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Relevant bibliographies by topics / Algorithm with memory

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Academic literature on the topic 'Algorithm with memory'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Algorithm with memory"

1

Meng, Zhao Fei, and Shu Dong Zhang. "Buddy Algorithm Optimization in Linux Memory Management." Applied Mechanics and Materials 423-426 (September 2013): 2746–50. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2746.

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Buddy algorithm is one of the classical algorithms for Linux Memory Management. In buddy algorithm, free memory blocks, even if they have the same size and consecutive address, they cant be combined into a larger memory space because they dont have a partnership. For these shortcomings of the algorithm, this paper proposes an improved buddy algorithm which mainly include in the main data structure adding an array and a variable respectively to record the starting address and the number of two adjacent free memory blocks which are in the linked list of last order and do not meet the partnership

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Gavhane, Bhagyashree Ashok, and Prashant Vitthalrao Kathole. "Alternative RAM Mapping Algorithm for Embedded Memory Blocks in FPGA." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 2542–49. http://dx.doi.org/10.31142/ijtsrd7172.

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Likhoded, Nikolai A. "Generalized blocked Floyd – Warshall algorithm." Journal of the Belarusian State University. Mathematics and Informatics, no. 3 (November 29, 2019): 84–92. http://dx.doi.org/10.33581/10.33581/2520-6508-2019-3-84-92.

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One of the most commonly used on practice all-pairs shortest paths algorithms on weighted graphs is Floyd – Warshall algorithm. Blocked version serves as a basis for obtaining effective parallel algorithms to be implemented on multicore central processing units, on computers with distributed memory, on graphics processing units (GPU). Increasing computation granularity in blocked versions of algorithm leads to a more efficient usage of caches and more efficient organization of parallel computations. In this paper we introduce generalization of blocked Floyd – Warshall algorithm. Computing bloc

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Dermawan, Tri Setya. "Comparison of Dijkstra dan Floyd-Warshall Algorithm to Determine the Best Route of Train." IJID (International Journal on Informatics for Development) 7, no. 2 (2019): 8. http://dx.doi.org/10.14421/ijid.2018.07202.

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Abstract - The best route is the path found based on the minimum price of a train journey using the Dijkstra and Floyd-Warshall algorithms. This study aims to find out the comparison of Dijkstra and Floyd-Warshall algorithms in finding the best path on a train trip. The results of route discovery will be displayed in a web-based application using the PHP programming language and MySQL database. The results of these two algorithms are compared using 4 parameters: time complexity, memory complexity, level of completion and level of optimization.Based on the comparison results from the implementa

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Liu, Tian, Wei Zhang, Tao Xu, and Guan Wang. "Research and Analysis of Design and Optimization of Magnetic Memory Material Cache Based on STT-MRAM." Key Engineering Materials 815 (August 2019): 28–34. http://dx.doi.org/10.4028/www.scientific.net/kem.815.28.

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This paper proposes a cache replacement algorithm based on STT-MRAM magnetic memory, which aims to make the material system based on STT-MRAM magnetic memory better used. The algorithm replaces the data blocks in the cache by considering the position of the STT-MRAM magnetic memory head and the hardware characteristics of the STT-MRAM magnetic memory. This method will be different from the traditional magnetic memory-based common cache replacement algorithm. Traditional replacement algorithms are generally designed with only the algorithm to improve the cache, and the hardware characteristics

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Likhoded, N. A., and A. A. Tolstsikau. "Locality estimation of parallel algorithm for distributed memory computers." Doklady of the National Academy of Sciences of Belarus 64, no. 6 (2020): 647–56. http://dx.doi.org/10.29235/1561-8323-2020-64-6-647-656.

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Locality is an algorithm characteristic describing a usage level of fast access memory. For example, in case of distributed memory computers we focus on memory of each computational node. To achieve the high performance of algorithm implementation one should choose the best possible locality option. Studying the parallel algorithm locality is to estimate the number and volume of data communications. In this work, we formulate and prove the statements for computers with distributed memory that allow us to estimate the asymptotic volume of data communication operations. These estimation results

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Likhoded, N. A., and A. A. Tolstsikau. "Locality estimation of parallel algorithm for distributed memory computers." Doklady of the National Academy of Sciences of Belarus 64, no. 6 (2020): 647–56. http://dx.doi.org/10.29235/1561-8323-2020-64-6-647-656.

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Locality is an algorithm characteristic describing a usage level of fast access memory. For example, in case of distributed memory computers we focus on memory of each computational node. To achieve the high performance of algorithm implementation one should choose the best possible locality option. Studying the parallel algorithm locality is to estimate the number and volume of data communications. In this work, we formulate and prove the statements for computers with distributed memory that allow us to estimate the asymptotic volume of data communication operations. These estimation results

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8

Anggreani, Desi, Aji Prasetya Wibawa, Purnawansyah Purnawansyah, and Herman Herman. "Perbandingan Efisiensi Algoritma Sorting dalam Penggunaan Bandwidth." ILKOM Jurnal Ilmiah 12, no. 2 (2020): 96–103. http://dx.doi.org/10.33096/ilkom.v12i2.538.96-103.

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The most used algorithm is the sorting algorithm. There have been many popping sorting algorithms that can be used, in this study researchers took three sorting algorithms namely Insertion Sort, Selection Sort, and Merge Sort. As for this study will analyze the comparison of execution time and memory usage by considering the number of enter data of each algorithm used. The data used in this study is ukhuwah NET network bandwidth usage data connected in the Faculty of Computer Science in the form of double data types. After implementing and analyzing in terms of execution time merge sort algori

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Asamoah, Dominic, Evans Baidoo, and Stephen Opoku Oppong. "Optimizing Memory using Knapsack Algorithm." International Journal of Modern Education and Computer Science 9, no. 5 (2017): 34–42. http://dx.doi.org/10.5815/ijmecs.2017.05.05.

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Yen, G. G., and A. N. Michel. "Unlearning algorithm in associative memory." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 43, no. 10 (1996): 723–29. http://dx.doi.org/10.1109/82.539005.

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Dissertations / Theses on the topic "Algorithm with memory"

1

Jiang, Lianjun. "Preconditioning the limited-memory BFGS algorithm." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3239445.

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Wang, David Tawei. "Modern DRAM memory systems performance analysis and scheduling algorithm /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2432.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Lax, Derek Michael. "Memory efficient indexing algorithm for physical properties in OpenMC." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97862.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 55-56).<br>OpenMC is an open source Monte Carlo code designed at MIT with a focus on parallel scalability for large nuclear reactor simulations. The target problem for OpenMC is a full core high-fidelity multi-physics coupled simulation. This encompasses not only nuclear physics, but also material science and thermohydraulics. One of the challenges associated with this problem is efficient data managemen

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Yang, Tianshu. "Electric Load Forecasting Using Long Short-term Memory Algorithm." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6027.

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Abstract Power system load forecasting refers to the study or uses a mathematical method to process past and future loads systematically, taking into account important system operating characteristics, capacity expansion decisions, natural conditions, and social impacts, to meet specific accuracy requirements. Dependence of this, determine the load value at a specific moment in the future. Improving the level of load forecasting technology is conducive to the planned power management, which is conducive to rationally arranging the grid operation mode and unit maintenance plan, and is conducive

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Upadhyay, Abhyudaya. "Big Vector: An External Memory Algorithm and Data Structure." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279714.

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Ansari, Zafar A. "Limited Memory Space Dilation and Reduction Algorithms." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/9569.

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In this thesis, we present variants of Shor and Zhurbenko's r-algorithm, motivated by the memoryless and limited memory updates for differentiable quasi-Newton methods. This well known r-algorithm, which employs a space dilation strategy in the direction of the difference between two successive subgradients, is recognized as being one of the most effective procedures for solving nondifferentiable optimization problems. However, the method needs to store the space dilation matrix and update it at every iteration, resulting in a substantial computational burden for large-sized problems. To circu

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Cossentine, Tyler Andrew. "An efficient external sorting algorithm for flash memory embedded devices." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/40208.

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Many embedded systems applications involve storing and querying large datasets. Existing research in this area has focused on adapting and applying conventional database algorithms to embedded devices. Algorithms designed for processing queries on embedded devices must be able to execute given the small amount of available memory and energy constraints. Sorting is a fundamental algorithm used frequently in databases. Flash memory has unique performance characteristics. Page writes to flash memory are much more expensive than reads. In addition, random reads from flash memory can be performed a

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MARTIN, ROBERT ROHAN. "MULTI-LEVEL CELL FLASH MEMORY FAULT TESTING AND DIAGNOSIS." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1120232606.

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Saadane, Sofiane. "Algorithmes stochastiques pour l'apprentissage, l'optimisation et l'approximation du régime stationnaire." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30203/document.

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Dans cette thèse, nous étudions des thématiques autour des algorithmes stochastiques et c'est pour cette raison que nous débuterons ce manuscrit par des éléments généraux sur ces algorithmes en donnant des résultats historiques pour poser les bases de nos travaux. Ensuite, nous étudierons un algorithme de bandit issu des travaux de N arendra et Shapiro dont l'objectif est de déterminer parmi un choix de plusieurs sources laquelle profite le plus à l'utilisateur en évitant toutefois de passer trop de temps à tester celles qui sont moins performantes. Notre but est dans un premier temps de comp

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Silva, Silvio Ferreira da. "Como chegamos até aqui: a migração da memória e os instrumentos de controle." Pontifícia Universidade Católica de São Paulo, 2016. https://tede2.pucsp.br/handle/handle/19493.

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Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2016-12-02T12:38:40Z No. of bitstreams: 1 Silvio Ferreira da Silva.pdf: 1559404 bytes, checksum: 53cabe18964143039fcfc890e6dd0199 (MD5)<br>Made available in DSpace on 2016-12-02T12:38:40Z (GMT). No. of bitstreams: 1 Silvio Ferreira da Silva.pdf: 1559404 bytes, checksum: 53cabe18964143039fcfc890e6dd0199 (MD5) Previous issue date: 2016-10-26<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>This paper provides an overview of the relationship between man and machine, specifically the computer. It is a reflexive work, addr

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Books on the topic "Algorithm with memory"

1

Park, Tae-Soon. The clock algorithm for shared-memory multiprocessors. University of Toronto, Dept. of Computer Science, 1994.

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2

Rogers, David. Kanerva's sparse distributed memory: An associative memory algorithm well-suited to the connection machine. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.

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3

Aydinian, Harout. Information Theory, Combinatorics, and Search Theory: In Memory of Rudolf Ahlswede. Springer Berlin Heidelberg, 2013.

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Meyer, Ulrich, Peter Sanders, and Jop Sibeyn, eds. Algorithms for Memory Hierarchies. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36574-5.

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5

Algorithms and data structures for external memory. Published, sold, and distributed by now Publishers, 2008.

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Guerraoui, Rachid, and Paolo Romano, eds. Transactional Memory. Foundations, Algorithms, Tools, and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14720-8.

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7

Nicol, David. Massively parallel algorithms for trace-driven cache simulations. National Aeronautics and Space Administration, Langley Research Center, 1991.

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8

Rafael, Lins, ed. Garbage collection: Algorithms for automatic dynamic memory management. Wiley, 1996.

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9

M, Abello James, and Vitter Jeffrey Scott 1955-, eds. External memory algorithms: DIMACS Workshop External Memory and Visualization, May 20-22, 1998. American Mathematical Society, 1999.

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Feygin, G. Survivor sequence memory management in Viterbi Decoders. Computer Systems Research Institute, University of Toronto, 1991.

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Book chapters on the topic "Algorithm with memory"

1

Bader, David A., Ajith K. Illendula, Bernard M. E. Moret, and Nina R. Weisse-Bernstein. "Using PRAM Algorithms on a Uniform-Memory-Access Shared-Memory Architecture." In Algorithm Engineering. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44688-5_11.

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Edelkamp, Stefan. "External-Memory State Space Search." In Algorithm Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49487-6_6.

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3

Crauser, Andreas, and Kurt Mehlhorn. "LEDA-SM: Extending LEDA to Secondary Memory." In Algorithm Engineering. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48318-7_19.

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Rahman, Naila, Richard Cole, and Rajeev Raman. "Optimised Predecessor Data Structures for Internal Memory." In Algorithm Engineering. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44688-5_6.

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Juurlink, Ben H. H., and Harry A. G. Wijshoff. "The parallel hierarchical memory model." In Algorithm Theory — SWAT '94. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58218-5_22.

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Freivalds, Rūsinš, and Carl H. Smith. "Memory limited inductive inference machines." In Algorithm Theory — SWAT '92. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55706-7_2.

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Brengel, Klaus, Andreas Crauser, Paolo Ferragina, and Ulrich Meyer. "An Experimental Study of Priority Queues in External Memory." In Algorithm Engineering. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48318-7_27.

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Vasilache, Marcel. "Algorithm Optimizations: Low Memory Footprint." In Advances in Pattern Recognition. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5_11.

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Sanders, Peter. "Fast Priority Queues for Cached Memory." In Algorithm Engineering and Experimentation. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48518-x_19.

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Bojesen, Jesper, and Jyrki Katajainen. "Interchanging Tw Segments of an Array in a Hierarchical Memory System." In Algorithm Engineering. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44691-5_14.

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Conference papers on the topic "Algorithm with memory"

1

Li, Jian, Wei Yu, and Bao-Ping Yan. "Memory effect in DBSCAN algorithm." In Education (ICCSE). IEEE, 2009. http://dx.doi.org/10.1109/iccse.2009.5228532.

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Kuznetsov, Igor, and Andrey Filchenkov. "Solving Continuous Control with Episodic Memory." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/365.

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Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance. Previous works on memory mechanisms show benefits of using episodic-based data structures for discrete action problems in terms of sample-efficiency. The application of episodic memory for continuous control with a large action space is not trivial. Our study aims to answer the question: can episodic memory be used to improve agent's performance in continuous control? Our proposed algorithm combines episodic memory with Actor-Critic architecture by modify

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Oluwatope, Ayodeji, Bamidele Ojo, G. Adesola Aderounmu, and Matthew Adigun. "A Memory Optimized Public-Key Crypto Algorithm Using Modified Modular Exponentiation (MME)." In InSITE 2006: Informing Science + IT Education Conference. Informing Science Institute, 2006. http://dx.doi.org/10.28945/3032.

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Since the advent of data communication over networks, it has become imperative to ensure security of information. Cryptography is a technique that is being employed. This paper takes a look at an important aspect of the public key encryption scheme, the modular exponentiation technique, with the view of optimizing it. Taking a look at some public key encryption schemes, it would be observed that the modular exponentiation process is primal to achieving high speed algorithms in data encryption. With special emphasis on the Montgomery exponentiation algorithm, a blend of this algorithm with the

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Abdelkafi, Omar, Lhassane Idoumghar, Julien Lepagnot, and Jean-Louis Paillaud. "MEmory Genetic Algorithm Hybridized for Zeolites." In 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017. http://dx.doi.org/10.1109/cec.2017.7969318.

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Sattar, Naw Safrin, and Shaikh Arifuzzaman. "Parallelizing Louvain Algorithm: Distributed Memory Challenges." In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018. http://dx.doi.org/10.1109/dasc/picom/datacom/cyberscitec.2018.00122.

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Zhu Wenzhong. "Dispatch algorithm of flash memory system." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5688974.

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Goel, S., M. Shaaban, T. Darwish, H. Mahmoud, and M. Bayoumi. "Memory accesses reduction for MIME algorithm." In 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698). IEEE, 2003. http://dx.doi.org/10.1109/icme.2003.1221739.

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Tang, Rui, Simon Fong, Xin-She Yang, and Suash Deb. "Wolf search algorithm with ephemeral memory." In 2012 Seventh International Conference on Digital Information Management (ICDIM). IEEE, 2012. http://dx.doi.org/10.1109/icdim.2012.6360147.

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Chu, Yu-Jung, and Thinh Nguyen. "Memory Decoding Algorithm for FSO Transmission." In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020. http://dx.doi.org/10.1109/vtc2020-spring48590.2020.9128944.

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Kärkkäinen, Juha, Dominik Kempa, Simon J. Puglisi, and Bella Zhukova. "Engineering External Memory Induced Suffix Sorting." In 2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX). Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974768.8.

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Reports on the topic "Algorithm with memory"

1

Cypher, Robert. Valiant's Maximum Algorithm with Sequential Memory Accesses. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203713.

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Byrd, R. H., L. Peihuang, and J. Nocedal. A limited-memory algorithm for bound-constrained optimization. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/204262.

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Becker, R., J. Stolken, C. Jannetti, and J. Bassani. An Implicit Algorithm for the Numerical Simulation of Shape-Memory Alloys. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15013637.

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D'Azevedo, E. F., and C. H. Romine. Reducing communication costs in the conjugate gradient algorithm on distributed memory multiprocessors. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7172467.

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D`Azevedo, E. F., and C. H. Romine. Reducing communication costs in the conjugate gradient algorithm on distributed memory multiprocessors. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10176473.

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Price, Ryan. Hierarchical Temporal Memory Cortical Learning Algorithm for Pattern Recognition on Multi-core Architectures. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.202.

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Fujimoto, Richard M., and Hwa-Chung Feng. A Shared Memory Algorithm and Proof for the Generalized Alternative Construct in CSP (Communicating Sequential Processes). Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada203009.

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Pothen, A. Sparse matrix algorithms on distributed memory multiprocessors. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7205554.

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Vitter, Jeffrey S. External Memory Algorithms: Dealing With Massive Data. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440839.

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Vitter, Jeffrey S. External Memory Algorithms: Dealing With MASSIVE Data. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada415374.

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