Добірка наукової літератури з теми "Load graph"
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Статті в журналах з теми "Load graph"
Bok, Kyoungsoo, Junwon Kim, and Jaesoo Yoo. "Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores." Symmetry 11, no. 7 (July 16, 2019): 926. http://dx.doi.org/10.3390/sym11070926.
Повний текст джерелаMoharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online load balancing under graph constraints." ACM SIGMETRICS Performance Evaluation Review 41, no. 1 (June 14, 2013): 363–64. http://dx.doi.org/10.1145/2494232.2465751.
Повний текст джерелаMoharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online Load Balancing Under Graph Constraints." IEEE/ACM Transactions on Networking 24, no. 3 (June 2016): 1690–703. http://dx.doi.org/10.1109/tnet.2015.2442597.
Повний текст джерелаCOSNARD, M., and M. LOI. "AUTOMATIC TASK GRAPH GENERATION TECHNIQUES." Parallel Processing Letters 05, no. 04 (December 1995): 527–38. http://dx.doi.org/10.1142/s0129626495000473.
Повний текст джерелаYang, Carl, Aydın Buluç, and John D. Owens. "GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU." ACM Transactions on Mathematical Software 48, no. 1 (March 31, 2022): 1–51. http://dx.doi.org/10.1145/3466795.
Повний текст джерелаDraief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 01 (March 2009): 199–220. http://dx.doi.org/10.1017/s0021900200005313.
Повний текст джерелаDraief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 1 (March 2009): 199–220. http://dx.doi.org/10.1239/jap/1238592125.
Повний текст джерелаSharma, Bhuvan, Van C. Willis, Claudia S. Huettner, Kirk Beaty, Jane L. Snowdon, Shang Xue, Brett R. South, Gretchen P. Jackson, Dilhan Weeraratne, and Vanessa Michelini. "Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs." JAMIA Open 3, no. 3 (September 29, 2020): 332–37. http://dx.doi.org/10.1093/jamiaopen/ooaa028.
Повний текст джерелаAlistarh, Dan, Giorgi Nadiradze, and Amirmojtaba Sabour. "Dynamic Averaging Load Balancing on Cycles." Algorithmica 84, no. 4 (December 24, 2021): 1007–29. http://dx.doi.org/10.1007/s00453-021-00905-9.
Повний текст джерелаJeurissen, R., and W. Layton. "Load balancing by graph coloring, an algorithm." Computers & Mathematics with Applications 27, no. 3 (February 1994): 27–32. http://dx.doi.org/10.1016/0898-1221(94)90043-4.
Повний текст джерелаДисертації з теми "Load graph"
Barat, Remi. "Load Balancing of Multi-physics Simulation by Multi-criteria Graph Partitioning." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0961/document.
Повний текст джерелаMultiphysics simulation couple several computation phases. When they are run in parallel on memory-distributed architectures, minimizing the simulation time requires in most cases to balance the workload across computation units, for each computation phase. Moreover, the data distribution must minimize the induced communication. This problem can be modeled as a multi-criteria graph partitioning problem. We associate with each vertex of the graph a vector of weights, whose components, called “criteria”, model the workload of the vertex for each computation phase. The edges between vertices indicate data dependencies, and can be given a weight representing the communication volume transferred between the two vertices. The goal is to find a partition of the vertices that both balances the weights of each part for each criterion, and minimizes the “edgecut”, that is, the sum of the weights of the edges cut by the partition. The maximum allowed imbalance is provided by the user, and we search for a partition that minimizes the edgecut, among all the partitions whose imbalance for each criterion is smaller than this threshold. This problem being NP-Hard in the general case, this thesis aims at devising and implementing heuristics that allow us to compute efficiently such partitions. Indeed, existing tools often return partitions whose imbalance is higher than the prescribed tolerance. Our study of the solution space, that is, the set of all the partitions respecting the balance constraints, reveals that, in practice, this space is extremely large. Moreover, we prove in the mono-criterion case that a bound on the normalized vertex weights guarantees the existence of a solution, and the connectivity of the solution space. Based on these theoretical results, we propose improvements of the multilevel algorithm. Existing tools implement many variations of this algorithm. By studying their source code, we emphasize these variations and their consequences, in light of our analysis of the solution space. Furthermore, we define and implement two initial partitioning algorithms, focusing on returning a solution. From a potentially imbalanced partition, they successively move vertices from one part to another. The first algorithm performs any move that reduces the imbalance, while the second performs at each step the move reducing the most the imbalance. We present an original data structure that allows us to optimize the choice of the vertex to move, and leads to partitions of imbalance smaller on average than existing methods. We describe the experimentation framework, named Crack, that we implemented in order to compare the various algorithms at stake. This comparison is performed by partitioning a set of instances including an industrial test case, and several fictitious cases. We define a method for generating realistic weight distributions corresponding to “Particles-in-Cells”-like simulations. Our results demonstrate the necessity to coerce the vertex weights during the coarsening phase of the multilevel algorithm. Moreover, we evidence the impact of the vertex ordering, which should depend on the graph topology, on the efficiency of the “Heavy-Edge” matching scheme. The various algorithms that we consider are implemented in an open- source graph partitioning software called Scotch. In our experiments, Scotch and Crack returned a balanced partition for each execution, whereas MeTiS, the current most used partitioning tool, fails regularly. Additionally, the edgecut of the solutions returned by Scotch and Crack is equivalent or better than the edgecut of the solutions returned by MeTiS
Predari, Maria. "Load balancing for parallel coupled simulations." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0369/document.
Повний текст джерелаLoad balancing is an important step conditioning the performance of parallel applications. The goal is to distribute roughly equal amounts of computational load across a number of processors, while minimising interprocessor communication. A common approach to model the problem is based on graph structures and graph partitioning algorithms. Moreover, new challenges involve the simulation of more complex physical phenomena, where different parts of the computational domain exhibit different physical behavior. Such simulations follow the paradigm of multi-physics or multi-scale modeling approaches. Combining such different models in massively parallel computations is still a challenge to reach high performance. Additionally, traditional load balancing algorithms are often inadequate, and more sophisticated solutions should be explored. In this thesis, we propose new graph partitioning algorithms that balance the load of such simulations, refered to as co-partitioning. We formulate this problem with the use of graph partitioning with initially fixed vertices which we believe represents efficiently the additional constraints of coupled simulations. We have therefore developed a direct algorithm for graph partitioning that manages successfully problems with fixed vertices. The algorithm is implemented inside Scotch partitioner and a series of experiments were carried out on the DIMACS graph collection. Moreover we proposed three copartitioning algorithms that respect the constraints of the respective coupled codes. We finally validated our algorithms by an experimental study comparing our methods with current strategies on artificial cases and on real-life coupled simulations
Sun, Jiawen. "The GraphGrind framework : fast graph analytics on large shared-memory systems." Thesis, Queen's University Belfast, 2018. https://pure.qub.ac.uk/portal/en/theses/the-graphgrind-framework-fast-graph-analytics-on-large-sharedmemory-systems(e1eb006f-3a68-4d05-91fe-961d04b42694).html.
Повний текст джерелаDeveci, Mehmet. "Load-Balancing and Task Mapping for Exascale Systems." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429199721.
Повний текст джерелаBotadra, Harnish. "iC2mpi a platform for parallel execution of graph-structured iterative computations /." unrestricted, 2006. http://etd.gsu.edu/theses/available/etd-07252006-165725/.
Повний текст джерелаTitle from title screen. Sushil Prasad, committee chair. Electronic text (106 p. : charts) : digital, PDF file. Description based on contents viewed June 11, 2007. Includes bibliographical references. Includes bibliographical references (p. 61-53).
Yildiz, Ali. "Resource-aware Load Balancing System With Artificial Neural Networks." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607613/index.pdf.
Повний текст джерелаZheng, Chunfang. "GRAPHICAL MODELING AND SIMULATION OF A HYBRID HETEROGENEOUS AND DYNAMIC SINGLE-CHIP MULTIPROCESSOR ARCHITECTURE." UKnowledge, 2004. http://uknowledge.uky.edu/gradschool_theses/249.
Повний текст джерелаGillet, Noel. "Optimisation de requêtes sur des données massives dans un environnement distribué." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0553/document.
Повний текст джерелаDistributed data store are massively used in the actual context of Big Data. In addition to provide data management features, those systems have to deal with an increasing amount of queries sent by distant users in order to process data mining or data visualization operations. One of the main challenge is to evenly distribute the workload of queries between the nodes which compose these system in order to minimize the treatment time. In this thesis, we tackle the problem of query allocation in a distributed environment. We consider that data are replicated and a query can be handle only by a node storing the concerning data. First, near-optimal algorithmic proposals are given when communications between nodes are asynchronous. We also consider that some nodes can be faulty. Second, we study more deeply the impact of data replication on the query treatement. Particularly, we present an algorithm which manage the data replication based on the demand on these data. Combined with our allocation algorithm, we guaranty a near-optimal allocation. Finally, we focus on the impact of data replication when queries are received as a stream by the system. We make an experimental evaluation using the distributed database Apache Cassandra. The experiments confirm the interest of our algorithmic proposals to improve the query treatement compared to the native allocation scheme in Cassandra
Tbaileh, Ahmad Anan. "Robust Non-Matrix Based Power Flow Algorithm for Solving Integrated Transmission and Distribution Systems." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/89362.
Повний текст джерелаPHD
Cheng, Danling. "Integrated System Model Reliability Evaluation and Prediction for Electrical Power Systems: Graph Trace Analysis Based Solutions." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28944.
Повний текст джерелаPh. D.
Книги з теми "Load graph"
Graph out loud: A graphical guide to popular culture. New York: Gotham Books, 2009.
Знайти повний текст джерелаGraph out loud: A graphical guide to popular culture. New York: Gotham Books, 2009.
Знайти повний текст джерелаGraph out loud: A graphical guide to popular culture. New York: Gotham Books, 2009.
Знайти повний текст джерелаGraph out loud: A graphical guide to popular culture. New York: Gotham Books, 2009.
Знайти повний текст джерелаKrake, LR, N. Steele Scott, MA Rezaian, and RH Taylor. Graft-transmitted Diseases of Grapevines. CSIRO Publishing, 1999. http://dx.doi.org/10.1071/9780643101067.
Повний текст джерелаTurley, Glynda. Lily Pond Medium Turley Biblecover Grape: This is the Day Which the Lord Hath Made Psalm 118:24. Gregg Manufacturing, 1999.
Знайти повний текст джерелаTurley, Glynda. Lily Pond Large Turley Biblecover Grape: This is the Day Which the Lord Hath Made Psalm 118:24. Gregg Manufacturing, 1999.
Знайти повний текст джерелаTurley, Glynda. Lily Pond Extra Large Turley Biblecover Grape: This is the Day Which the Lord Hath Made Psalm 118:24. Gregg Manufacturing, 1999.
Знайти повний текст джерелаЧастини книг з теми "Load graph"
Dey, Lakshmi Kanta, Debashis Ghosh, and Satya Bagchi. "Efficient Load Balancing Algorithm Using Complete Graph." In Communications in Computer and Information Science, 643–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23223-7_83.
Повний текст джерелаBoillat, Jacques E. "Fast load balancing in Cayley graphs and in circuits." In Graph-Theoretic Concepts in Computer Science, 315–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57899-4_62.
Повний текст джерелаKiefer, Tim, Dirk Habich, and Wolfgang Lehner. "Penalized Graph Partitioning for Static and Dynamic Load Balancing." In Euro-Par 2016: Parallel Processing, 146–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43659-3_11.
Повний текст джерелаAliev, Araz R., and Nigar T. Ismayilova. "Graph-Based Load Balancing Model for Exascale Computing Systems." In 11th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions and Artificial Intelligence - ICSCCW-2021, 229–36. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92127-9_33.
Повний текст джерелаWanka, Rolf. "Any Load-Balancing Regimen for Evolving Tree Computations on Circulant Graphs Is Asymptotically Optimal." In Graph-Theoretic Concepts in Computer Science, 413–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36379-3_36.
Повний текст джерелаBusato, Federico, and Nicola Bombieri. "Efficient Load Balancing Techniques for Graph Traversal Applications on GPUs." In Euro-Par 2018: Parallel Processing, 628–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96983-1_45.
Повний текст джерелаMunasinghe, Kalyani, and Richard Wait. "Load Balancing by Changing the Graph Connectivity on Heterogeneous Clusters." In Advances in Grid Computing - EGC 2005, 1040–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11508380_106.
Повний текст джерелаMárquez, Claudio, Eduardo César, and Joan Sorribes. "Graph-Based Automatic Dynamic Load Balancing for HPC Agent-Based Simulations." In Euro-Par 2015: Parallel Processing Workshops, 405–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27308-2_33.
Повний текст джерелаDeng, Yiran, Yingjie Zhou, and Zhiyong Zhang. "Short-Long Correlation Based Graph Neural Networks for Residential Load Forecasting." In Neural Information Processing, 428–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92270-2_37.
Повний текст джерелаZheng, Hang, Xu Ding, Yang Wang, and Chong Zhao. "Attention Based Spatial-Temporal Graph Convolutional Networks for RSU Communication Load Forecasting." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 99–114. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92635-9_7.
Повний текст джерелаТези доповідей конференцій з теми "Load graph"
Moharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online load balancing under graph constraints." In the ACM SIGMETRICS/international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2465529.2465751.
Повний текст джерелаSun, He, and Luca Zanetti. "Distributed Graph Clustering by Load Balancing." In SPAA '17: 29th ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3087556.3087569.
Повний текст джерелаTalukder, Nilothpal, and Mohammed J. Zaki. "Parallel graph mining with dynamic load balancing." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840995.
Повний текст джерелаPandat, Ami, Nidhi Gupta, and Minal Bhise. "Load Balanced Semantic Aware Distributed RDF Graph." In IDEAS 2021: 25th International Database Engineering & Applications Symposium. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472163.3472167.
Повний текст джерелаKumar, Kriti, Rahul Sinha, M. Girish Chandra, and Naveen Kumar Thokala. "Data-driven electrical load disaggregation using graph signal processing." In 2016 IEEE Annual India Conference (INDICON). IEEE, 2016. http://dx.doi.org/10.1109/indicon.2016.7839109.
Повний текст джерелаBatreddy, Subbareddy, Kriti Kumar, and M. Girish Chandra. "On Using Graph Signal Processing for Electrical Load Disaggregation." In 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, 2019. http://dx.doi.org/10.1109/hipcw.2019.00008.
Повний текст джерелаBelghaouti, Fethi, Amel Bouzeghoub, Zakia Kazi Aoul, and Raja Chiky. "Graph-oriented load-shedding for semantic Data Stream processing." In 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM). IEEE, 2015. http://dx.doi.org/10.1109/iwcim.2015.7347064.
Повний текст джерелаKumar, Amit, and Hemant Kumar Meena. "Non-intrusive load monitoring based on graph signal processing." In 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE). IEEE, 2017. http://dx.doi.org/10.1109/rdcape.2017.8358232.
Повний текст джерелаKuo, Ming-Chia, Pangfeng Liu, and Jan-Jan Wu. "An Efficient Dynamic Load-Balancing Large Scale Graph-Processing System." In the 2018 VII International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3301326.3301343.
Повний текст джерелаZhao, Bochao, Lina Stankovic, and Vladimir Stankovic. "Blind non-intrusive appliance load monitoring using graph-based signal processing." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418158.
Повний текст джерелаЗвіти організацій з теми "Load graph"
Bhatele, Abhinav, Sebastien Fourestier, Harshitha Menon, Laxmikant V. Kale, and Francois Pellegrini. Applying graph partitioning methods in measurement-based dynamic load balancing. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1114706.
Повний текст джерелаBhatele, A., S. Fourestier, H. Menon, L. Kale, and F. Pellegrini. Applying graph partitioning methods in measurement-based dynamic load balancing. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1093410.
Повний текст джерелаFinancial Stability Report - Second Semester of 2020. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/rept-estab-fin.sem2.eng-2020.
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