ARCHITECTURAL AND STRUCTURAL AND FUNCTIONAL FEATURES OF THE ORGANIZATION OF PARALLEL-HIERARCHICAL MEMORY
Leonid Timchenko
tumchenko_li@gsuite.duit.edu.uaState University of Infrastructure and Technology (Ukraine)
https://orcid.org/0000-0001-5056-5913
Natalia Kokriatska
State University of Infrastructure and Technology (Ukraine)
https://orcid.org/0000-0003-0090-3886
Volodymyr Tverdomed
State University of Infrastructure and Technology (Ukraine)
https://orcid.org/0000-0002-0695-1304
Iryna Yepifanova
Vinnytsia National Technical Unіversity (Ukraine)
https://orcid.org/0000-0002-0391-9026
Yurii Didenko
State University of Infrastructure and Technology (Ukraine)
https://orcid.org/0009-0008-1033-4238
Dmytro Zhuk
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
https://orcid.org/0000-0001-8951-5542
Maksym Kozyr
State University of Infrastructure and Technology (Ukraine)
https://orcid.org/0009-0007-2564-6552
Iryna Shakhina
Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University (Ukraine)
https://orcid.org/0000-0002-4318-6189
Abstract
Parallel hierarchical memory (PI memory) is a new type of memory that is designed to improve the performance of parallel computing systems. PI memory is composed of two blocks: a mask RAM and a tail element RAM. The mask RAM stores the masks that are used to encode the information, while the tail element RAM stores the actual information. The address block of the PI memory is responsible for generating the physical addresses of the cells where the tail elements and their masks are stored. The address block also stores the field of addresses where the array was written and associates this field of addresses with the corresponding external address used to write the array. The proposed address block structure is able to efficiently generate the physical addresses of the cells where the tail elements and their masks are stored. The address block is also able to store the field of addresses where the array was written and associate this field of addresses with the corresponding external address used to write the array. The proposed address block structure has been implemented in a prototype PI memory. The prototype PI memory has been shown to be able to achieve significant performance improvements over traditional memory architectures. The paper will present a detailed description of the PI transformation algorithm, a description of the different modes of addressing organization that can be used in PI memory, an analysis of the efficiency of parallel-hierarchical memory structures, and a discussion of the challenges and future research directions in the field of PI memory.
Keywords:
parallel hierarchical memory, PI memory, address block, mask RAM, tail element RAM, performance improvementReferences
Aboutabl A. E., Elsayed M. N.: A Novel Parallel Algorithm for Clustering Documents Based on the Hierarchical Agglomerative Approach. International Journal of Computer Science & Information Technology – IJCSIT 3(2), 2011, 152–163.
Google Scholar
Bisikalo O. et al.: Parameterization of the Stochastic Model for Evaluating Variable Small Data in the Shannon Entropy Basis. Entropy 25(2), 2023, 184.
Google Scholar
Bykov M. et al.: Neural network modelling by rank configurations. Proc. of SPIE 10808, 2018, 1080821.
Google Scholar
Kim S., Wunsch D. C.: A GPU based Parallel Hierarchical Fuzzy ART clustering. IJCNN IEEE, 2011, 2778–2782.
Google Scholar
Kohonen T.: Self Organization and Associative Memory: Third Edition. Springer-Verlag, New York, 1989.
Google Scholar
Kovtun V., Izonin I.: Study of the Operation Process of the E-Commerce Oriented Ecosystem of 5Ge Base Station, Which Supports the Functioning of Independent Virtual Network Segments. Journal of Theoretical and Applied Electronic Commerce Research 16(7), 2021, 2883–2897.
Google Scholar
Kukharchuk V. V. et al.: Features of the angular speed dynamic measurements with the use of an encoder. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska – IAPGOS 12(3), 2022, 20–26.
Google Scholar
Kukharchuk V. V. et al.: Information Conversion in Measuring Channels with Optoelectronic Sensors. Sensors 22(1), 2022, 271.
Google Scholar
Kuusilinna K. et al.: Configurable parallel memory architecture for multimedia computers, Journal of Systems Architecture 47(14–15), 2002, 1089–1115.
Google Scholar
Kvуetnyy R. et al.: Inverse correlation filters of objects features with optimized regularization for image processing. Proc. SPIE 12476, 2022, 124760Q.
Google Scholar
Li Z., Li K., Xiao D., Yang L.: An Adaptive Parallel Hierarchical Clustering Algorithm. Perrott, R., Chapman, B.M., Subhlok, J., de Mello, R.F., Yang, L.T. (eds): High Performance Computing and Communications. HPCC 2007. Lecture Notes in Computer Science 4782. Springer, Berlin, Heidelberg 2007.
Google Scholar
Nere A., Lipasti M.: Optimizing Hierarchical Algorithms for GPGPUs. Master's Project Report. University of Wisconsin Madison, 2010.
Google Scholar
Orazayeva A. et al.: Biomedical image segmentation method based on contour preparation, Proc. SPIE 12476, 2022, 1247605.
Google Scholar
Osman A. A. M.: A Multi-Level WEB Based Parallel Processing System: A Hierarchical Volunteer Computing Approach. World Academy of Science, Engineering and Technology 13, 2006, 66–71.
Google Scholar
Pavlov S. V. et al.: The use of Bayesian methods in the task of localizing the narcotic substances distribution. International Scientific and Technical Conference on Computer Sciences and Information Technologies 2, 2019, 8929835, 60–63.
Google Scholar
Rajasekaran S.: Efficient Parallel Hierarchical Clustering Algorithms. IEEE Transactions on Parallel and Distributed Systems 16(6), 2005, 497–502.
Google Scholar
Romanyuk S. A. et al.: Using lights in a volume-oriented rendering. Proc. SPIE 10445, 2017, 104450U.
Google Scholar
Rose K.: Deterministic Annealing, Clustering and Optimization. Ph.D. Thesis, California Institute of Technology, Pasadena, 1991.
Google Scholar
Sobota B.: Parallel Hierarchical Model of Visualization Computing. Journal of Information, Control and Management Systems 5(2), 2007, 345–350.
Google Scholar
Sudarshan R. Lee S. E.: A Parallel Hierarchical Solver for the Poisson Equation, May 14, 2003,.
Google Scholar
Timchenko L. et al.: New methods of network modelling using parallel-hierarchical networks for processing data and reducing erroneous calculation risk. CEUR Workshop 2805, 2020, 201–212.
Google Scholar
Timchenko L. I., Kokriatskaia N. I., Pavlov S. V., Tverdomed V.: Method of indicators forecasting of biomedical images using a parallel-hierarchical network. Proc. of SPIE 11176, 2019, 111762Q.
Google Scholar
Timchenko L. I.: A multistage parallel-hierarchic network as a model of a neuronlike computation scheme. Cybern Syst Anal. 36, 2000, 251–267.
Google Scholar
Tolegen G., Toleu A., Mamyrbayev O., Mussabayev R.: Neural Named Entity Recognition for Kazakh. Lecture Notes in Computer Science 13452, 2023, 3–15.
Google Scholar
Tymkovych M. et al: Ice crystals microscopic images segmentation based on active contours. IEEE 39th International Conference on Electronics and Nanotechnology – ELNANO 2019, 493–496 [https://doi.org/10.1109/ELNANO.2019.8783332].
Google Scholar
Vasilevskyi O. et al.: A new approach to assessing the dynamic uncertainty of measuring devices. Proc. of SPIE 10808, 2018, 108082E.
Google Scholar
Vysotska O. V., Nosov K.: An approach to determination of the criteria of harmony of biological objects. Proc. of SPIE, 10808, 2018, 108083B
Google Scholar
Wójcik W., Pavlov S., Kalimoldayev M.: Information Technology in Medical Diagnostics II. Taylor & Francis Group, CRC Press, Balkema book, London 2019.
Google Scholar
Ybytayeva G. et al.: Creating a Thesaurus "Crime-Related Web Content" Based on a Multilingual Corpus. CEUR Workshop Proceedings 3396, 2023, 77–87.
Google Scholar
Zeki S.: A Vision of the Brain. Blackwell Scientific Publications, Oxford 1993.
Google Scholar
Zhao X., Guo Y., Feng Z., Hu S.: Parallel Hierarchical Cross Entropy Optimization for On-Chip Decap Budgeting. Design Automation Conference, Anaheim, CA, USA, 2010, 843–848.
Google Scholar
Authors
Leonid Timchenkotumchenko_li@gsuite.duit.edu.ua
State University of Infrastructure and Technology Ukraine
https://orcid.org/0000-0001-5056-5913
Authors
Natalia KokriatskaState University of Infrastructure and Technology Ukraine
https://orcid.org/0000-0003-0090-3886
Authors
Volodymyr TverdomedState University of Infrastructure and Technology Ukraine
https://orcid.org/0000-0002-0695-1304
Authors
Iryna YepifanovaVinnytsia National Technical Unіversity Ukraine
https://orcid.org/0000-0002-0391-9026
Authors
Yurii DidenkoState University of Infrastructure and Technology Ukraine
https://orcid.org/0009-0008-1033-4238
Authors
Dmytro ZhukState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
https://orcid.org/0000-0001-8951-5542
Authors
Maksym KozyrState University of Infrastructure and Technology Ukraine
https://orcid.org/0009-0007-2564-6552
Authors
Iryna ShakhinaVinnytsia Mykhailo Kotsiubynskyi State Pedagogical University Ukraine
https://orcid.org/0000-0002-4318-6189
Statistics
Abstract views: 107PDF downloads: 125
Most read articles by the same author(s)
- Leonid Timchenko, Natalia Kokriatskaia, Volodymyr Tverdomed, Natalia Kalashnik, Iryna Shvarts, Vladyslav Plisenko, Dmytro Zhuk, Saule Kumargazhanova, LOCAL DIFFERENCE THRESHOLD LEARNING IN FILTERING NORMAL WHITE NOISE , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 2 (2023)
- Leonid Timchenko, Natalia Kokriatskaia, Mykhailo Rozvodiuk, Volodymyr Tverdomed, Yuri Kutaev, Saule Smailova, Vladyslav Plisenko, Liudmyla Semenova, Dmytro Zhuk, THE USE OF Q-PREPARATION FOR AMPLITUDE FILTERING OF DISCRETED IMAGE , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 4 (2022)
- Leonid Timchenko, Natalia Kokriatskaya, Volodymyr Tverdomed, Oleksandr Stetsenko, Valentina Kaplun, Oleg K. Kolesnytskyj, Oleksandr Reshetnik, Saule Smailova, Ulzhalgas Zhunissova, SEGMENTATION OF MULTIGRADATION IMAGES BASED ON SPATIAL CONNECTIVITY FEATURES , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 3 (2023)
- Leonid Timchenko, Natalia Kokriatskaia, Volodymyr Tverdomed, Anatolii Horban, Oleksandr Sobovyi, Liudmyla Pogrebniak, Nelia Burlaka, Yurii Didenko, Maksym Kozyr, Ainur Kozbakova, NEUROBIOLOGICAL PROPERTIES OF THE STRUCTURE OF THE PARALLEL-HIERARCHICAL NETWORK AND ITS USAGE FOR PATTERN RECOGNITION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 14 No. 3 (2024)
