Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 299-304.doi: 10.11896/JsJkx.190700047

• Computer Network • Previous Articles     Next Articles

Model of Cartesian Product of Modulo p Residual Class Addition Group for Interconnection Networks

SHI Teng1 and SHI Hai-zhong2   

  1. 1 School of Electronic and Information Engineering,Lanzhou City University,Lanzhou 730070,China
    2 College of Mathematics and Statistics,Northwest Normal University,Lanzhou 730070,China
  • Published:2020-07-07
  • About author:SHI Teng, born in 2000.His main research interests include network science and language.
    SHI Hai-zhong, born in 1962, Ph.D, professor.His main research interests include interconnection network, graph semigroup, (V, R)-semigroup, undirec-ted graph language, digraph language, random graph language, (V, R)-language, network science and language.

Abstract: Many applications require high computational density of the system,the computational density here refers to the computational power of a system in a certain volume or area.This is why a large number of distributed computing such as grid computing and cloud computing cannot completely replace supercomputing.Supercomputers are also widely used in emerging fields.Academician Chen Zuoning pointed out that the United States is developing an exascale supercomputer with a new advanced architecture (probably not a classical one),and China is also actively developing its own exascale supercomputer.Interconnection network is an important part of supercomputer architecture.Academician Chen pointed out that interconnection network is deci-sive to the performance-price ratio of the system.In this paper,a Cartesian product of modulo p residual class addition groups model for interconnection networks was designed,which can be used to characterize well-known interconnection networks such as hypercube and folded hypercube.More importantly,many new interconnection networks have been designed using this model.These new interconnection networks have their own characteristics and greatly enrich the seed bank of interconnection networks.

Key words: Cartesian product, Exascale supercomputer, Folded hypercube, Hypercube, Interconnection network, Model, Modulo p residual class addition group

CLC Number: 

  • TP393
[1] SI H W,FENG L S.Semour Cray:The Father of Supercomputer.Journal of Dialectics of Nature,2018,40(7):127-133.
[2] CHEN Z N.Supercomputers Entering a New Era.Democracy & Science,2017,167(4):24-25.
[3] WANG D X,CHEN G L.Analysis of Interconnection Network Structure.BeiJing:Science Press,1990.
[4] XU J M.A First Course in Graph Theory.BeiJing:Science Press,2015.
[5] AKERS S B,KRISHNAMURTHY B.A Group-Theoretic Model for Symmetric Interconnection Networks.IEEE Transactions on Computers,1989,38(4):555-565.
[6] LEIGHTON F T.Complexity Issues in VLSI:Optimal Layouts for Shuffle-exchange graphs and other networks//Cambridge,MA:MIT Press.1983:76-93.
[7] PEASE M C.The Indirect Binary n-cube Microprocessor Array.IEEE Transactions on Computers,1977,C-26:458-473.
[8] EI-AMAWY A,LATIFI S.Properties and Performance of Folded Hypercubes.IEEE Transactions on Parallel and Distributed Systems,1991,2(3):31-42.
[9] LAKSHMIVARAHAN S,JWO J S,DHALL S K.Symmetry in Interconnection Networks Based on Cayley graphs of permutation groups:A Survey.Parallel Computing,1993,19(4):361-407.
[10] XU J M.Combinatorial Theory in Networks.BeiJing:Science Press,2007.
[11] XU J M.Combinatorial Theory in Networks.BeiJing:Science Press,2013.
[12] SHI H Z.Some New Cartesian Product interconnection Networks.Computer Science,2013,40(6A):265-270,306.
[13] SHI H Z,LU J B.On ConJectures of Interconnection Networks.Computer Engineering and Applications,2008,44(31):112-115.
[14] SHI H Z.Regular Graph Connected Cycle:A Unity Model of Many Interconnection Networks//Proceedings of the Tenth National Conference of Operations Research Society of China.BeiJing,2010:202-208.
[15] SHI H Z,SHI Y.A New Model for Interconnection Networks:k-hierarchical Ring and r-layer graph networks.http://vdisk.weibo.com/s/dlizJyferZ-ZI.
[16] SHI H Z,SHI Y.A Hierarchical Ring Group-theoretic Model for Interconnection Networks.http://vdisk.weibo.com/s/dlizJyfeBX-2J.
[17] SHI H Z,SHI Y.Cell-breading graph Model for Interconnection Networks.http://vdisk.weibo.com/s/dlizJyfesb05y.
[18] SHI H Z.New Model for Interconnection Networks:Multipartite Group-theoretic Model.Computer Science,2013,40(9):21-24.
[19] SHI H Z.A Ring-theoretic Model-for Interconnection Network.BeiJing:Doctor Thesis,Institute of Applied Mathematics,Academia Sinica,1998.
[20] SHI H Z,NIU P F,MA J Y,et al.A Vector Graph Model for Interconnection Networks.Operations Research Transactions,2011,15(3):115-123.
[21] SHI H Z,SHI Y.M-layers Binary Graph Model for Interconnection Networks.Computer Science,2017,44(Z2):308-311.
[22] LIU X,GUO H,SUN R J,et al.The Characteristic Analysis and Exascale Scalability Research of Large Scale Parallel Applications on Sunway TaihuLight Supercomputer.Chinese Journal of Computers,2018,41(10):2209-2220.
[23] DU D Z,HSU F,HWANG F K.Hamiltonian Property of d-consecutive Digraphs.Mathematical and Computing Modeling,1993,17(11):61-63.
[1] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2] WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220.
[3] HU Yu-jiao, JIA Qing-min, SUN Qing-shuang, XIE Ren-chao, HUANG Tao. Functional Architecture to Intelligent Computing Power Network [J]. Computer Science, 2022, 49(9): 249-259.
[4] WANG Zi-kai, ZHU Jian, ZHANG Bo-jun, HU Kai. Research and Implementation of Parallel Method in Blockchain and Smart Contract [J]. Computer Science, 2022, 49(9): 312-317.
[5] DOU Jia-wei. Privacy-preserving Hamming and Edit Distance Computation and Applications [J]. Computer Science, 2022, 49(9): 355-360.
[6] YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63.
[7] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[8] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[9] LI Tang, QIN Xiao-lin, CHI He-yu, FEI Ke. Secure Coordination Model for Multiple Unmanned Systems [J]. Computer Science, 2022, 49(7): 332-339.
[10] WANG Wen-qiang, JIA Xing-xing, LI Peng. Adaptive Ensemble Ordering Algorithm [J]. Computer Science, 2022, 49(6A): 242-246.
[11] DU Hong-yi, YANG Hua, LIU Yan-hong, YANG Hong-peng. Nonlinear Dynamics Information Dissemination Model Based on Network Media [J]. Computer Science, 2022, 49(6A): 280-284.
[12] XIE Bai-lin, LI Qi, KUANG Jiang. Microblog Popular Information Detection Based on Hidden Semi-Markov Model [J]. Computer Science, 2022, 49(6A): 291-296.
[13] WANG Jun-feng, LIU Fan, YANG Sai, LYU Tan-yue, CHEN Zhi-yu, XU Feng. Dam Crack Detection Based on Multi-source Transfer Learning [J]. Computer Science, 2022, 49(6A): 319-324.
[14] LI Sun, CAO Feng. Analysis and Trend Research of End-to-End Framework Model of Intelligent Speech Technology [J]. Computer Science, 2022, 49(6A): 331-336.
[15] CHU Yu-chun, GONG Hang, Wang Xue-fang, LIU Pei-shun. Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4 [J]. Computer Science, 2022, 49(6A): 337-344.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!