Relevant bibliographies by topics / Multi-level graph partitioning

Academic literature on the topic 'Multi-level graph partitioning'

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Published: 6 September 2023

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Journal articles on the topic "Multi-level graph partitioning"

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Fatih Talu, Muhammed. "Multi-level spectral graph partitioning method." Journal of Statistical Mechanics: Theory and Experiment 2017, no. 9 (September 27, 2017): 093406. http://dx.doi.org/10.1088/1742-5468/aa85ba.

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Moreira, Orlando, Merten Popp, and Christian Schulz. "Evolutionary multi-level acyclic graph partitioning." Journal of Heuristics 26, no. 5 (July 15, 2020): 771–99. http://dx.doi.org/10.1007/s10732-020-09448-8.

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Pastukhov, R. K., A. V. Korshunov, D. Yu Turdakov, and S. D. Kuznetsov. "Improving quality of graph partitioning using multi-level optimization." Programming and Computer Software 41, no. 5 (September 2015): 302–6. http://dx.doi.org/10.1134/s0361768815050096.

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Djidjev, Hristo N., Georg Hahn, Susan M. Mniszewski, Christian F. A. Negre, and Anders M. N. Niklasson. "Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics." Algorithms 12, no. 9 (September 7, 2019): 187. http://dx.doi.org/10.3390/a12090187.

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The simulation of the physical movement of multi-body systems at an atomistic level, with forces calculated from a quantum mechanical description of the electrons, motivates a graph partitioning problem studied in this article. Several advanced algorithms relying on evaluations of matrix polynomials have been published in the literature for such simulations. We aim to use a special type of graph partitioning to efficiently parallelize these computations. For this, we create a graph representing the zero–nonzero structure of a thresholded density matrix, and partition that graph into several components. Each separate submatrix (corresponding to each subgraph) is then substituted into the matrix polynomial, and the result for the full matrix polynomial is reassembled at the end from the individual polynomials. This paper starts by introducing a rigorous definition as well as a mathematical justification of this partitioning problem. We assess the performance of several methods to compute graph partitions with respect to both the quality of the partitioning and their runtime.
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Li, Yong, Jinxing Li, Yu Sun, and Haisheng Li. "Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing." Biomimetics 7, no. 4 (October 17, 2022): 168. http://dx.doi.org/10.3390/biomimetics7040168.

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With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing.
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Ahmed, Nesreen, and Ryan Rossi. "Interactive Visual Graph Analytics on the Web." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 566–69. http://dx.doi.org/10.1609/icwsm.v9i1.14653.

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We present a web-based network visual analytics platform called GraphVis that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision-making. The platform is designed with simplicity in mind and allows users to visualize and explore networks in seconds with a simple drag-and-drop of a graph file into the web browser. GraphVis is fast and flexible, web-based, requires no installation, while supporting a wide range of graph formats as well as state-of-the-art visualization and analytic techniques. In particular, the multi-level network analysis engine of GraphVis gives rise to a variety of new possibilities for exploring, analyzing, and understanding complex networks interactively in real-time. Finally, we also highlight other key aspects including filtering, querying, ranking, manipulating, exporting, partitioning (community/role discovery), as well as tools for dynamic network analysis and visualization, interactive graph generators (including two new block model approaches), and a variety of multi-level network analysis and statistical techniques.
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Wan, Xinchen, Kaiqiang Xu, Xudong Liao, Yilun Jin, Kai Chen, and Xin Jin. "Scalable and Efficient Full-Graph GNN Training for Large Graphs." Proceedings of the ACM on Management of Data 1, no. 2 (June 13, 2023): 1–23. http://dx.doi.org/10.1145/3589288.

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Graph Neural Networks (GNNs) have emerged as powerful tools to capture structural information from graph-structured data, achieving state-of-the-art performance on applications such as recommendation, knowledge graph, and search. Graphs in these domains typically contain hundreds of millions of nodes and billions of edges. However, previous GNN systems demonstrate poor scalability because large and interleaved computation dependencies in GNN training cause significant overhead in current parallelization methods. We present G3, a distributed system that can efficiently train GNNs over billion-edge graphs at scale. G3 introduces GNN hybrid parallelism which synthesizes three dimensions of parallelism to scale out GNN training by sharing intermediate results peer-to-peer in fine granularity, eliminating layer-wise barriers for global collective communication or neighbor replications as seen in prior works. G3 leverages locality-aware iterative partitioning and multi-level pipeline scheduling to exploit acceleration opportunities by distributing balanced workload among workers and overlapping computation with communication in both inter-layer and intra-layer training processes. We show via a prototype implementation and comprehensive experiments that G3 can achieve as much as 2.24x speedup in a 16-node cluster, and better final accuracy over prior works.
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Deepthy, Dinesan, and Joseph Varghese Kureethara. "INDUCED \(nK_{2}\) DECOMPOSITION OF INFINITE SQUARE GRIDS AND INFINITE HEXAGONAL GRIDS." Ural Mathematical Journal 8, no. 1 (July 29, 2022): 23. http://dx.doi.org/10.15826/umj.2022.1.003.

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The induced \(nK_2\) decomposition of infinite square grids and hexagonal grids are described here. We use the multi-level distance edge labeling as an effective technique in the decomposition of square grids. If the edges are adjacent, then their color difference is at least 2 and if they are separated by exactly a single edge, then their colors must be distinct. Only non-negative integers are used for labeling. The proposed partitioning technique per the edge labels to get the induced \(nK_2\) decomposition of the ladder graph is the square grid and the hexagonal grid.
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Drummond, C. "Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks." Journal of Artificial Intelligence Research 16 (February 1, 2002): 59–104. http://dx.doi.org/10.1613/jair.904.

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This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm.
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Li, Zhi Yong, and Yun Ping Ai. "Study on the Multi-Core DSP Parallel Processing of Polarization Images Registration." Advanced Materials Research 756-759 (September 2013): 3532–36. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3532.

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Three-channel polarization images must be registered before pixel-level fusion processing to acquire accurate polarization characteristics information. In the condition of serial processing, the image registration efficiency is bad, and then the real-time of polarization imaging application is poor. The multi-core DSP chip which type is TMS320C6670 is selected as the polarization images processing platform. Fourier-Mellin Transform (FMT) is selected as the registration algorithm. The parallel processing of the polarization image registration is studied based on data flow model. The hierarchical task graph is designed in the parallel processing tasks partitioning. According to processing performance and functions, four DSP cores and two FFT coprocessors are divided into different processor groups in each task processing stage. Same hierarchical tasks are assigned to each processor group. According to principles including load balancing and reducing inter-processor communication, algorithms and data of each hierarchical task are assigned manually to each processing unit in the processor group. Experimental results show that the average processing time is 0.429 second while the average registration accuracy achieves 0.5 pixel, the propose parallel processing method improves the efficiency of the polarization image registration.
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Dissertations / Theses on the topic "Multi-level graph partitioning"

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Agarwal, Prateek. "Multi-level Partitioning Algorithms & Reliability Analysis for Transit Networks." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5806.

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Public transit systems are an indispensable part of any metropolitan city. Its success depends on many factors, chief among which is the ease with which users can query for optimal journeys using mobile apps. Conventional approaches model the transit network as a time-expanded or time-dependent graph and run a variant of the Dijkstra's algorithm. However, this method turns out to be too slow for large networks. Furthermore, while planning a journey using public transit, besides travel time, the number of transfers is equally important. To address these problems, several multi-criteria journey planning algorithms such as Round-based Public Transit Routing (RAPTOR), Transfer Patterns, and Trip-based Public Transit Routing (TBTR) have been designed in the past decade. The thesis proposes several modifications to the TBTR algorithm in order to achieve faster queries. Specifically, building on the premise of the range TBTR (rTBTR) problem which finds the optimal journeys departing within a certain time window, we introduce a new One-To-Many rTBTR. Empirical results show that, for one-to-many range queries, our implementation reduces the query times by approximately 70--80\% compared with repeated application of rTBTR. It helps in scenarios where users query for the shortest paths between two geographical locations (instead of two stops) since a location can have multiple nearby stops. Although algorithms such as TBTR and RAPTOR are efficient, they can be speeded up using partitioning-based techniques. To this end, we propose HypTBTR to efficiently solve the bicriteria shortest paths by combining TBTR with a partitioning-based speed-up. We also explore how multi-level partitioning of hypergraphs derived from the transit networks can be used to reduce preprocessing times. The proposed upgrades make the TBTR algorithm practical for large-scale applications. The benefits of our approach are demonstrated using several country-level open datasets. For one-to-one shortest path queries, HypTBTR is 30--40% faster than the TBTR algorithm. Multi-level partitioning in NHypTBTR was found to reduce the preprocessing time by approximately 20--50% on our test networks. The thesis also analyzes the effect of different hypergraph partitioning algorithms such as hMETIS and KaHyPar along with the effect of different weighting schemes on the algorithm's performance. Lastly, the thesis presents an empirical case study demonstrating another real-world application of journey planning algorithms. We study the problem of unreliability in the transit systems, which is one of the main contributing factors behind the decline in transit ridership. We empirically quantify unreliability by evaluating the difference between the scheduled timetables and the actual operations using the Intelligent Transportation System (ITS) data from Bangalore Metropolitan Transport Corporation (BMTC). Our findings can help transit operators create realistic schedules that are easy to adhere to. We also explore how differences in scheduled and actual trips translate to changes in travel time at a source-destination level. Since transit itineraries often involve transfers, departures from schedules can lead to missed connections and longer out-of-vehicle travel times. Using RAPTOR, we queried over a million bi-criteria shortest paths on one day of ITS data. The results show that an average travel time difference of about 17% at the trip level can result in approximately 50% longer journeys for popular source destinations.
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Book chapters on the topic "Multi-level graph partitioning"

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Al-Shaikhli, Saif Dawood Salman, Michael Ying Yang, and Bodo Rosenhahn. "Medical Image Segmentation Using Multi-level Set Partitioning with Topological Graph Prior." In Image and Video Technology – PSIVT 2013 Workshops, 157–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53926-8_15.

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Bunyak, Filiz, and Kannappan Palaniappan. "Level Set-Based Fast Multi-phase Graph Partitioning Active Contours Using Constant Memory." In Advanced Concepts for Intelligent Vision Systems, 145–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04697-1_14.

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Toulouse, Michel, Krishnaiyan Thulasiraman, and Fred Glover. "Multi-level Cooperative Search: A New Paradigm for Combinatorial Optimization and an Application to Graph Partitioning." In Euro-Par’99 Parallel Processing, 533–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48311-x_75.

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Chevalier, Cédric, and François Pellegrini. "Improvement of the Efficiency of Genetic Algorithms for Scalable Parallel Graph Partitioning in a Multi-level Framework." In Euro-Par 2006 Parallel Processing, 243–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823285_25.

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"Topology-Aware Load-Balance Schemes for Heterogeneous Graph Processing." In Advances in Computer and Electrical Engineering, 113–43. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3799-1.ch005.

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Inspired by the insights presented in Chapters 2, 3, and 4, in this chapter the authors present the KCMAX (K-Core MAX) and the KCML (K-Core Multi-Level) frameworks: novel k-core-based graph partitioning approaches that produce unbalanced partitions of complex networks that are suitable for heterogeneous parallel processing. Then they use KCMAX and KCML to explore the configuration space for accelerating BFSs on large complex networks in the context of TOTEM, a BSP heterogeneous GPU + CPU HPC platform. They study the feasibility of the heterogeneous computing approach by systematically studying different graph partitioning strategies, including the KCMAX and KCML algorithms, while processing synthetic and real-world complex networks.
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Conference papers on the topic "Multi-level graph partitioning"

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Moreira, Orlando, Merten Popp, and Christian Schulz. "Evolutionary multi-level acyclic graph partitioning." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205455.3205464.

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Pope, Aaron S., Daniel R. Tauritz, and Alexander D. Kent. "Evolving Multi-level Graph Partitioning Algorithms." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7849930.

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Lee, Y. M., J. S. Wu, T. F. Jiang, and Y. S. Chen. "Direct Numerical Simulation of the Interaction of an Ultra Short-Pulsed Intense Laser With a H2+ Molecule." In ASME 2008 First International Conference on Micro/Nanoscale Heat Transfer. ASMEDC, 2008. http://dx.doi.org/10.1115/mnht2008-52350.

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In this paper, interactions of a linearly polarized ultra short-pulsed intense laser with a single H2+ molecule at various angles of incidence are studied by directly solving the time-dependent three-dimensional Schrodinger equation (TDSE), assuming Born-Oppenheimer approximation. An explicit stagger-time algorithm is employed for time integration of the TDSE, in which the real and imaginary parts of the wave function are defined at alternative times, while a cell-centered finite-volume method is utilized for spatial discretization of the TDSE on Cartesian grids. The TDSE solver is then parallelized using domain decomposition method on distributed memory machines by applying a multi-level graph-partitioning technique. The solver is applied to simulate laser-molecular interaction with test conditions including: laser intensity of 0.5*1014 W/cm2, wavelength of 800 nm, three pulses in time, angle of incidence of 0–90° and inter-nuclear distance of 2 a.u.. Simulation conditions include 4 million hexahedral cells, 90 a.u. long in z direction, and time-step size of 0.005 a.u.. Ionization rates, harmonic spectra and instantaneous distribution of electron densities are then obtained from the solution of the TDSE. Future possible extension of the present method is also outlined at the end of this paper.
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Koeln, Justin P., Matthew A. Williams, and Andrew G. Alleyne. "Hierarchical Control of Multi-Domain Power Flow in Mobile Systems: Part I — Framework Development and Demonstration." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9908.

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This two-part paper presents the development of a hierarchical control framework for the control of power flow throughout mobile systems. These vehicles are comprised of multiple interconnected systems each with multiple subsystems which exhibit dynamics over a wide range of timescales. These interconnections and the timescale separation pose a significant challenge when developing an effective control strategy. Part I presents the proposed graph-based modeling approach and the three-level hierarchical control framework developed to directly address these interconnections and timescale separation. The mobile system is represented as a directed graph with vertices corresponding to the states of the vehicle and edges capturing the power flow throughout the vehicle. The mobile system and the corresponding graph are partitioned spatially into systems and subsystems and temporally into vertices of slow, medium, and fast dynamics. The partitioning facilitates the development of models used by model predictive controllers at each level of the hierarchy. A simple example system is used to demonstrate the approach. Part II utilizes this framework to control the power flow in the electrical and thermal systems of an aircraft. Simulation results show the benefits of hierarchical control compared to centralized and decentralized control methods.
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