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Journal articles on the topic 'Parallel Patterns'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Goh, John, and David Taniar. "Mining Parallel Patterns from Mobile Users." International Journal of Business Data Communications and Networking 1, no. 1 (2005): 50–76. http://dx.doi.org/10.4018/jbdcn.2005010104.

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2

Prabhakar, Raghu, David Koeplinger, Kevin J. Brown, et al. "Generating Configurable Hardware from Parallel Patterns." ACM SIGARCH Computer Architecture News 44, no. 2 (2016): 651–65. http://dx.doi.org/10.1145/2980024.2872415.

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3

Catanzaro, Bryan, and Kurt Keutzer. "Parallel computing with patterns and frameworks." XRDS: Crossroads, The ACM Magazine for Students 17, no. 1 (2010): 22–27. http://dx.doi.org/10.1145/1836543.1836552.

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4

Jadud, Matthew C., Jon Simpson, and Christian L. Jacobsen. "Patterns for programming in parallel, pedagogically." ACM SIGCSE Bulletin 40, no. 1 (2008): 231–35. http://dx.doi.org/10.1145/1352322.1352219.

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5

Prabhakar, Raghu, David Koeplinger, Kevin J. Brown, et al. "Generating Configurable Hardware from Parallel Patterns." ACM SIGPLAN Notices 51, no. 4 (2016): 651–65. http://dx.doi.org/10.1145/2954679.2872415.

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6

Prabhakar, Raghu, David Koeplinger, Kevin J. Brown, et al. "Generating Configurable Hardware from Parallel Patterns." ACM SIGOPS Operating Systems Review 50, no. 2 (2016): 651–65. http://dx.doi.org/10.1145/2954680.2872415.

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7

Meddah, Ishak H. A., and Nour El Houda REMIL. "Parallel and Distributed Pattern Mining." International Journal of Rough Sets and Data Analysis 6, no. 3 (2019): 1–17. http://dx.doi.org/10.4018/ijrsda.2019070101.

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The treatment of large data is difficult and it looks like the arrival of the framework MapReduce is a solution of this problem. This framework can be used to analyze and process vast amounts of data. This happens by distributing the computational work across a cluster of virtual servers running in a cloud or a large set of machines. Process mining provides an important bridge between data mining and business process analysis. Its techniques allow for extracting information from event logs. Generally, there are two steps in process mining, correlation definition or discovery and the inference

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8

ZHAO, SHIQI, HAIFENG WANG, TING LIU, and SHENG LI. "Extracting paraphrase patterns from bilingual parallel corpora." Natural Language Engineering 15, no. 4 (2009): 503–26. http://dx.doi.org/10.1017/s1351324909990155.

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AbstractParaphrase patterns are semantically equivalent patterns, which are useful in both paraphrase recognition and generation. This paper presents a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the paraphrase patterns in English are extracted using the patterns in another language as pivots. We make use of log-linear models for computing the paraphrase likelihood between pattern pairs and exploit feature functions based on maximum likelihood estimation (MLE), lexical weighting (LW), and monolingual word alignment (MWA). Using the presented metho

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9

Danelutto, Marco, Peter Kilpatrick, Gabriele Mencagli, and Massimo Torquati. "State access patterns in stream parallel computations." International Journal of High Performance Computing Applications 32, no. 6 (2017): 807–18. http://dx.doi.org/10.1177/1094342017694134.

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We introduce a set of state access patterns suitable for managing accesses to states in parallel computations operating on streams. The state access patterns are useful for modelling typical stream parallel applications. We present a classification of the patterns according to the extent and way in which the state can be structured and accessed. We define precisely the state access patterns and discuss possible implementation schemas, performances and possibilities to manage adaptivity (parallelism degree) in the patterns. We present experimental results relative to implementations built on to

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10

Darema-Rogers, F., G. F. Pfister, and K. So. "Memory access patterns of parallel scientific programs." ACM SIGMETRICS Performance Evaluation Review 15, no. 1 (1987): 46–58. http://dx.doi.org/10.1145/29904.29912.

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11

De Sensi, Daniele, Tiziano De Matteis, Massimo Torquati, Gabriele Mencagli, and Marco Danelutto. "Bringing Parallel Patterns Out of the Corner." ACM Transactions on Architecture and Code Optimization 14, no. 4 (2017): 1–26. http://dx.doi.org/10.1145/3132710.

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12

Paulin, Pierre G., Ali Erdem Özcan, Vincent Gagné, Bruno Lavigueur, and Olivier Benny. "Parallel programming patterns for multi-processor SoC." ACM Transactions on Embedded Computing Systems 12, no. 1s (2013): 1–25. http://dx.doi.org/10.1145/2435227.2435243.

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13

Whalen, Sean, Sophie Engle, Sean Peisert, and Matt Bishop. "Network-theoretic classification of parallel computation patterns." International Journal of High Performance Computing Applications 26, no. 2 (2012): 159–69. http://dx.doi.org/10.1177/1094342012436618.

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14

Furman, Ya A. "Parallel recognition of different classes of patterns." Pattern Recognition and Image Analysis 19, no. 3 (2009): 380–93. http://dx.doi.org/10.1134/s1054661809030031.

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15

Robison, Arch D. "Composable Parallel Patterns with Intel Cilk Plus." Computing in Science & Engineering 15, no. 2 (2013): 66–71. http://dx.doi.org/10.1109/mcse.2013.21.

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16

Roberts, Patrick D. "Stochastic recruitment in parallel fiber activity patterns." Behavioral and Brain Sciences 20, no. 2 (1997): 263–64. http://dx.doi.org/10.1017/s0140525x97431436.

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Random-excitation granule cells are likely to overwhelm spatiotemporal sequences described as “tidal waves” in Braitenberg et al.'s target article. A mechanism is proposed involving the Golgi cells to reinforce tidal waves against noise. The recurrent inhibition by the Golgi calls can recruit random excitations of granule cells in phase with sequences of mossy fiber input.

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17

Bautista, Godofredo, Mary Jacquiline Romero, Giovanni Tapang, and Vincent Ricardo Daria. "Parallel two-photon photopolymerization of microgear patterns." Optics Communications 282, no. 18 (2009): 3746–50. http://dx.doi.org/10.1016/j.optcom.2009.06.047.

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18

MacDonald, S., J. Anvik, S. Bromling, J. Schaeffer, D. Szafron, and K. Tan. "From patterns to frameworks to parallel programs." Parallel Computing 28, no. 12 (2002): 1663–83. http://dx.doi.org/10.1016/s0167-8191(02)00190-4.

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19

Stypka, Jan, Piotr Anielski, Szymon Mentel, et al. "PARALLEL PATTERNS FOR AGENT-BASED EVOLUTIONARY COMPUTING." Computer Science 17, no. 1 (2016): 83. http://dx.doi.org/10.7494/csci.2016.17.1.83.

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20

Prabhakar, Raghu, Yaqi Zhang, David Koeplinger, et al. "Plasticine: A Reconfigurable Accelerator for Parallel Patterns." IEEE Micro 38, no. 3 (2018): 20–31. http://dx.doi.org/10.1109/mm.2018.032271058.

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21

Goswami, Dhrubajyoti, Ajit Singh, and Bruno R. Preiss. "From Design Patterns to Parallel Architectural Skeletons." Journal of Parallel and Distributed Computing 62, no. 4 (2002): 669–95. http://dx.doi.org/10.1006/jpdc.2001.1809.

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22

del Rio Astorga, David, Manuel F. Dolz, Luis Miguel Sánchez, J. Daniel García, Marco Danelutto, and Massimo Torquati. "Finding parallel patterns through static analysis in C++ applications." International Journal of High Performance Computing Applications 32, no. 6 (2017): 779–88. http://dx.doi.org/10.1177/1094342017695639.

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Since the ‘free lunch’ of processor performance is over, parallelism has become the new trend in hardware and architecture design. However, parallel resources deployed in data centers are underused in many cases, given that sequential programming is still deeply rooted in current software development. To address this problem, new methodologies and techniques for parallel programming have been progressively developed. For instance, parallel frameworks, offering programming patterns, allow expressing concurrency in applications to better exploit parallel hardware. Nevertheless, a large portion o

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23

Kuang, Zhe-jun, Hang Zhou, Dong-dai Zhou, Jin-peng Zhou, and Kun Yang. "A non-group parallel frequent pattern mining algorithm based on conditional patterns." Frontiers of Information Technology & Electronic Engineering 20, no. 9 (2019): 1234–45. http://dx.doi.org/10.1631/fitee.1800467.

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24

Dumitrescu, Sorina, Gheorghe Păun, and Arto Salomaa. "Pattern Languages Versus Parallel Communicating Grammar Systems." International Journal of Foundations of Computer Science 08, no. 01 (1997): 67–80. http://dx.doi.org/10.1142/s0129054197000057.

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We compare the power of two (fairly different) recently investigated language identifying devices: patterns and parallel communicating (PC) grammar systems. The simulation of multi-patterns by context-free PC grammar systems is rather obvious, but, unexpectedly, this can be realized also by (non-centralized) PC grammar systems with right-linear components. Moreover, infinite multi-patterns forming a regular set can also be simulated by PC grammar systems with right-linear components, whereas PC grammar systems with context-free components can simulate context-free multi-patterns with context-f

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25

Gofrit, Ofer N., Ben Gofrit, Yuval Roditi, et al. "Patterns of metastases progression- The linear parallel ratio." PLOS ONE 17, no. 9 (2022): e0274942. http://dx.doi.org/10.1371/journal.pone.0274942.

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Background Linear and parallel are the two leading models of metastatic progression. In this study we propose a simple way to differentiate between them. While the linear model predicts accumulation of genetic and epigenetic alterations within the primary tumor by founder cells before spreading as waves of metastases, the parallel model suggests preclinical distribution of less advanced disseminated tumor cells with independent selection and expansion at the ectopic sites. Due to identical clonal origin and time of dispatching, linear metastases are expected to have comparable diameters in any

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26

Kong, Xianwen, Clément M. Gosselin, and Pierre-Luc Richard. "Type Synthesis of Parallel Mechanisms With Multiple Operation Modes." Journal of Mechanical Design 129, no. 6 (2006): 595–601. http://dx.doi.org/10.1115/1.2717228.

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There are usually several motion patterns having the same degrees of freedom (DOF). For example, planar motion, spherical motion, and spatial translation are motion patterns with 3 DOF. An f-DOF parallel mechanism with multiple operation modes is a parallel mechanism that can generate different motion patterns with f DOF. Up to now, no method has been proposed for the type synthesis of parallel mechanisms with multiple operation modes. This paper presents a general method for the type synthesis of parallel mechanisms with multiple operation modes. Using the proposed approach, 3-DOF parallel me

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27

Meddah, Ishak H. A., and Khaled Belkadi. "Parallel Distributed Patterns Mining Using Hadoop MapReduce Framework." International Journal of Grid and High Performance Computing 9, no. 2 (2017): 70–85. http://dx.doi.org/10.4018/ijghpc.2017040105.

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The treatment of large data is proving more difficult in different axes, but the arrival of the framework MapReduce is a solution of this problem. With it we can analyze and process vast amounts of data. It does this by distributing the computational work across a cluster of virtual servers running in a cloud or large set of machines while process mining provides an important bridge between data mining and business process analysis. The process mining techniques allow for extracting information from event logs. In general, there are two steps in process mining: correlation definition or discov

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28

Meddah, Ishak H. A., Khaled Belkadi, and Mohamed Amine Boudia. "Parallel Mining Small Patterns from Business Process Traces." International Journal of Software Science and Computational Intelligence 8, no. 1 (2016): 32–45. http://dx.doi.org/10.4018/ijssci.2016010103.

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Hadoop MapReduce has arrived to solve the problem of treatment of big data, also the parallel treatment, with this framework the authors analyze, process a large size of data. It based for distributing the work in two big steps, the map and the reduce steps in a cluster or big set of machines. They apply the MapReduce framework to solve some problems in the domain of process mining how provides a bridge between data mining and business process analysis, this technique consists to mine lot of information from the process traces; In process mining, there are two steps, correlation definition and

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29

Galatenko, V. A., N. I. Viukova, and K. A. Kostyukhin. "Using Program Patterns for Parallel Programming. Base Concepts." PROGRAMMNAYA INGENERIA 10, no. 4 (2019): 147–59. http://dx.doi.org/10.17587/prin.10.147-159.

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30

Galatenko, V. A., N. I. Viukova, and K. A. Kostyukhin. "Using Program Patterns for Parallel Programming. Practical Usage." PROGRAMMNAYA INGENERIA 10, no. 6 (2019): 243–56. http://dx.doi.org/10.17587/prin.10.243-256.

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31

Maltare, Nilesh, and Vithal N. "Applying Parallel Design Patterns on Molecular Dynamics Simulation." International Journal of Computer Applications 181, no. 50 (2019): 21–24. http://dx.doi.org/10.5120/ijca2019918674.

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32

Hegedus, Z. S., Z. Zelenka, and G. Gardner. "Interference patterns generated by a plane-parallel plate." Applied Optics 32, no. 13 (1993): 2285. http://dx.doi.org/10.1364/ao.32.002285.

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33

FISCHER, JÖRG, and SERGEI GORLATCH. "TURING UNIVERSALITY OF RECURSIVE PATTERNS FOR PARALLEL PROGRAMMING." Parallel Processing Letters 12, no. 02 (2002): 229–46. http://dx.doi.org/10.1142/s012962640200094x.

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Dijkstra's famous thesis "goto considered harmful", which paved the way for structured programming, was formally substantiated by the result of Böhm and Jacopini on the Turing universality of the three well-known basic programming constructs. We argue for a similar ideal in parallel programming — "send-receive considered harmful" — i.e. abandoning explicit send-receive statements between processors and expressing programs using a restricted set of parallel constructs. We deal with recursive patterns of parallelism, represented formally as morphisms in a suitable calculus. The aim of this paper

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34

Abdulah, Sameh, and Walid Atwa. "Robust Frequent Patterns Mining Algorithms on Parallel Systems." International Journal of Computer Applications 186, no. 36 (2024): 24–30. http://dx.doi.org/10.5120/ijca2024923878.

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35

López-Gómez, Javier, Javier Fernández Muñoz, David del Rio Astorga, Manuel F. Dolz, and J. Daniel Garcia. "Exploring stream parallel patterns in distributed MPI environments." Parallel Computing 84 (May 2019): 24–36. http://dx.doi.org/10.1016/j.parco.2019.03.004.

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36

PREDANIC, MLADEN, JAYASELVI KOLLI, PAYMAN YOUSEFZADEH, and JOSEPH PENNISI. "Disparate Blood Flow Patterns in Parallel Umbilical Arteries." Obstetrics & Gynecology 91, no. 5, Part 1 (1998): 757–60. http://dx.doi.org/10.1097/00006250-199805000-00022.

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37

Madhyastha, T. M., and D. A. Reed. "Learning to classify parallel input/output access patterns." IEEE Transactions on Parallel and Distributed Systems 13, no. 8 (2002): 802–13. http://dx.doi.org/10.1109/tpds.2002.1028437.

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38

Hoffmann, Henry, Anant Agarwal, and Srinivas Devadas. "Selecting Spatiotemporal Patterns for Development of Parallel Applications." IEEE Transactions on Parallel and Distributed Systems 23, no. 10 (2012): 1970–82. http://dx.doi.org/10.1109/tpds.2011.298.

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39

Predanic, M. "Disparate Blood Flow Patterns in Parallel Umbilical Arteries." Obstetrics & Gynecology 91, no. 5 (1998): 757–60. http://dx.doi.org/10.1016/s0029-7844(98)00059-3.

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40

Kutrib, Martin, and Jan-Thomas Löwe. "Massively parallel fault tolerant computations on syntactical patterns." Future Generation Computer Systems 18, no. 7 (2002): 905–19. http://dx.doi.org/10.1016/s0167-739x(02)00070-5.

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41

Bourgeois, Kevin, Sophie Robert, Sébastien Limet, and Victor Essayan. "Efficient Implicit Parallel Patterns for Geographic Information System." Procedia Computer Science 108 (2017): 545–54. http://dx.doi.org/10.1016/j.procs.2017.05.235.

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42

Garcia, J. Daniel, David del Rio, Marco Aldinucci, et al. "Challenging the abstraction penalty in parallel patterns libraries." Journal of Supercomputing 76, no. 7 (2019): 5139–59. http://dx.doi.org/10.1007/s11227-019-02826-5.

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43

Aldinucci, M., S. Campa, M. Danelutto, P. Kilpatrick, and M. Torquati. "Design patterns percolating to parallel programming framework implementation." International Journal of Parallel Programming 42, no. 6 (2013): 1012–31. http://dx.doi.org/10.1007/s10766-013-0273-6.

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44

Hetsroni, G., A. Mosyak, Z. Segal, and E. Pogrebnyak. "Two-phase flow patterns in parallel micro-channels." International Journal of Multiphase Flow 29, no. 3 (2003): 341–60. http://dx.doi.org/10.1016/s0301-9322(03)00002-8.

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45

Pázmándi, F., and T. Geszti. "Parallel Dynamics of Neural Networks with Correlated Patterns." Europhysics Letters (EPL) 13, no. 8 (1990): 673–78. http://dx.doi.org/10.1209/0295-5075/13/8/001.

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46

Huda, Zia Ul, Ali Jannesari, and Felix Wolf. "Using Template Matching to Infer Parallel Design Patterns." ACM Transactions on Architecture and Code Optimization 11, no. 4 (2015): 1–21. http://dx.doi.org/10.1145/2688905.

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47

Sossa, Juan Humberto. "An improved parallel algorithm for thinning digital patterns." Pattern Recognition Letters 10, no. 2 (1989): 77–80. http://dx.doi.org/10.1016/0167-8655(89)90070-6.

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48

Wolf, Felix, Bernd Mohr, Jack Dongarra, and Shirley Moore. "Automatic analysis of inefficiency patterns in parallel applications." Concurrency and Computation: Practice and Experience 19, no. 11 (2007): 1481–96. http://dx.doi.org/10.1002/cpe.1128.

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49

Leung, Carson Kai-Sang, Fan Jiang, Adam G. M. Pazdor, and Aaron M. Peddle. "Parallel social network mining for interesting ‘following’ patterns." Concurrency and Computation: Practice and Experience 28, no. 15 (2016): 3994–4012. http://dx.doi.org/10.1002/cpe.3773.

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50

Johansson, Jimmy, Camilla Forsell, Mats Lind, and Matthew Cooper. "Perceiving Patterns in Parallel Coordinates: Determining Thresholds for Identification of Relationships." Information Visualization 7, no. 2 (2008): 152–62. http://dx.doi.org/10.1057/palgrave.ivs.9500166.

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This article presents a study that investigates the ability of humans to perceive relationships (patterns) in parallel coordinates, an ability that is crucial to the use of this popular visualization technique. It introduces a visual quality metric, acceptable distortions of patterns, which establishes the level of noise that may be present in data while allowing accurate identification of patterns. This metric was used to assess perceptual performance of standard 2D parallel coordinates and multi-relational 3D parallel coordinates in two experiments. In multirelational 3D parallel coordinates

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