Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Architektoniczne, strukturalne i funkcjonalne cechy równoległo-hierarchicznej organizacji pamięci
Języki publikacji
Abstrakty
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 parallelhierarchical memory structures, and a discussion of the challenges and future research directions in the field of PI memory.
Równoległa pamięć hierarchiczna (pamięć PI) jest nowym typem pamięci zaprojektowanym w celu poprawy wydajności równoległych systemów obliczeniowych. Pamięć PI składa się z dwóch bloków: maski RAM i ogon RAM. Maska RAM przechowuje maski używane do kodowania informacji, podczas gdy ogon RAM przechowuje rzeczywiste informacje. Blok adresowy pamięci PI jest odpowiedzialny za generowanie fizycznych adresów komórek, w których przechowywane są elementy końcowe i ich maski. Blok adresowy przechowuje również pole adresu, w którym tablica została zapisana i kojarzy to pole adresu z odpowiednim adresem zewnętrznym użytym do zapisu tablicy. Proponowana struktura bloku adresowego jest w stanie efektywnie generować fizyczne adresy komórek, w których przechowywane są elementy ogonowe i ich maski. Blok adresowy może również przechowywać pole adresu, w którym tablica została zapisana i powiązać to pole adresu z odpowiednim adresem zewnętrznym użytym do zapisu tablicy. Zaproponowana struktura bloku adresowego została zaimplementowana w prototypie pamięci PI. Wykazano, że prototyp pamięci PI jest w stanie znacznie poprawić wydajność w porównaniu z tradycyjnymi architekturami pamięci. W artykule zostanie przedstawiony szczegółowy opis algorytmu konwersji PI, opis różnych trybów adresowania, które mogą być używane w pamięci PI, analiza wydajności równoległo-hierarchicznych struktur pamięci oraz omówienie wyzwań i przyszłych kierunków badań w dziedzinie pamięci PI.
Rocznik
Tom
Strony
46--52
Opis fizyczny
Bibliogr. 31 poz., rys.
Twórcy
autor
- State University of Infrastructure and Technology, Kyiv, Ukraine
autor
- State University of Infrastructure and Technology, Kyiv, Ukraine
autor
- State University of Infrastructure and Technology, Kyiv, Ukraine
autor
- Vinnytsia National Technical Unіversity, Vinnytsia, Ukraine
autor
- State University of Infrastructure and Technology, Kyiv, Ukraine
autor
- State University of Infrastructure and Technology, Kyiv, Ukraine
autor
- State University of Infrastructure and Technology, Kyiv, Ukraine
autor
- Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University, Vinnytsia, Ukraine
Bibliografia
- [1] 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.
- [2] Bisikalo O. et al.: Parameterization of the Stochastic Model for Evaluating Variable Small Data in the Shannon Entropy Basis. Entropy 25(2), 2023, 184.
- [3] Bykov M. et al.: Neural network modelling by rank configurations. Proc. of SPIE 10808, 2018, 1080821.
- [4] Kim S., Wunsch D. C.: A GPU based Parallel Hierarchical Fuzzy ART clustering. IJCNN IEEE, 2011, 2778–2782.
- [5] Kohonen T.: Self Organization and Associative Memory: Third Edition. Springer-Verlag, New York, 1989.
- [6] 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.
- [7] 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.
- [8] Kukharchuk V. V. et al.: Information Conversion in Measuring Channels with Optoelectronic Sensors. Sensors 22(1), 2022, 271.
- [9] Kuusilinna K. et al.: Configurable parallel memory architecture for multimedia computers, Journal of Systems Architecture 47(14–15), 2002, 1089–1115.
- [10] Kvуetnyy R. et al.: Inverse correlation filters of objects features with optimized regularization for image processing. Proc. SPIE 12476, 2022, 124760Q.
- [11] 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.
- [12] Nere A., Lipasti M.: Optimizing Hierarchical Algorithms for GPGPUs. Master's Project Report. University of Wisconsin Madison, 2010.
- [13] Orazayeva A. et al.: Biomedical image segmentation method based on contour preparation, Proc. SPIE 12476, 2022, 1247605.
- [14] 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.
- [15] 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.
- [16] Rajasekaran S.: Efficient Parallel Hierarchical Clustering Algorithms. IEEE Transactions on Parallel and Distributed Systems 16(6), 2005, 497–502.
- [17] Romanyuk S. A. et al.: Using lights in a volume-oriented rendering. Proc. SPIE 10445, 2017, 104450U.
- [18] Rose K.: Deterministic Annealing, Clustering and Optimization. Ph.D. Thesis, California Institute of Technology, Pasadena, 1991.
- [19] Sobota B.: Parallel Hierarchical Model of Visualization Computing. Journal of Information, Control and Management Systems 5(2), 2007, 345–350.
- [20] Sudarshan R. Lee S. E.: A Parallel Hierarchical Solver for the Poisson Equation, May 14, 2003,.
- [21] Timchenko L. et al.: New methods of network modelling using parallelhierarchical networks for processing data and reducing erroneous calculation risk. CEUR Workshop 2805, 2020, 201–212.
- [22] 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.
- [23] Timchenko L. I.: A multistage parallel-hierarchic network as a model of a neuronlike computation scheme. Cybern Syst Anal. 36, 2000, 251–267.
- [24] Tolegen G., Toleu A., Mamyrbayev O., Mussabayev R.: Neural Named Entity Recognition for Kazakh. Lecture Notes in Computer Science 13452, 2023, 3–15.
- [25] 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].
- [26] Vasilevskyi O. et al.: A new approach to assessing the dynamic uncertainty of measuring devices. Proc. of SPIE 10808, 2018, 108082E.
- [27] Vysotska O. V., Nosov K.: An approach to determination of the criteria of harmony of biological objects. Proc. of SPIE, 10808, 2018, 108083B
- [28] Wójcik W., Pavlov S., Kalimoldayev M.: Information Technology in Medical Diagnostics II. Taylor & Francis Group, CRC Press, Balkema book, London 2019.
- [29] Ybytayeva G. et al.: Creating a Thesaurus "Crime-Related Web Content" Based on a Multilingual Corpus. CEUR Workshop Proceedings 3396, 2023, 77–87.
- [30] Zeki S.: A Vision of the Brain. Blackwell Scientific Publications, Oxford 1993.
- [31] 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-38de1fcc-abf6-4b85-8552-fbf259ce5cd5