Algorithms for high-performance recurrent affine data and signal processing and a method of improving its input-output locality

被引:0
作者
Chusov, Andrey A. [1 ]
Chusova, Alina E. [1 ]
Smadych, Nikita S. [1 ]
机构
[1] Far Eastern Fed Univ, Vladivostok, Russia
来源
MARINE INTELLECTUAL TECHNOLOGIES | 2023年 / 04期
关键词
high-performance data processing; digital signal processing; parallel algorithms; SPMD-parallelism; affine transformation; data locality; cryptography;
D O I
10.37220/MIT.2023.62.4.029
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
The paper proposes a new method for customizing patterns of input-output access to data in order to improve its locality in implementations of high-performance signal and data processing based on recurrent affine transformation. This method is implemented in a proposed parallel algorithm with improved data locality which, together with alternative methods of parallelization, is formally described and presented in the paper. The formalism is expressed in terms of abstract algebraic structures and as such the method is applicable to a wide range of data and signal processing problems which are based on the recurrent affine transform. The applicability of the proposed algorithm is derived from its "single program-multiple data" class of parallel problems and implementing architectures. The latter are widespread due to relative simplicity of their implementation compared to other classes which require separate management of parallel threads of execution. These architectures include, for example, vector, signal and graphical multiprocessors. Use of the proposed method and algorithm, as an example, is specified in the paper for the problems of parallel generation of pseudorandom numbers, parallel implementation of linear and affine feedback registers as well as a parallel implementation of affine block and stream ciphers based on the recurrent affine transform which are employed in modern algorithms of confidential image encryption.
引用
收藏
页码:242 / 250
页数:9
相关论文
共 24 条
  • [1] [Anonymous], 2018, GOST 34.12-2018
  • [2] Artemeva I. L., 2023, VYCHISLITELNYE TEHNO, P22
  • [3] Accelerating Multivariate Cryptography with Constructive Affine Stream Transformations
    Carenzo, Michael
    Polak, Monika
    [J]. PROCEEDINGS OF THE 2019 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2019, : 221 - 225
  • [4] Cid C., 2021, Tech. Rep. 2021/1205
  • [5] Dworkin M., 2001, Advanced encryption standard (AES), DOI DOI 10.6028/NIST.FIPS.197
  • [6] Efficient Algorithm for Finding Roots of Error-Locator Polynomials
    Fedorenko, Sergei Valentinovich
    [J]. IEEE ACCESS, 2021, 9 : 38673 - 38686
  • [7] Ferreira M, 2018, Ph.D. Dissertation
  • [8] A Parallel and Reconfigurable United Architecture for Fibonacci and Galois LFSR
    Li, Wei
    Yang, Xuan
    [J]. 2015 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS IHMSC 2015, VOL I, 2015, : 203 - 206
  • [9] RGB image encryption based on symmetric keys using Arnold transform, 3D chaotic map and affine hill cipher
    Lone, Manzoor Ahmad
    Qureshi, Shaima
    [J]. OPTIK, 2022, 260
  • [10] Lorenzon AF, 2019, SPRINGERBRIEF COMPUT, P1, DOI 10.1007/978-3-030-28719-1