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检索条件"主题词=Graph algorithm"

共 515 条记录，以下是71-80 订阅

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NUMA-aware Scalable graph Traversal on SGI UV Systems

NUMA-aware Scalable Graph Traversal on SGI UV Systems

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ACM Workshop on High Performance graph Processing (HPGP)

作者： Yasui, Yuichiro Fujisawa, Katsuki Goh, Eng Lim Baron, John Sugiura, Atsushi Uchiyama, Takashi Kyushu Univ Nishi Ku 744 Motooka Fukuoka Japan Silicon Graph Int Corp 900 North McCarthy Blvd Milpitas CA USA SGI Japan Ltd Shibuya Ku Yebisu Garden Pl Tower 31F4-20-3 Ebisu Tokyo Japan

ISBN: (纸本)9781450343503

Breadth-first search (BFS) is one of the most fundamental processing algorithm singraph theory. We previously presented a scalable BFS algorithm based on Beamer's direction optimizing algorithm forn on-uniform memory access(NUMA)-based systems, in which the NUMA architecture was care-fully considered. This paper presents our new implementation that reduces remote memory access in a top-down direction of direction-optimizing algorithm. We also discuss numerical results obtained on the SGI UV 2000 and UV 300 systems, which are shared-memory super computers based on a cache coherent (cc)-NUMA architecture that can handle thousands of threads on a single operating system. Our implementation has a chieved performance rates of 219 billion edges per second on a Kronecker graph with 2(34) vertices and 2(38) edges on arack of an SGI UV 300 system with 1,152 threads. This result exceeds the fast estentry for a shared memory system on the current graph500 list presented in November 2015, which includes our previous implementation.

关键词： graph algorithm NUMA-aware graph500 benchmark

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Towards Next-Generation graph Processing and Management Platform

Towards Next-Generation Graph Processing and Management Plat...

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ACM Workshop on High Performance graph Processing (HPGP)

作者： Suzumura, Toyotaro IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA Univ Tokyo Barcelona Supercomp Ctr Tokyo 1138654 Japan

ISBN: (纸本)9781450343503

Application which need to process and manage large graph data sets have imposed significant challenges for data science community inrecent times. This talk discusses the key challenges which need to be handled when implementing a next-generation graph processing and management platform. There are severalkey problems which needs to bead dressed in building such large graph processing system. First, optimized techniques needs to be followed for managing extremely large graph data. Second, new programming models and software tools need to be created for efficiently processing large graphs. This talk will discuss the approaches which need to be followed in addressing these two major issues and will highlight our vision in achieving the challenges of next-generation graph processing and management.

关键词： Large graph Processing High Performance Computing graph Data Management graph algorithm

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Tegraph: A Novel General-Purpose Temporal graph Computing Engine 38

TeGraph: A Novel General-Purpose Temporal Graph Computing En...

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Huan, Chengying Liu, Hang Liu, Mengxing Liu, Yongchao He, Changhua Chen, Kang Jiang, Jinlei Wu, Yongwei Song, Shuaiwen Leon Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Comp Sci & Technol Beijing Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA Ant Grp Hangzhou Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Temporal graphs attach time information to edges and are commonly used for implementing time-critical applications that can not be effectively processed by traditional static and dynamic graph processing engines. State-of-the-art solutions that target temporal path problems remain ad-hoc and often suboptimal. A unified and high-performance solution that could efficiently process general temporal path problems via a universal optimization strategy and relieve practitioners from heavy optimization efforts is in urgent demand. In this paper, we make two key observations: (1) temporal path problems can be described as topological-optimum problems and solved by a universal single scan execution model;and (2) data redundancy commonly occurs in the native format of the transformed temporal graphs, which is unnecessary for information propagation and can be eliminated for better memory utilization and execution efficiency. Based on these core insights, we propose TEgraph, the first general-purpose temporal graph computing engine to provide a unified optimization strategy and execution model for general temporal path problems and their applications. TEgraph not only presents temporal information-aware graph representation that naturally fits temporal graphs but also offers general system-level supports such as out-of-core execution. Extensive evaluation reveals that TEgraph can achieve significant speedups over the state-of-the-art designs with up to two orders of magnitude (241x) with the throughput of two hundred million edges per second.

关键词： graph algorithm temporal graphs

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Detection of distant structural similarities in a set of proteins using a fast graph-based method

Detection of distant structural similarities in a set of pro...

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5th International Conference on Intelligent Systems for Molecular Biology (ISMB-97)

作者： Koch, I Lengauer, T German Natl Res Ctr Informat Technol GMD Inst Algorithms & Sci Comp St Augustin Germany

ISBN: (纸本)1577350227

We introduce a method for finding weak structural similarities in a set of protein structures. Proteins are considered at their secondary structure level. The method uses a rigorous graph-theoretical algorithm which finds all structural similarities. Protein structures are modelled as undirected labelled graphs, the so-called protein graphs. We suggest that for detecting the similarities between two protein structures it is sufficient to find similarities in the protein core which consists of tightly packed secondary structure elements. Therefore, we can restrict ourselves to solving the maximal common connected subgraph problem instead of the maximal common subgraph problem. We have modified the algorithm by Bron and Kerbosch for solving that problem. The speed of the algorithm increases drastically. After calculating all maximal common connected substructures for all pairwise comparisons in a set of protein graphs the common substructure in all proteins can be calculated by intersecting them. In this paper we characterize the method briefly and explain the modelling of the protein structure in detail. For the pairwise alignment the similarity of porin (1OMF) with bacteriochlorophyll a (3BCL) and BirA protein (1BIB) with DNA polymerase III (2POL) will be discussed. In the case of the multiple structure alignment the similarity in variants of four phosphatases and in subtilisin Carlsberg, carboxypeptidase, elongation factor Tu, and flavodoxin will be represented. Our first experiments show that the method works correctly and fast. The method can be used for arbitrary graphs. Thus, different graph-theoretical models of protein structures can be examined.

关键词： multiple structure alignment graph algorithm maximal common subgraph structural similarity phosphatases

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graph-theory Based Simplification Techniques for Efficient Biological Network Analysis 3

Graph-theory Based Simplification Techniques for Efficient B...

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3rd IEEE International Conference on Big Data Computing Service and Applications (BigDataService)

作者： Ko, Euiseong Kang, Mingon Chang, Hyung Jae Kim, Donghyun Kennesaw State Univ Dept Comp Sci Marietta GA 30060 USA Troy Univ Montgomery Dept Comp Sci Montgomery AL USA

ISBN: (纸本)9781509063185

The recent years have witnessed the remarkable expansion of publicly available biological data in the related research fields. Many researches in these fields often require massive data to be analyzed by utilizing high-throughput sequencing technologies. However, it is very challenging to interpret the data efficiently due to it high complexity. This paper introduces two new graph algorithms which aim to improve the efficiency of the existing methods for biological network data interpretation. In particular, the algorithms focus on the problem of how to simplify gene regulatory networks so that many existing algorithms can efficiently discover important connected components of a biological system in their own context as many times as they need. The performance of the proposed algorithms is compared with each other with gene expression data of glioblastoma brain tumor cancer.

关键词： Biological Network Analysis graph algorithm Greedy algorithm Optimization

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Improving graph-Based Image Segmentation Using Automatic Programming 17

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17th European Conference on Applications of Evolutionary Computation (EvpApplications)

作者： Magnusson, Lars Vidar Olsson, Roland Ostfold Univ Coll IT Dept Halden Norway

ISBN: (纸本)9783662455234;9783662455227

This paper investigates how Felzenszwalb's and Huttenlocher's graph-based segmentation algorithm can be improved by automatic programming. We show that computers running Automatic Design of algorithms Through Evolution (ADATE), our system for automatic programming, have induced a new graph-based algorithm that is 12 percent more accurate than the original without affecting the runtime efficiency. The result shows that ADATE is capable of improving an effective image segmentation algorithm and suggests that the system can be used to improve image analysis algorithms in general.

关键词： Image segmentation graph algorithm Evolutionary computation Automatic programming

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TEA: A General-Purpose Temporal graph Random Walk Engine 23

TEA: A General-Purpose Temporal Graph Random Walk Engine

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18th European Conference on Computer Systems (EuroSys)

作者： Huan, Chengying Song, Shuaiwen Leon Pandey, Santosh Liu, Hang Liu, Yongchao Lepers, Baptiste He, Changhua Chen, Kang Jiang, Jinlei Wu, Yongwei Tsinghua Univ Beijing Peoples R China Baihai Technol Inc Guangzhou Peoples R China Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ USA Ant Grp Hangzhou Peoples R China Univ Neuchatel Neuchatel Switzerland

ISBN: (纸本)9781450394871

Many real-world graphs are temporal in nature, where the temporal information indicates when a particular edge is changed (e.g., edge insertion and deletion). Performing random walks on such temporal graphs is of paramount value. The state-of-the-art sampling strategies are tailored for conventional static graphs and thus cannot effectively tackle the dynamic nature of temporal graphs due to several significant efficiency challenges, i.e., high sampling complexity, gigantic index space, and poor programmability. In this paper, we present TEA, the first highly-efficient general-purpose TEmporal graph random walk engine. At its core, TEA introduces a new hybrid sampling approach that combines two Monte Carlo sampling methods together to drastically reduce space complexity and achieve high sampling speed. TEA further employs a series of algorithmic and system-level optimizations to remarkably improve the sampling efficiency, as well as provide streaming graph support. Finally, we introduce a temporal-centric programming model to ease the implementation of various random walk algorithms on temporal graphs. Experimental results demonstrate that TEA can achieve up to 3 orders of magnitude speedups over the state-of-the-art random walk engines on large temporal graphs.

关键词： Random walk graph algorithm Temporal graph

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graphLib: A Parallel graph Mining Library for Joint Cloud Computing

GraphLib: A Parallel Graph Mining Library for Joint Cloud Co...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Zhang, Kai Fang, Yange Zheng, Yingying Zeng, Hongbin Xu, Lijie Wang, Wei Chinese Acad Sci State Key Lab Comp Sci Inst Software Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728169811

graph algorithms are widely applied in social networks, computational biology, Internet security and a broad range of complexity science. Although there are many state-of-the-art graph frameworks, few frameworks support parallel graph mining in joint cloud computing environment. In this paper, we propose graphLib, a parallel graph mining library, based on a BSP (Bulk Synchronous Parallel) service over joint cloud computing which was proposed in our prior work. We first summarize the features of commonly-used graph mining algorithms, and present our approaches for parallelizing typical graph mining algorithms. graphLib includes 17 parallel graph mining algorithms that can be used in 3 scenarios. We evaluate the performance of 4 typical parallel graph algorithms in graphLib on three real-world datasets. Our parallelized algorithms can achieve sub-linear scalability.

关键词： graph algorithm BSP JointCloud

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Distributed Triangle Counting algorithms in Simple graph Stream 25

Distributed Triangle Counting Algorithms in Simple Graph Str...

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25th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Yu, Mengdi Song, Chao Gu, Jiqing Liu, Ming Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Elect Sci & Technol China Big Data Res Ctr Chengdu Peoples R China

ISBN: (纸本)9781728125831

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, one-pass streaming algorithm for counting global and local triangles has been widely studied, and most researches focus on the single-machine streaming algorithm in a 'offline+batch processing' mode. However, researches on distributed online algorithm on multiple machines are still in its infancy, and this stage has not been thoroughly studied. In this paper, we investigate the triangle counting problem in large-scale simple undirected graphs whose edges arrive as a stream. We propose two distributed online streaming algorithms to estimate the global number of triangles, which are based on the current best performance sampling-based streaming algorithm. We mainly realize the reasonable partition of the graph stream, so that each worker independently estimates the number of triangles in a subgraph of the graph stream. Experimental results show that our algorithms reduce the estimation error and are several times more accurate than state-of-the-art streaming algorithms.

关键词： Approximate algorithm distributed streaming algorithm graph algorithm graph stream triangle counting algorithm

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DisNet: A Framework for Distributed graph Computation

DisNet: A Framework for Distributed Graph Computation

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International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Lichtenwalter, Ryan Chawla, Nitesh V. Univ Notre Dame Dept Comp Sci Notre Dame IN 46556 USA

ISBN: (纸本)9780769543758

With the rise of network science as an exciting interdisciplinary research topic, efficient graph algorithms are in high demand. Problematically, many such algorithms measuring important properties of networks have asymptotic lower bounds that are quadratic, cubic, or higher in the number of vertices. For analysis of social networks, transportation networks, communication networks, and a host of others, computation is intractable. In these networks computation in serial fashion requires years or even decades. Fortunately, these same computational problems are often naturally parallel. We present here the design and implementation of a master-worker framework for easily computing such results in these circumstances. The user needs only to supply two small fragments of code describing the fundamental kernel of the computation. The framework automatically divides and distributes the workload and manages completion using an arbitrary number of heterogeneous computational resources. In practice, we have used thousands of machines and observed commensurate speedups. Writing only 31 lines of standard C++ code, we computed betweenness centrality on a network of 4.7M nodes in 25 hours.

关键词： algorithm design and analysis Approximation algorithms C++ language Data structures DisNet Instruction sets Libraries Parallel processing Writing asymptotic lower bounds betweenness centrality communication network analysis distributed computing distributed graph computation graph algorithm graph algorithms graph theory heterogeneous computational resources map-reduce master-worker framework network science network theory (graphs) parallel algorithms parallel processing social network analysis standard C++ code transportation network analysis

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