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A novel parallel image encryption algorithm based on chaos

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 1211-1223页

作者： Cavusoglu, Unal Kacar, Sezgin Sakarya Univ Dept Comp Engn TR-54187 Serdivan Sakarya Turkey Sakarya Univ Dept Elect & Elect Engn TR-54187 Serdivan Sakarya Turkey

With rapid technology development, data sizes are increasing and more powerful hardware structures are needed to process these data. An alternative solution for this process is to parallel the transactions to be realized, to provide the time gain, increase the efficiency and reduce the transaction costs. In this article, a new chaos-based parallel encryption algorithm design is presented to meet this requirements. In the study, a new random number generator (RNG) and chaos-based parallel encryption algorithm are developed. Using developed RNG, confusion and encryption of pixel in images have been realized. Also parallel computing has been used to increase security and speedup the encryption. With the new parallel encryption algorithm, the security and performance tests of the image encryption application and the encryption process have been made. In addition, the evaluated performance of the parallel computing is described in speedup and efficiency.

关键词： Image encryption parallel algorithm Chaos RNG

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parallel transfer evolution algorithm

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APPLIED SOFT COMPUTING 2019年 75卷 686-701页

作者： Laili, Yuanjun Zhang, Lin Li, Yun Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Dongguan Univ Technol Sch Comp Sci & Network Secur Dongguan 523828 Peoples R China Univ Strathclyde Fac Engn Glasgow G1 1XJ Lanark Scotland

parallelization of an evolutionary algorithm takes the advantage of modular population division and information exchange among multiple processors. However, existing parallel evolutionary algorithms are rather ad hoc and lack a capability of adapting to diverse problems. To accommodate a wider range of problems and to reduce algorithm design costs, this paper develops a parallel transfer evolution algorithm. It is based on the island-model of parallel evolutionary algorithm and, for improving performance, transfers both the connections and the evolutionary operators from one sub-population pair to another adaptively. Needing no extra upper selection strategy, each sub-population is able to select autonomously evolutionary operators and local search operators as subroutines according to both the sub-population's own and the connected neighbor's ranking boards. The parallel transfer evolution is tested on two typical combinatorial optimization problems in comparison with six existing ad-hoc evolutionary algorithms, and is also applied to a real-world case study in comparison with five typical parallel evolutionary algorithms. The tests show that the proposed scheme and the resultant PEA offer high flexibility in dealing with a wider range of combinatorial optimization problems without algorithmic modification or redesign. Both the topological transfer and the algorithmic transfer are seen applicable not only to combinatorial optimization problems, but also to non-permutated complex problems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Evolutionary computation parallel algorithm Topological design algorithmic adaptation

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A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU

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JOURNAL OF SUPERCOMPUTING 2019年第2期75卷 732-745页

作者： Jiang, Yu Zhao, Minghao Hu, Chengquan He, Lili Bai, Hongtao Wang, Jin Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Changchun Jilin Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Key Lab Broadband Wireless Commun & Sensor Networ Nanjing Jiangsu Peoples R China

According to the complexity of ocean data, this paper adopts a parallel mining algorithm of association rules to explore the correlation and regularity of oxygen, temperature, phosphate, nitrate and silicate in the ocean. After the marine data is interpolated, this paper utilizes the parallel FP-growth algorithm to mine the data and then briefly analyzes the mining results of the frequent itemsets and association rules. The relationship between the parallel efficiency and the core number of CPU is analyzed through datasets with different scales. The experimental results indicate that the acceleration effect is ideal when each thread scored 200,000-300,000 data, which leads to more than 1.2 times of performance improvement.

关键词： Association rules mining FP-growth WOA13 parallel algorithm

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TBGMax: leveraging two-boundary graph pattern for lossless maximum-flow acceleration

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OPTIMIZATION 2022年第7期71卷 2047-2072页

作者： Wei, Wei Mu, Yashuang Yang, Weidong Xu, Heyang Henan Univ Technol Minist Educ Key Lab Grain Informat Proc & Control Zhengzhou Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Peoples R China

The maximum-flow (max-flow) problem is a classic combinatorial optimization problem that has been used in many kinds of *** existing methods accelerate by contracting large-size subgraphs, but can only obtain approximated results with significant deviations. To address the problem, we propose a two-boundary graph pattern-based contraction algorithm for lossless max-flow acceleration (TBGMax). TBGMax can obtain accurate results by contracting two-boundary graphs of any size into edges, only involves connectivity information and does not need any extra information such as the edge capacity and local topology. TBGMax can accelerate even further by excluding irrelevant nodes. Random and real graphs-based simulations show that TBGMax can accelerate classic max-flow algorithm by up to 75.1 times in benchmark problem families and up to 22.3 times in real-world road networks, and at best only involve an average of 0.002% of the nodes in a graph.

关键词： Boundary node maximum flow graph contraction overlay parallel algorithm

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Improved algorithm for parallel mining collaborative frequent itemsets in multiple data streams

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第3-Sup期22卷 S6133-S6141页

作者： Liu, Fang'ai Wang, Qianqian Wang, Xin Shandong Normal Univ Sch Informat Sci & Engn 88 East Wenhua Rd Jinan 250014 Shandong Peoples R China

With the rapid development of the World Wide Web technology, complex and diverse data present explosive growth, so frequent itemset mining plays an essential role. In view of the mining frequent itemsets in multiple data streams by limited computing power of a single processor, an improved algorithm of parallel Mining Collaborative frequent itemsets in multiple data streams (PMCMD-Stream) was proposed. Firstly, the algorithm compresses the potential and frequent itemsets into CP-Tree only by one-scan and applies increment method to inserting or deleting related branch on CP-Tree, we do not need to repeatedly scanning the databases to generate many candidate frequent itemsets and save the running time. Secondly, this parallelized algorithm can be run in the MapReduce programming environment. Finally, the valuable frequent itemsets, namely global collaborative frequent itemsets, were obtained. Because each candidate frequent itemset is independent, and different candidate frequent itemsets can be processed by multiple computing machines concurrently. The experimental results show that PMCMD-Stream algorithm not only can improve the mining efficiency but also have much better scalability than the existing algorithms, so as to discover the collaborative frequent itemsets from large-scale data streams.

关键词： Stream data mining Multiple data streams parallel algorithm Sliding window Frequent itemsets Collaborative frequent itemsets

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Maximum Flow on Highly Dynamic Graphs

Maximum Flow on Highly Dynamic Graphs

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Luo, Juntong Sallinen, Scott Ripeanu, Matei The University of British Columbia Vancouver Canada

ISBN: (纸本)9798350324457

Recent advances in dynamic graph processing have enabled the analysis of highly dynamic graphs with change at rates as high as millions of edge changes per second. Solutions in this domain, however, have been demonstrated only for relatively simple algorithms like PageRank, breadth-first search, and connected components. Expanding beyond this, we explore the maximum flow problem, a fundamental, yet more complex problem, in graph analytics. We propose a novel, distributed algorithm for max-flow on dynamic graphs, and implement it on top of an asynchronous vertex-centric abstraction. We show that our algorithm can process both additions and deletions of vertices and edges efficiently at scale on fast-evolving graphs, and provide a comprehensive analysis by evaluating, in addition to throughput, two criteria that are important when applied to real-world problems: result latency and solution stability. © 2023 IEEE.

关键词： asynchronous algorithm dynamic graph processing graph streaming maximum flow parallel algorithm

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Analysis of parallel finite element algorithm based on three linearization methods for the steady incompressible MHD flow

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2019年第1期78卷 35-54页

作者： Tang, Qili Huang, Yunqing Xiangtan Univ Hunan Key Lab Computat & Simulat Sci & Engn Key Lab Intelligent Comp & Informat Proc Minist EducSch Math & Computat Sci Xiangtan 411105 Peoples R China

Based on full domain partition, parallel finite element algorithm based on three linearizations is designed to solve the stationary incompressible magnetohydrodynamics (MHD). In every algorithm, each processor is in charge of a subproblem which is linearized by three options: Stokes-type, Newton's and Picard's iterations. Each subproblem is defined in the whole domain with vast majority of the degrees of freedom associated with the particular subdomain. The error estimates of these proposed algorithms with respect to iterative step and small mesh sizes are analyzed. Finally, some numerical experiments are provided to show the validity and high efficiency of our algorithms. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Full domain partition parallel algorithm Finite element Linearization Stationary incompressible magnetohydrodynamics

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Design and implementation of fast and hardware-efficient parallel processing elements to set full and partial permutations in Benes networks

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JOURNAL OF ENGINEERING-JOE 2021年第6期2021卷 312-320页

作者： Koloko, Labson Matsumoto, Takahiro Obara, Hitoshi Akita Univ Grad Sch Engn Sci Tegata Gakuen Akita 0108502 Japan

A new design for parallel and distributed processing elements (PEs) is proposed to configure Benes networks based on a novel parallel algorithm that can realise full and partial permutations in a unified manner with very little overhead time and extra hardware. The proposed design reduces the hardware complexity of PEs from O(N2)to O(N(log2N)2) due to a distributed architecture. In the proposed design, asynchronous operation was introduced in parts to reduce the time complexity per PE stage down to O(1) within a certain N, while it takes O(log2N) time per PE stage in conventional algorithms. A prototype parallel was constructed and PEs were distributed in a field programmable gate array to investigate performance for the switch size of N = 4 to 32. The experimental results demonstrate that the proposed design outperforms a recent method by at least several times in terms of hardware and processing time complexities.

关键词： field programmable gate array network theory (graphs) Logic and switching circuits Logic circuits hardware complexity PEs parallel algorithms parallel algorithm processing time complexities distributed processing elements switch size Multiprocessing systems partial permutations Combinatorial mathematics parallel architectures Beneš networks asynchronous operation hardware-efficient parallel processing elements overhead time field programmable gate arrays distributed architecture time complexity PE stage Computational complexity computational complexity parallel architecture prototype parallel

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SR2C: A Structurally Redundant Short Reads Collapser for Optimizing DNA Data Compression 29

SR2C: A Structurally Redundant Short Reads Collapser for Opt...

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29th IEEE International Conference on parallel and Distributed Systems, ICPADS 2023

作者： Sun, Hui Ma, Huidong Zheng, Yingfeng Xie, Haonan Wang, Xiaofei Liu, Xiaoguang Wang, Gang Nankai University College of C.S. Nankai-Orange D.T. Joint Lab Nankai-Baidu Joint Lab ICIC TMCC SysNet DISSec GTIISC Tianjin300350 China Guangxi University School of Electrical Engineering Nanning530004 China

ISBN: (纸本)9798350330717

The current redundant sequence deduplication algorithms cannot remove structural repetitive DNA short reads such as mirror, reverse, paired, and complementary palindromes in high-throughput genomics sequencing data. Moreover, these methods also cannot construct indexes to recover the original sequences, thus failing to meet the requirements of lossless compression for downstream applications. To address these problems, we propose a data structure called Cycle-Hash-Linkage (CHL) and present a CPU parallelism optimization algorithm named SR2C (Structurally Redundant Short Reads Collapser) based on CHL to improve the compression ratio of DNA sequencing data. Experimental results on actual data from the NCBI database demonstrate that SR2C achieves an average residual sequence percentage improvement of 2.556% compared to the state-of-the-art redundant sequence deduplication algorithm, Minirmd. Furthermore, SR2C cascaded optimization improves the average compression ratios of compression algorithms Pigz, PBzip2, XZ, and 7Z by 92.345%, 78.999%, 10.132%, and 7.434%, respectively. By leveraging multi-core CPU parallel computation, SR2C effectively reduces time consumption, which achieves 2-5X deduplication and recovers *** same name Linux toolkit is freely available at https://***/fahaihi/SR2C. © 2023 IEEE.

关键词： data compression DNA sequencing data compression parallel algorithm redundancy deduplication rolling-hash algorithm structurally redundant reads

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Low-rank Parareal: a low-rank parallel-in-time integrator

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BIT NUMERICAL MATHEMATICS 2023年第1期63卷 13-13页

作者： Carrel, Benjamin Gander, Martin J. Vandereycken, Bart Univ Geneva Sect Math Geneva Switzerland

In this work, the Parareal algorithm is applied to evolution problems that admit good low-rank approximations and for which the dynamical low-rank approximation (DLRA) can be used as time stepper. Many discrete integrators for DLRA have recently been proposed, based on splitting the projected vector field or by applying projected Runge-Kutta methods. The cost and accuracy of these methods are mostly governed by the rank chosen for the approximation. These properties are used in a new method, called low-rank Parareal, in order to obtain a time-parallel DLRA solver for evolution problems. The algorithm is analyzed on affine linear problems and the results are illustrated numerically.

关键词： Dynamical low-rank approximation Initial value problem Matrix differential equation parallel algorithm

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