum mechanics divide-and-conquer large molecules

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Designing fault tolerant algorithms for reconfigurable

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INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE 2005年第1期16卷 71-88页

作者： Fernández-Zepeda, JA Estrella-Balderrama, A Bourgeois, AG CICESE Dept Comp Sci Ensenada 22860 Baja California Mexico Univ Noroeste Phuket 83000 Thailand Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

This paper proposes a procedure to design fault tolerant algorithms for the R-Mesh and some of its restrictive variations. This procedure first identifies a healthy sub-mesh from a faulty model using the bypass and removal fault model. Then it uses scalable algorithms to simulate the larger faulty model on the resulting healthy sub-mesh. The algorithms for the bypass model tolerates n faults in an n x n R-Mesh (LR-Mesh) and runs in O(T log n) (O(T)) time, where T is the execution time on the original mesh without faults. For the removal model, we design fault tolerant algorithms for some interesting variations of the R-Mesh, specifically, the NXR-Mesh and the NXLR-Mesh. We propose the first scaling simulations for these models and present a simulation of the R-Mesh on the NXR-Mesh. The results of this paper enable us to consider certain reconfigurable models in a more practical environment than previously allowed.

关键词： reconfigurable architectures R-Mesh fault tolerance scalable algorithms simulations

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Conceptual interfaces in hypre

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2006年第1-2期22卷 239-251页

作者： Falgout, RD Jones, JE Yang, UM Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94551 USA

The hypre software library is being developed with the aim of providing scalable solvers for the solution of large, sparse linear systems oil massively parallel computers. To this end, the notion of conceptual interfaces was introduced. These interfaces give applications users a more natural means for describing their linear systems, and provide access to methods Such as geometric multigrid which require additional information beyond just the matrix. This paper discusses the design of the Conceptual interfaces in hypre and illustrates their use with various examples. A brief overview of the solvers and preconditioners available through these interfaces is also given. Published by Elsevier B.V.

关键词： conceptual interfaces high performance preconditioners scalable algorithms

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A hardware-assisted scalable solution for interactive volume rendering of time-varying data

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2002年第3期8卷 286-301页

作者： Lum, EB Ma, KL Clyne, J Univ Calif Davis CIPIC Davis CA 95616 USA Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Natl Ctr Atmospher Res Boulder CO 80303 USA

We present a scalable volume rendering technique that exploits lossy compression and low-cost commodity hardware to permit highly interactive exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 42 million voxels each time step) at interactive rates. By clustering multiple PCs together, we demonstrate the data-size scalability of our method. The frame rates achieved make possible the interactive exploration of data in the temporal, spatial, and transfer function domains. A comprehensive evaluation of our method based on experimental studies using data sets (up to 134 million voxels per time step) from turbulence flow simulations is also presented.

关键词： compression disk I/O high performance computing out-of-core processing parallel rendering PC scalable algorithms scientific visualization texture hardware time-varying data transform encoding volume rendering

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Network Resilience and the Length-Bounded Multicut Problem: Reaching the Dynamic Billion-Scale with Guarantees

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PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS 2018年第1期2卷 1–26页

作者： Kuhnle, Alan Crawford, Victoria G. Thai, My T. Univ Florida Comp & Informat Sci & Engn Gainesville FL 32306 USA

Motivated by networked systems in which the functionality of the network depends on vertices in the network being within a bounded distance T of each other, we study the length-bounded multicut problem: given a set of pairs, find a minimum-size set of edges whose removal ensures the distance between each pair exceeds T. We introduce the first algorithms for this problem capable of scaling to massive networks with billions of edges and nodes: three highly scalable algorithms with worst-case performance ratios. Furthermore, one of our algorithms is fully dynamic, capable of updating its solution upon incremental vertex / edge additions or removals from the network while maintaining its performance ratio. Finally, we show that unless NP subset of BPP, there is no polynomial-time, approximation algorithm with performance ratio better than Omega(T), which matches the ratio of our dynamic algorithm up to a constant factor.

关键词： scalable algorithms length-bounded multicut

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CoSimHeat: An Effective Heat Kernel Similarity Measure Based on Billion-Scale Network Topology 22

CoSimHeat: An Effective Heat Kernel Similarity Measure Based...

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31st ACM Web Conference (WWW)

作者： Yu, Weiren Jian Yang Zhang, Maoyin Di Wu Univ Warwick Coventry England Nanjing Univ Sci & Technol Jiangsu Peoples R China Nanjing Univ Sci & Technol Jiangsu Jiangsu Peoples R China Hunan Univ Changsha Peoples R China

ISBN: (纸本)9781450390965

Myriads of web applications in the Big Data era demand an effective measure of similarity based on billion-scale network structures, e.g., collaborative filtering. Recently, CoSimRank has been devised as a promising graph-theoretic similarity model, which iteratively captures the notion that "two distinct nodes are evaluated as similar if they are connected with similar nodes". However, the existing CoSimRank model for assessing similarities may either yield unsatisfactory results or rather cost-inhibitive, rendering it impractical in massive graphs. In this paper, we propose CoSimHeat, a novel scalable graph-theoretic similarity model based on heat diffusion. Specifically, we first formulate CoSimHeat model by taking advantage of heat diffusion to emulate the activities of similarity propagations on the Web. Then, we show that the similarities produced by CoSimHeat are more satisfactory than those from CoSimRank families since CoSimHeat fulfils four axioms that an ideal similarity model should satisfy while circumventing the "dead-loop" problem of CoSimRank. Next, we propose a fast algorithm to substantially accelerate CoSimHeat computations on billion-sized graphs, with guarantees of accuracy. Our experiments on various datasets validate that CoSimHeat achieves higher accuracy and is order-of-magnitude faster than state-of-the-art competitors.

关键词： Similarity Search Link Analysis scalable algorithms

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