Relevant bibliographies by topics / Efficient parallel algorithms

Academic literature on the topic 'Efficient parallel algorithms'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Efficient parallel algorithms"

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de Werra, D. "Efficient parallel algorithms." European Journal of Operational Research 44, no. 3 (February 1990): 426. http://dx.doi.org/10.1016/0377-2217(90)90254-9.

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Bahig, Hazem M., Mohamed A. G. Hazber, Khaled Al-Utaibi, Dieaa I. Nassr, and Hatem M. Bahig. "Efficient Sequential and Parallel Prime Sieve Algorithms." Symmetry 14, no. 12 (November 30, 2022): 2527. http://dx.doi.org/10.3390/sym14122527.

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Generating prime numbers less than or equal to an integer number m plays an important role in many asymmetric key cryptosystems. Recently, a new sequential prime sieve algorithm was proposed based on set theory. The main drawback of this algorithm is that the running time and storage are high when the size of m is large. This paper introduces three new algorithms for a prime sieve based on two approaches. The first approach develops a fast sequential prime sieve algorithm based on set theory and some structural improvements to the recent prime sieve algorithm. The second approach introduces two new parallel algorithms in the shared memory parallel model based on static and dynamic strategies. The analysis of the experimental studies shows the following results. (1) The proposed sequential algorithm outperforms the recent prime sieve algorithm in terms of running time by 98% and memory consumption by 80%, on average. (2) The two proposed parallel algorithms outperform the proposed sequential algorithm by 72% and 67%, respectively, on average. (3) The maximum speedups achieved by the dynamic and static parallel algorithms using 16 threads are 7 and 4.5, respectively. As a result, the proposed algorithms are more effective than the recent algorithm in terms of running time, storage and scalability in generating primes.
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He, Xin. "Efficient parallel algorithms for series parallel graphs." Journal of Algorithms 12, no. 3 (September 1991): 409–30. http://dx.doi.org/10.1016/0196-6774(91)90012-n.

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Rajasekaran, S. "Efficient parallel hierarchical clustering algorithms." IEEE Transactions on Parallel and Distributed Systems 16, no. 6 (June 2005): 497–502. http://dx.doi.org/10.1109/tpds.2005.72.

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Miller, R., and Q. F. Stout. "Efficient parallel convex hull algorithms." IEEE Transactions on Computers 37, no. 12 (1988): 1605–18. http://dx.doi.org/10.1109/12.9737.

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Gibbons, Phillip B., Yossi Matias, and Vijaya Ramachandran. "Efficient Low-Contention Parallel Algorithms." Journal of Computer and System Sciences 53, no. 3 (December 1996): 417–42. http://dx.doi.org/10.1006/jcss.1996.0079.

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García-Monzó, Alejandro, Héctor Migallón, Antonio Jimeno-Morenilla, José-Luis Sánchez-Romero, Héctor Rico, and Ravipudi Rao. "Efficient Subpopulation Based Parallel TLBO Optimization Algorithms." Electronics 8, no. 1 (December 23, 2018): 19. http://dx.doi.org/10.3390/electronics8010019.

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A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuristic algorithms allow problems to be solved more quickly than deterministic methods. The computational time required to obtain the optimum (or near optimum) value of a cost function is a critical aspect of scientific applications in countless fields of knowledge. Therefore, we proposed efficient algorithms parallel to Teaching-learning-based optimization algorithms. TLBO is efficient and free from specific parameters to be tuned. The parallel proposals were designed with two levels of parallelization, one for shared memory platforms and the other for distributed memory platforms, obtaining good parallel performance in both types of parallel architectures and on heterogeneous memory parallel platforms.
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Du, Q., M. Mu, and Z. N. Wu. "Efficient Parallel Algorithms for Parabolic Problems." SIAM Journal on Numerical Analysis 39, no. 5 (January 2002): 1469–87. http://dx.doi.org/10.1137/s0036142900381710.

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RAJASEKARAN, SANGUTHEVAR. "EFFICIENT PARALLEL ALGORITHMS FOR TEMPLATE MATCHING." Parallel Processing Letters 12, no. 03n04 (September 2002): 359–64. http://dx.doi.org/10.1142/s012962640200104x.

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The parallel complexity of template matching has been well studied. In this paper we present more work-efficient algorithms than the existing ones. Our algorithms are based on FFT primitives. We consider the following models of computing: PRAM and the hypercube.
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DAS, SAJAL K., PAUL S. FISHER, and HUA ZHANG. "EFFICIENT PARALLEL ALGORITHMS FOR PATTERN RECOGNITION∗." Parallel Algorithms and Applications 2, no. 1-2 (January 1994): 81–98. http://dx.doi.org/10.1080/10637199408915408.

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Dissertations / Theses on the topic "Efficient parallel algorithms"

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Stadtherr, Hans. "Work efficient parallel scheduling algorithms." [S.l. : s.n.], 1998. http://deposit.ddb.de/cgi-bin/dokserv?idn=962681369.

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Huang, Chun-Hsi. "Communication-efficient bulk synchronous parallel algorithms." Buffalo, N.Y. : Dept. of Computer Science, State University of New York at Buffalo, 2001. http://www.cse.buffalo.edu/tech%2Dreports/2001%2D06.ps.Z.

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Liang, Weifa, and wliang@cs anu edu au. "Designing Efficient Parallel Algorithms for Graph Problems." The Australian National University. Department of Computer Science, 1997. http://thesis.anu.edu.au./public/adt-ANU20010829.114536.

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Graph algorithms are concerned with the algorithmic aspects of solving graph problems. The problems are motivated from and have application to diverse areas of computer science, engineering and other disciplines. Problems arising from these areas of application are good candidates for parallelization since they often have both intense computational needs and stringent response time requirements. Motivated by these concerns, this thesis investigates parallel algorithms for these kinds of graph problems that have at least one of the following properties: the problems involve some type of dynamic updates; the sparsification technique is applicable; or the problems are closely related to communications network issues. The models of parallel computation used in our studies are the Parallel Random Access Machine (PRAM) model and the practical interconnection network models such as meshes and hypercubes. ¶ Consider a communications network which can be represented by a graph G = (V;E), where V is a set of sites (processors), and E is a set of links which are used to connect the sites (processors). In some cases, we also assign weights and/or directions to the edges in E. Associated with this network, there are many problems such as (i) whether the network is k-edge (k-vertex) connected withfixed k; (ii) whether there are k-edge (k-vertex) disjoint paths between u and v for a pair of given vertices u and v after the network is dynamically updated by adding and/or deleting an edge etc; (iii) whether the sites in the network can communicate with each other when some sites and links fail; (iv) identifying the first k edges in the network whose deletion will result in the maximum increase in the routing cost in the resulting network for fixed k; (v) how to augment the network at optimal cost with a given feasible set of weighted edges such that the augmented network is k-edge (k-vertex) connected; (vi) how to route messages through the network efficiently. In this thesis we answer the problems mentioned above by presenting efficient parallel algorithms to solve them. As far as we know, most of the proposed algorithms are the first ones in the parallel setting. ¶ Even though most of the problems concerned in this thesis are related to communications networks, we also study the classic edge-coloring problem. The outstanding difficulty to solve this problem in parallel is that we do not yet know whether or not it is in NC. In this thesis we present an improved parallel algorithm for the problem which needs [bigcircle]([bigtriangleup][superscript 4.5]log [superscript 3] [bigtriangleup] log n + [bigtriangleup][superscript 4] log [superscript 4] n) time using [bigcircle](n[superscript 2][bigtriangleup] + n[bigtriangleup][superscript 3]) processors, where n is the number of vertices and [bigtriangleup] is the maximum vertex degree. Compared with a previously known result on the same model, we improved by an [bigcircle]([bigtriangleup][superscript 1.5]) factor in time. The non-trivial part is to reduce this problem to the edge-coloring update problem. We also generalize this problem to the approximate edge-coloring problem by giving a faster parallel algorithm for the latter case. ¶ Throughout the design and analysis of parallel graph algorithms, we also find a technique called the sparsification technique is very powerful in the design of efficient sequential and parallel algorithms on dense undirected graphs. We believe that this technique may be useful in its own right for guiding the design of efficient sequential and parallel algorithms for problems in other areas as well as in graph theory.
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Yang, Yong. "Efficient parallel genetic algorithms applied to numerical optimisation." Thesis, Southampton Solent University, 2008. http://ssudl.solent.ac.uk/631/.

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This research is concerned with the optimisation of multi-modal numerical problems using genetic algorithms (GAs). GAs use randomised operators operating over a population of candidate solutions to generate new points in the search space. As the scale and complexity of target applications increase, run time becomes a major inhibitor. Parallel genetic algorithms (PGAs) have therefore become an important area of research. Coarse-grained implementations are one of the most popular models and many researchers are concerned primarily with this area. The island model was the only one class of parallel genetic algorithm on the coarse-grained processing platform. There are indiscriminate overlaps between sub-populations in the island model even if there is no communication between sub-populations. In order to determine whether the removal of the overlaps between sub-populations is beneficial, the partition model based on domain decomposition was motivated and showed that it can offer superior performance on a number of two dimensional test problems. However the partition model has a certain scalability problem. The main contribution of this thesis is to propose and develop an alternative approach, which replicates the beneficial behaviour of the partition model using more scalable techniques. It operates in a similar manner to the island model, but periodically performs a clustering analysis on each sub-population. The clustering analysis is used to identify regions of the search space in which more than one sub-population are sampling. The overlapping clusters are then merged and redistributed amongst sub-populations so that only one sub-population has samples in that region of the search space. It is shown that these overlaps between sub-populations are identified and removed by the clustering analysis without a priori domain knowledge. The performance of the new approach employing the clustering analysis is then compared with the island model and the partition model on a number of non-trivial problems. These experiments show that the new approach is robust, efficient and effective, and removes the scalability problem that prevents the wide application of the partition model.
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Goldberg, Andrew Vladislav. "Efficient graph algorithms for sequential and parallel computers." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14912.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Bibliography: p. 117-123.
by Andrew Vladislav Goldberg.
Ph.D.
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He, Xin. "On efficient parallel algorithms for solving graph problems /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487331541710947.

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Chen, Calvin Ching-Yuen. "Efficient Parallel Algorithms and Data Structures Related to Trees." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332626/.

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The main contribution of this dissertation proposes a new paradigm, called the parentheses matching paradigm. It claims that this paradigm is well suited for designing efficient parallel algorithms for a broad class of nonnumeric problems. To demonstrate its applicability, we present three cost-optimal parallel algorithms for breadth-first traversal of general trees, sorting a special class of integers, and coloring an interval graph with the minimum number of colors.
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Halverson, Ranette Hudson. "Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278153/.

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The goal of a parallel algorithm is to solve a single problem using multiple processors working together and to do so in an efficient manner. In this regard, there is a need to categorize strategies in order to solve broad classes of problems with similar structures and requirements. In this dissertation, two parallel algorithm design strategies are considered: linked list ranking and parentheses matching.
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邱祖淇 and Cho-ki Joe Yau. "Efficient solutions for the load distribution problem." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31222031.

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Yau, Cho-ki Joe. "Efficient solutions for the load distribution problem /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20971953.

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Books on the topic "Efficient parallel algorithms"

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Alan, Gibbons. Efficient parallel algorithms. Cambridge [England]: Cambridge University Press, 1988.

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Designing efficient algorithms for parallel computers. New York: McGraw-Hill, 1987.

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Hochschild, Peter H. Resource-efficient parallel algorithms. Stanford, CA: Dept. of ComputerScience, Stanford University, 1985.

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Designing efficient algorithms for parallel computers. Maidenhead: McGraw, 1988.

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Cantú-Paz, Erick. Efficient and Accurate Parallel Genetic Algorithms. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-4369-5.

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Cantú-Paz, Erick. Efficient and accurate parallel genetic algorithms. Boston, Mass: Kluwer Academic Publishers, 2000.

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Cantú-Paz, Erick. Efficient and accurate parallel genetic algorithms. New York: Springer Science+Business Media, LLC, 2001.

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Shen, Hong. Efficient design and implementation of parallel algorithms. Åbo: Åbo Akademis Förlag, 1991.

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Vajteršic, Marián. Algorithms for elliptic problems: Efficient sequential and parallel solvers. Dordrecht: Kluwer Academic Publishers, 1993.

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Vajteršic, Marián. Algorithms for Elliptic Problems: Efficient Sequential and Parallel Solvers. Dordrecht: Springer Netherlands, 1993.

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Book chapters on the topic "Efficient parallel algorithms"

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Dedorath, Jürgen, Jordan Gergov, and Torben Hagerup. "More efficient parallel flow algorithms." In Algorithms and Computations, 234–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0015428.

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Hyyrö, Heikki, Yoan Pinzon, and Ayumi Shinohara. "New Bit-Parallel Indel-Distance Algorithm." In Experimental and Efficient Algorithms, 380–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427186_33.

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Hsieh, Sun-Yuan, and Chin-Wen Ho. "An efficient parallel strategy for recognizing series-parallel graphs." In Algorithms and Computation, 496–504. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58325-4_216.

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Santos, Eunice E., and Pei-Yue Pauline Chu. "Efficient Parallel Algorithms for Dense Cholesky Factorization." In Parallel Computation, 600–602. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-49164-3_69.

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Cantú-Paz, Erick. "Master-Slave Parallel Genetic Algorithms." In Efficient and Accurate Parallel Genetic Algorithms, 33–48. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-4369-5_3.

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Bannach, Max, Malte Skambath, and Till Tantau. "Towards Work-Efficient Parallel Parameterized Algorithms." In WALCOM: Algorithms and Computation, 341–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-10564-8_27.

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Ramachandran, Vijaya, and Uzi Vishkin. "Efficient parallel triconnectivity in logarithmic time." In VLSI Algorithms and Architectures, 33–42. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/bfb0040371.

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Fredriksson, Kimmo, and Szymon Grabowski. "Efficient Bit-Parallel Algorithms for (δ,α)-Matching." In Experimental Algorithms, 170–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11764298_15.

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Atallah, Mikhail J., Danny Z. Chen, and Ovidiu Daescu. "Efficient parallel algorithms for planar st-graphs." In Algorithms and Computation, 223–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63890-3_25.

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Hanashiro, Erik J., Henrique Mongelli, and Siang W. Song. "Efficient Implementation of the BSP/CGM Parallel Vertex Cover FPT Algorithm." In Experimental and Efficient Algorithms, 253–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24838-5_19.

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Conference papers on the topic "Efficient parallel algorithms"

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Alves, David R., Madan S. Krishnakumar, and Vijay K. Garg. "Efficient Parallel Shortest Path Algorithms." In 2020 19th International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, 2020. http://dx.doi.org/10.1109/ispdc51135.2020.00034.

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Gibbons, Phillip B., Yossi Matias, and Vijaya Ramachandran. "Efficient low-contention parallel algorithms." In the sixth annual ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/181014.181382.

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Anderson, Daniel, Guy E. Blelloch, Anubhav Baweja, and Umut A. Acar. "Efficient Parallel Self-Adjusting Computation." In SPAA '21: 33rd ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3409964.3461799.

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Venter, Gerhard, and Brian Watson. "Efficient optimization algorithms for parallel applications." In 8th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4819.

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Klein, P. N. "Efficient parallel algorithms for chordal graphs." In [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science. IEEE, 1988. http://dx.doi.org/10.1109/sfcs.1988.21933.

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Mishin, Nikita, Daniil Berezun, and Alexander Tiskin. "Efficient Parallel Algorithms for String Comparison." In ICPP 2021: 50th International Conference on Parallel Processing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472456.3472489.

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Mishin, Nikita, Daniil Berezun, and Alexander Tiskin. "Efficient Parallel Algorithms for String Comparison." In ICPP 2021: 50th International Conference on Parallel Processing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472456.3472489.

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Panda, Manas Kumar, and G. Sajith. "Efficient Parallel Cache-Oblivious Sorting Algorithms." In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2022. http://dx.doi.org/10.1109/iceccme55909.2022.9988160.

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Greenberg, Albert G., Boris D. Lubachevsky, and Andrew M. Odlyzko. "Simple, efficient asynchronous parallel algorithms for maximization." In the fourth annual ACM symposium. New York, New York, USA: ACM Press, 1985. http://dx.doi.org/10.1145/323596.323625.

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McClintock, Jessica, and Anthony Wirth. "Efficient Parallel Algorithms for k-Center Clustering." In 2016 45th International Conference on Parallel Processing (ICPP). IEEE, 2016. http://dx.doi.org/10.1109/icpp.2016.22.

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Reports on the topic "Efficient parallel algorithms"

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Guattery, Stephen, and Gary L. Miller. Efficient Parallel Algorithms for Planar DAGs,. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada297035.

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Suciu, Dan, and Val Tannen. Efficient Compilation of High-Level Data Parallel Algorithms. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada290362.

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Kanellakis, Paris C., and Alex A. Shvartsman. Efficient Parallel Algorithms on Restartable Fail-Stop Processors. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada236249.

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Ramachandran, Vijaya. Fast and Processor-Efficient Parallel Algorithms for Reducible Flow Graphs. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada201651.

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Tolimieri, Richard. Efficient and Flexible Algorithms for Digital Signal Processing on Multiple Independent Node Parallel Computers. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada295094.

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Freitag, L., P. Plassmann, and M. Jones. An efficient parallel algorithm for mesh smoothing. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/414390.

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Hendrickson, B., R. Leland, and S. Plimpton. An efficient parallel algorithm for matrix-vector multiplication. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/6519330.

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Kanellakis, Paris O., and Alex A. Shvartsman. Fault-Tolerance and Efficiency in Massively Parallel Algorithms. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada283255.

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Hardwick, Jonathan C. Implementation and Evaluation of an Efficient 2D Parallel Delaunay Triangulation Algorithm,. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada328005.

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Madduri, Kamesh, David Ediger, Karl Jiang, David A. Bader, and Daniel Chavarria-Miranda. A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/951102.

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