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Relevant bibliographies by topics / Distributed and Parallel Computing / Journal articles

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Journal articles on the topic 'Distributed and Parallel Computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Wismüller, Roland. "Parallel and distributed computing." Software Focus 2, no. 3 (2001): 124. http://dx.doi.org/10.1002/swf.44.

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2

Umar, A. "Distributed And Parallel Computing." IEEE Concurrency 6, no. 4 (1998): 80–81. http://dx.doi.org/10.1109/mcc.1998.736439.

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3

Ramsay, A. "Distributed versus parallel computing." Artificial Intelligence Review 1, no. 1 (1986): 11–25. http://dx.doi.org/10.1007/bf01988525.

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4

Sunderam, V. S., and G. A. Geist. "Heterogeneous parallel and distributed computing." Parallel Computing 25, no. 13-14 (1999): 1699–721. http://dx.doi.org/10.1016/s0167-8191(99)00088-5.

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5

Stankovic. "Introduction—Parallel and Distributed Computing." IEEE Transactions on Computers C-36, no. 4 (1987): 385–86. http://dx.doi.org/10.1109/tc.1987.1676919.

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6

Dalcin, Lisandro D., Rodrigo R. Paz, Pablo A. Kler, and Alejandro Cosimo. "Parallel distributed computing using Python." Advances in Water Resources 34, no. 9 (2011): 1124–39. http://dx.doi.org/10.1016/j.advwatres.2011.04.013.

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7

Myint, Khin Nyein, Myo Hein Zaw, and Win Thanda Aung. "Parallel and Distributed Computing Using MPI on Raspberry Pi Cluster." International Journal of Future Computer and Communication 9, no. 1 (2020): 18–22. http://dx.doi.org/10.18178/ijfcc.2020.9.1.559.

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8

Mukaddes, A. M. M., and Ryuji Shioya. "Parallel Performance of Domain Decomposition Method on Distributed Computing Environment." International Journal of Engineering and Technology 2, no. 1 (2010): 28–34. http://dx.doi.org/10.7763/ijet.2010.v2.95.

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9

Lastovetsky, Alexey. "Special issue of Journal of Parallel and Distributed Computing: Heterogeneity in parallel and distributed computing." Journal of Parallel and Distributed Computing 72, no. 10 (2012): 1397. http://dx.doi.org/10.1016/j.jpdc.2012.06.002.

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10

Pan, Lei, Ming Kin Lai, Koji Noguchi, Javid J. Huseynov, Lubomir F. Bic, and Michael B. Dillencourt. "Distributed Parallel Computing Using Navigational Programming." International Journal of Parallel Programming 32, no. 1 (2004): 1–37. http://dx.doi.org/10.1023/b:ijpp.0000015563.36375.17.

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11

Lastovetsky, Alexey. "Heterogeneity in parallel and distributed computing." Journal of Parallel and Distributed Computing 73, no. 12 (2013): 1523–24. http://dx.doi.org/10.1016/j.jpdc.2013.08.010.

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12

Hertzberger, L. O. "Trends in parallel and distributed computing." Future Generation Computer Systems 7, no. 1 (1991): 31–40. http://dx.doi.org/10.1016/0167-739x(91)90014-o.

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13

Ozturan, Can, and Dan Grigoras. "Guest Editorial: Parallel and Distributed Computing." International Journal of Parallel Programming 39, no. 5 (2011): 582–83. http://dx.doi.org/10.1007/s10766-011-0166-5.

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14

SCHEININE, ALAN LOUIS. "PARALLEL COMPUTING AT CRS4." International Journal of Modern Physics C 04, no. 06 (1993): 1315–21. http://dx.doi.org/10.1142/s0129183193001038.

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An overview is given of parallel computing work being done at CRS4 (Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna). Parallel computation projects include: parallelization of a simulation of the interaction of high energy particles with matter (GEANT), domain decomposition for numerical solution of partial differential equations, seismic migration for oil prospecting, finite-element structural analysis, parallel molecular dynamics, a C++ library for distributed processing of specific functions, and real-time visualization of a computer simulation that runs as distributed processes.

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15

Chard, Kyle, James Muns, Richard Wai, and S. Tucker Taft. "Language support for parallel and distributed computing." ACM SIGAda Ada Letters 40, no. 2 (2021): 51–54. http://dx.doi.org/10.1145/3463478.3463481.

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Language constructs that support parallel computing are relatively well recognized at this point, with features such as parallel loops (optionally with reduction operators), divide-and-conquer parallelism, and general parallel blocks. But what language features would make distributed computing safer and more productive? Is it helpful to be able to specify on what node a computation should take place, and on what node data should reside, or is that overspecification? We don't normally expect a user of a parallel programming language to specify what core is used for a given iteration of a loop,

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16

ZHOU, Xu, Kai LU, Xiaoping WANG, et al. "Deterministic Message Passing for Distributed Parallel Computing." IEICE Transactions on Information and Systems E96.D, no. 5 (2013): 1068–77. http://dx.doi.org/10.1587/transinf.e96.d.1068.

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17

Kopysov, S. P., A. B. Ponomarev, and V. N. Rychkov. "Model computation mesh for parallel distributed computing." Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, no. 2 (April 2008): 194–96. http://dx.doi.org/10.20537/vm080264.

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18

Yang, Laurence T., Yi Pan, and Minyi Guo. "Parallel and distributed scientific and engineering computing." Parallel Computing 29, no. 11-12 (2003): 1505–8. http://dx.doi.org/10.1016/j.parco.2003.08.001.

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19

Yoshikawa, T., and H. Matsuoka. "Optical interconnections for parallel and distributed computing." Proceedings of the IEEE 88, no. 6 (2000): 849–55. http://dx.doi.org/10.1109/5.867698.

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20

D’Ambra, Pasqua, Marco Danelutto, and Daniela di Serafino. "Advanced environments for parallel and distributed computing." Parallel Computing 28, no. 12 (2002): 1635–36. http://dx.doi.org/10.1016/s0167-8191(02)00183-7.

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21

Ranganatban, N. "Parallel And Distributed Computing Handbook [Book Review]." IEEE Concurrency 5, no. 3 (1997): 74–75. http://dx.doi.org/10.1109/mcc.1997.605922.

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22

Zomaya, A. Y., T. El-Ghazawi, and O. Frieder. "Parallel and distributed computing for data mining." IEEE Concurrency 7, no. 4 (1999): 11–13. http://dx.doi.org/10.1109/mcc.1999.806974.

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23

Sunderam, V. S. "PVM: A framework for parallel distributed computing." Concurrency: Practice and Experience 2, no. 4 (1990): 315–39. http://dx.doi.org/10.1002/cpe.4330020404.

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24

Cap, Clemens H., and Volker Strumpen. "Efficient parallel computing in distributed workstation environments." Parallel Computing 19, no. 11 (1993): 1221–34. http://dx.doi.org/10.1016/0167-8191(93)90028-j.

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25

Iványi, P., J. Kruis, T. Kozubek, W. Gentzsch, and B. H. V. Topping. "Civil-Comp: Parallel, Distributed and Cloud Computing." Advances in Engineering Software 103 (January 2017): 12. http://dx.doi.org/10.1016/j.advengsoft.2016.11.002.

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26

Siwik, Leszek, Dawid Kala, Mateusz Godzik, Wojciech Turek, Aleksander Byrski, and Marek Kisiel-Dorohinicki. "Mobile Cloud for Parallel and Distributed Green Computing." Journal of Telecommunications and Information Technology 4 (December 20, 2017): 60–70. http://dx.doi.org/10.26636/jtit.2017.111817.

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Mobile Computing and Mobile Cloud Computing are the areas where intensive research is observed. The “mobility” landscape (devices, technologies, apps, etc.) evolves so fast that deﬁnitions and taxonomies do not catch up with so dynamic changes and there is still an ambiguity in deﬁnitions and common understanding of basic ideas and models. This research focuses on Mobile Cloud understood as parallel and distributed system consisting of a collection of interconnected (and virtualized) mobile devices dynamically provisioned and presented as one uniﬁed computing resource. This paper focuses on th

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27

Spahi, Enis, and D. Altilar. "ITU-PRP: Parallel and Distributed Computing Middleware for Java Developers." International Journal of Business & Technology 3, no. 1 (2014): 2–13. http://dx.doi.org/10.33107/ijbte.2014.3.1.01.

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ITU-PRP provides a Parallel Programming Framework for Java Developers on which they can adapt their sequential application code to operate on a distributed multi-host parallel environment. Developers would implement parallel models, such as Loop Parallelism, Divide and Conquer, Master-Slave and Fork-Join by the help of an API Library provided under framework. Produced parallel applications would be submitted to a middleware called Parallel Running Platform (PRP), on which parallel resources for parallel processing are being organized and performed. The middleware creates Task Plans (TP) accord

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28

Szymczyk, Magdalena, and Piotr Szymczyk. "Matlab and Parallel Computing." Image Processing & Communications 17, no. 4 (2012): 207–16. http://dx.doi.org/10.2478/v10248-012-0048-5.

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Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB paralle

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29

Seredyński, Franciszek. "New Trends in Parallel and Distributed Evolutionary Computing." Fundamenta Informaticae 35, no. 1-4 (1998): 211–30. http://dx.doi.org/10.3233/fi-1998-35123412.

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30

Wang, Chen, and Yong Meng Teo. "Supporting parallel computing on a distributed object architecture." Journal of Systems and Software 56, no. 3 (2001): 261–78. http://dx.doi.org/10.1016/s0164-1212(00)00102-3.

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31

Just, J. R., and R. S. Romaniuk. "Highly parallel distributed computing systems with optical interconnections." Microprocessing and Microprogramming 27, no. 1-5 (1989): 489–93. http://dx.doi.org/10.1016/0165-6074(89)90097-5.

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32

Hanuliak, Michal. "Unified Analytical Models of Parallel and Distributed Computing." American Journal of Networks and Communications 3, no. 1 (2014): 1. http://dx.doi.org/10.11648/j.ajnc.20140301.11.

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33

Cantoni, Virginio, Luca Lombardi, and Paolo Lombardi. "Future scenarios of parallel computing: Distributed sensor networks." Journal of Visual Languages & Computing 18, no. 5 (2007): 484–91. http://dx.doi.org/10.1016/j.jvlc.2007.08.007.

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34

Lathia, M. "A Useful Resource for Parallel and Distributed Computing." IEEE Distributed Systems Online 6, no. 4 (2005): 4. http://dx.doi.org/10.1109/mdso.2005.19.

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35

NISHI, Yasukazu, Shinya OHNUMA, and Takuji SAKAI. "350 Parallel and Distributed Computing by Network-Agents." Proceedings of The Computational Mechanics Conference 2001.14 (2001): 361–62. http://dx.doi.org/10.1299/jsmecmd.2001.14.361.

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36

Cunha, José C., and Pedro D. Medeiros. "Special Issue: Parallel and Distributed Computing (EuroPar 2005)." Concurrency and Computation: Practice and Experience 19, no. 17 (2007): 2183–84. http://dx.doi.org/10.1002/cpe.1267.

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37

Li, Zhiyang, and Keqiu Li. "Recent advances in parallel computing and distributed network." Concurrency and Computation: Practice and Experience 27, no. 16 (2015): 4137–39. http://dx.doi.org/10.1002/cpe.3512.

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38

Senger, Hermes, and Claudio Geyer. "Parallel and distributed computing for Big Data applications." Concurrency and Computation: Practice and Experience 28, no. 8 (2016): 2412–15. http://dx.doi.org/10.1002/cpe.3813.

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39

Bokhari, S. H. "Partitioning problems in parallel, pipeline, and distributed computing." IEEE Transactions on Computers 37, no. 1 (1988): 48–57. http://dx.doi.org/10.1109/12.75137.

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40

Song, Huajun, and Peihua Qiu. "Three-dimensional image registration using distributed parallel computing." IET Image Processing 12, no. 10 (2018): 1713–20. http://dx.doi.org/10.1049/iet-ipr.2017.1021.

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41

GUO, M. "Special Section on Parallel/Distributed Computing and Networking." IEICE Transactions on Information and Systems E89-D, no. 2 (2006): 387–89. http://dx.doi.org/10.1093/ietisy/e89-d.2.387.

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42

Wang, Hao, Yan Yang, Xiaobo Zhang, and Bo Peng. "Parallel multi-view concept clustering in distributed computing." Neural Computing and Applications 32, no. 10 (2019): 5621–31. http://dx.doi.org/10.1007/s00521-019-04243-4.

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43

Khan, Samee Ullah, Pascal Bouvry, and Thomas Engel. "Energy-efficient high-performance parallel and distributed computing." Journal of Supercomputing 60, no. 2 (2010): 163–64. http://dx.doi.org/10.1007/s11227-010-0485-0.

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44

Pascarella, Domenico, Salvatore Venticinque, Rocco Aversa, Massimiliano Mattei, and Luciano Blasi. "Parallel and distributed computing for UAVs trajectory planning." Journal of Ambient Intelligence and Humanized Computing 6, no. 6 (2015): 773–82. http://dx.doi.org/10.1007/s12652-015-0282-y.

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45

Zoraja, Ivan, Hermann Hellwagner, and Vaidy Sunderam. "SCIPVM: Parallel distributed computing on SCI workstation clusters." Concurrency: Practice and Experience 11, no. 3 (1999): 121–38. http://dx.doi.org/10.1002/(sici)1096-9128(199903)11:3<121::aid-cpe368>3.0.co;2-z.

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46

Gichamo, Tseganeh Z., and David G. Tarboton. "UEB parallel: Distributed snow accumulation and melt modeling using parallel computing." Environmental Modelling & Software 125 (March 2020): 104614. http://dx.doi.org/10.1016/j.envsoft.2019.104614.

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47

Liu, Fudong, Zheng Shan, and Yihang Chen. "Parallel Nonnegative Matrix Factorization with Manifold Regularization." Journal of Electrical and Computer Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/6270816.

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Nonnegative matrix factorization (NMF) decomposes a high-dimensional nonnegative matrix into the product of two reduced dimensional nonnegative matrices. However, conventional NMF neither qualifies large-scale datasets as it maintains all data in memory nor preserves the geometrical structure of data which is needed in some practical tasks. In this paper, we propose a parallel NMF with manifold regularization method (PNMF-M) to overcome the aforementioned deficiencies by parallelizing the manifold regularized NMF on distributed computing system. In particular, PNMF-M distributes both data samp

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48

Lin, Hong. "A Case Study of Teaching Parallel and Distributed Computing Topics on a Computer Cluster." Journal of Cases on Information Technology 16, no. 2 (2014): 58–71. http://dx.doi.org/10.4018/jcit.2014040105.

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This paper presents the establishment of cluster computing lab at a minority serving institution that aims to provide computing resources to support undergraduate computer science curriculum. The computing resources of the cluster are managed by a job distribution environment that allows the users to upload, compile, and run their jobs. The job distribution software distributes the submitted jobs to the computing nodes of the cluster. The authors will present a case study of using this platform to teach parallel and distributed computing topics in the operating system course. The evaluation of

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49

Chockler, Gregory, Eliezer Dekel, Joseph JaJa, and Jimmy Lin. "Special Issue of the Journal of Parallel and Distributed Computing: Cloud Computing." Journal of Parallel and Distributed Computing 69, no. 9 (2009): 813. http://dx.doi.org/10.1016/j.jpdc.2009.07.002.

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50

Chi, Song. "Application of MATLAB Parallel Programming Technology." Applied Mechanics and Materials 602-605 (August 2014): 3787–90. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3787.

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The parallel application program development technology and process is analyzed based on the MATLAB parallel and distributed computing toolbox. Hereby, the comparison of the serial computing and the parallel computing is done by computer simulations, and the parallel computing program design and develop method is proposed. The simulations results show that, the parallel computing technology has many advantages in the calculation of high intensive and it is convenience of the parallel application development using MATLAB.

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