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Nonconvex Dantzig selector and its parallel computing algorithm

STATISTICS AND computing 2024年第6期34卷 1-21页

作者： Wen, Jiawei Yang, Songshan Zhao, Delin Meta 1 Hacker Way Menlo Pk CA 94025 USA Renmin Univ China Ctr Appl Stat 59 Zhongguancun St Beijing 100872 Peoples R China Renmin Univ China Inst Stat & Big Data 59 Zhongguancun St Beijing 100872 Peoples R China

The Dantzig selector is a popular & ell;1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1$$\end{document}-type variable selection method widely used across various research fields. However, & ell;1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1$$\end{document}-type methods may not perform well for variable selection without complex irrepresentable conditions. In this article, we introduce a nonconvex Dantzig selector for ultrahigh-dimensional linear models. We begin by demonstrating that the oracle estimator serves as a local optimum for the nonconvex Dantzig selector. In addition, we propose a one-step local linear approximation estimator, called the Dantzig-LLA estimator, for the nonconvex Dantzig selector, and establish its strong oracle property. The proposed regularization method avoids the restrictive conditions imposed by & ell;1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1$$\end{document} regularization methods to guarantee the model selection consistency. Furthermore, we propose an efficient and parallelizable computing algorithm based on feature-splitting to address the computational challenges associated with the nonconvex Dantzig selector in high-dimensional settings. A comprehensive numerical study is conducted to evaluate the performance of the nonconvex Dantzig selector and the computing efficiency of the feature-splitting algorithm. The results demonstrate that the Dantzig selector with non

关键词： High-dimensional regularization model Local linear approximation parallel computing SCAD

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A Novel parallel computing Confidentiality Scheme Based on Hindmarsh-Rose Model

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Computers, Materials & Continua 2023年第8期76卷 1325-1341页

作者： Jawad Ahmad Mimonah Al Qathrady Mohammed SAlshehri Yazeed Yasin Ghadi Mujeeb Ur Rehman Syed Aziz Shah School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates School of Science Technology and HealthYork St John UniversityYorkYO317EXUK Research Centre for Intelligent Healthcare Coventry UniversityCoventryCV15FBUK

Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this ***,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption *** 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical *** this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure *** randomness of the chaotic map is assessed through standard *** proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value ***,a fast image encryption technique utilizing the new chaotic system is *** novelty of the approach is confirmed through time complexity *** further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key *** results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure ***,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.

关键词： Hindmarsh-rose model image encryption SHA-256 parallel computing

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Research and application of the parallel computing method for the grid-based Xin'anjiang model

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HYDROLOGY RESEARCH 2023年第4期54卷 591-605页

作者： Liu, Qian Wan, Dingsheng Yu, Yufeng Zhang, Yangming Hohai Univ Coll Comp & Informat Nanjing 211100 Jiangsu Peoples R China Bank Nanjing Nanjing 211100 Jiangsu Peoples R China

The grid-based Xin'anjiang model (GXM) has been widely applied to flood forecasting. However, when the model warm-up period is long and the amount of input data is large, the computational efficiency of the GXM is obviously low. Therefore, a GXM parallel algorithm based on grid flow direction division is proposed from the perspective of spatial parallelism, which realizes the parallel computing of the GXM by extracting the parallel routing sequence of the watershed grids. To solve data skew, a DAG scheduling algorithm based on dynamic priority is proposed for task scheduling. The proposed GXM parallel algorithm is verified in the Qianhe River watershed of Shaanxi Province and the Tunxi watershed of Anhui Province. The results show that the GXM parallel algorithm based on grid flow direction division has good flood forecasting accuracy and higher computational efficiency than the traditional serial computing method. In addition, the DAG scheduling algorithm can effectively improve the parallel efficiency of the GXM.

关键词： distributed hydrologic model flood forecasting grid-based Xin'anjiang model parallel computing

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An incompressible flow solver on a GPU/CPU heterogeneous architecture parallel computing platform

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Theoretical & Applied Mechanics Letters 2023年第5期13卷 387-393页

作者： Qianqian Li Rong Li Zixuan Yang The State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Sciences University of Chinese Academy of SciencesBeijing 100049China

A computational fluid dynamics(CFD)solver for a GPU/CPU heterogeneous architecture parallel computing platform is developed to simulate incompressible flows on billion-level grid *** solve the Poisson equation,the conjugate gradient method is used as a basic solver,and a Chebyshev method in combination with a Jacobi sub-preconditioner is used as a *** developed CFD solver shows good performance on parallel efficiency,which exceeds 90%in the weak-scalability test when the number of grid points allocated to each GPU card is greater than *** the acceleration test,it is found that running a simulation with 10403 grid points on 125 GPU cards accelerates by 203.6x over the same number of CPU *** developed solver is then tested in the context of a two-dimensional lid-driven cavity flow and three-dimensional Taylor-Green vortex *** results are consistent with previous results in the literature.

关键词： GPU Acceleration parallel computing Poisson equation Preconditioner

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A parallel computing based model for online binary computation offloading in mobile edge computing

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COMPUTER COMMUNICATIONS 2023年 203卷 248-261页

作者： Acheampong, Abednego Zhang, Yiwen Xu, Xiaolong Anhui Univ Sch Comp Sci & Technol Hefei 230039 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China

Mobile-edge computing (MEC) with wireless power transfer has recently emerged as a viable concept for improving the data processing capacity of limited powered networks like wireless sensor networks (WSN) and the internet of things (IoT). In this research, we explore a wireless MEC network with a binary offloading strategy. Each mobile device's (MDs) computation task is either performed locally or entirely offloaded to a MEC server. We aim to develop an online system that adapts task offloading decisions and resource allocations to changing wireless channel conditions in real-time. This necessitates solving difficult combinatorial optimization problems quickly within the channel coherence time, which is difficult to achieve with traditional optimization approaches. To address this issue, we offer a parallel computing architecture in which several parallel offloading actors as deep neural networks (DNNs) are used as a scalable method to learn binary offloading decisions from experience. It avoids the need to solve combinatorial optimization issues, reducing computational complexity significantly, especially in large networks. Compared to existing optimization approaches, numerical results demonstrate that the proposed algorithm can achieve optimal performance while reducing computing time by an acceptable margin. For instance, our algorithm achieves a latency rate of 0.033 s in a network of 30 MDs.

关键词： parallel computing Wireless power transfer Edge computing Intelligent control Computation task

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A parallel computing Method for the Computation of the Moore-Penrose Generalized Inverse for Shared-Memory Architectures

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IEEE ACCESS 2023年 11卷 134834-134845页

作者： Gelvez-Almeida, Elkin Barrientos, Ricardo J. Vilches-Ponce, Karina Mora, Marco Univ Catolica Maule Doctorado Modelamiento Matemat Aplicado Talca 3480112 Chile Univ Catolica Maule Fac Ciencias Ingn Lab Technol Res Pattern Recognit LITRP Talca 3480112 Chile Univ Simon Bolivar Fac Ciencias Basicas & Biomed San Jose De Cucuta 540006 Colombia Univ Catolica Maule Fac Ciencias Ingn Dept Comp & Ind Talca 3480112 Chile Univ Catolica Maule Fac Ciencias Basicas Dept Matemat Fis & Estadist Talca 3480112 Chile

The computation of the Moore-Penrose generalized inverse is a commonly used operation in various fields such as the training of neural networks based on random weights. Therefore, a fast computation of this inverse is important for problems where such neural networks provide a solution. However, due to the growth of databases, the matrices involved have large dimensions, thus requiring a significant amount of processing and execution time. In this paper, we propose a parallel computing method for the computation of the Moore-Penrose generalized inverse of large-size full-rank rectangular matrices. The proposed method employs the Strassen algorithm to compute the inverse of a nonsingular matrix and is implemented on a shared-memory architecture. The results show a significant reduction in computation time, especially for high-rank matrices. Furthermore, in a sequential computing scenario (using a single execution thread), our method achieves a reduced computation time compared with other previously reported algorithms. Consequently, our approach provides a promising solution for the efficient computation of the Moore-Penrose generalized inverse of large-size matrices employed in practical scenarios.

关键词： High-performance computing Moore-Penrose generalized inverse matrix neural networks with random weights parallel computing Strassen algorithm

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Heterogeneous parallel computing method for 3D transient nonlinear thermomechanical problems on CPU-GPU platforms

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2023年 157卷 177-190页

作者： Wang, Shengquan Wang, Yujie Zhang, Shuai Cai, Yong Li, Guangyao Zheng, Hao Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Beijing Inst Technol Shenzhen Automot Res Inst Shenzhen 518118 Guangdong Peoples R China Guangzhou Automobile Grp CO LTD Automot Engn Inst Guangzhou 511434 Guangdong Peoples R China

A heterogeneous parallel computing method for 3D transient nonlinear thermomechanical problems is proposed based on CPU-GPU platforms. The method is developed based on the stable node-based smoothed finite element method (SNS-FEM) and element-by-element (EBE) strategy. The SNS-FEM method ensures the accuracy of temperature and displacement solution. To obtain higher computational efficiency, a series of parallel computing strategies are proposed in this paper. A one-to-one mapping relationship is established between smoothing domains calculation and threads in GPU. To improve the efficiency of constructing the smoothing domain, a semiparallel construction method is developed. And a preindexing method is proposed to improve the computational efficiency of solving linear equations by the parallel preconditioned conjugate gradient (PCG) method. The developed heterogeneous parallel computing method is quite accurate and efficient in computing the model composed of tetrahedral elements, obtaining a speedup ratio of 52.31 when the model contains 67,204 degrees of freedom.

关键词： Transient thermomechanical SNS-FEM Element-by-element parallel computing GPU

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Cooperative Job Scheduling and Data Allocation in Data-Intensive parallel computing Clusters

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IEEE TRANSACTIONS ON CLOUD computing 2023年第3期11卷 2392-2406页

作者： Wang, Haoyu Liu, Guoxin Shen, Haiying Univ Virginia Dept Comp Sci Charlottesville VA 22904 USA Epic Syst I-31942 Verona WI Italy

In data-intensive parallel computing clusters, it is important to provide deadline-guaranteed service to jobs while minimizing resource usage (e.g., network bandwidth and energy). Under the current computing framework (that first allocates data and then schedules jobs), in a busy cluster with many jobs, it is difficult to achieve high data locality (hence low bandwidth consumption), deadline guarantee, and high energy savings simultaneously. We model the problem to simultaneously achieve these three objectives using integer programming. Due to the NP-hardness of the problem, we propose a heuristic Cooperative job Scheduling (CSA) and data Allocation method. CSA novelly reverses the order of data allocation and job scheduling in the current computing framework. Job-scheduling-first enables CSA to proactively consolidate tasks with more common requested data to the same server when conducting deadline-aware scheduling, and also consolidate the tasks to as few servers as possible to maximize energy savings. This facilitates the subsequent data allocation step to allocate a data block to the server that hosts most of this data's requester tasks, thus maximally enhancing data locality. To achieve the tradeoff between data locality and energy savings with specified weights, CSA has a cooperative recursive refinement process that recursively adjusts the job schedule and data allocation schedule. We further propose two enhancement algorithms (i.e., minimum k-cut data reallocation algorithm and bipartite based task reassignment algorithm) to further improve the performance of CSA through additional data reallocation and task reassignment, respectively. Trace-driven experiments in the simulation and the real cluster show that CSA outperforms other schedulers in supplying deadline-guarantee and resource-efficient services and the effectiveness of each enhancement. Also, the enhancement algorithms are effective in improving CSA.

关键词： Task analysis Servers Resource management Schedules Clustering algorithms Processor scheduling Costs Job scheduler data allocation parallel computing data locality

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Fast chaotic encryption scheme based on separable moments and parallel computing

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 38793页

作者： Kamrani, Abdelhalim Zenkouar, Khalid Najah, Said El Fadili, Hakim Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol Lab Intelligent Syst & Applicat LSIA Fes Morocco Univ Sidi Mohamed Ben Abdellah Ecole Natl Sci Appl Fez Fes Morocco

In this paper, we propose three novel image encryption algorithms. Separable moments and parallel computing are combined in order to enhance the security aspect and time performance. The three proposed algorithms are based on TKM (Tchebichef-Krawtchouk moments), THM (Tchebichef-Hahn moments) and KHM (Krawtchouk-Hahn moments) respectively. A novel chaotic scheme is introduced, that enhances security by adding a layer of block permutation on top of the classical confusion/diffusion scheme, and reduces time cost through parallel computing. This approach offers improved security and faster performance compared to classical encryption schemes. The proposed algorithms are tested under several criteria and the experimental results show a remarkable resilience against all well-known attacks. Furthermore, the novel parallel encryption scheme exhibits a drastic improvement in the time performance. The proposed algorithms are compared to the state-of-the-art methods and they stand out as a promising choice for reliable use in real world applications.

关键词： Image encryption Separable moments Chaos cryptography parallel computing

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parallel computing Techniques for Accelerating Machine Learning Algorithms on Big Data

Parallel Computing Techniques for Accelerating Machine Learn...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mishra, Rahul Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350357769

In the era of Big Data, the computational demands of machine learning (ML) algorithms have grown exponentially, necessitating the development of efficient parallel computing techniques. This research paper delves into the exploration and evaluation of advanced parallel computing methodologies tailored for accelerating ML algorithms when applied to vast datasets. We commence by providing a comprehensive overview of the existing parallelization paradigms, highlighting their strengths and limitations in the context of ML. Subsequently, we introduce novel techniques that harness the combined power of distributed systems, multicore processors, and graphics processing units (GPUs) to achieve significant speed-ups. Experimental results, derived from processing multi-terabyte datasets, demonstrate that our proposed methods can achieve up to a tenfold increase in computational efficiency compared to traditional approaches. Furthermore, we present a detailed analysis of the scalability, fault-tolerance, and resource utilization of our techniques, providing insights for practitioners aiming to deploy ML algorithms on large-scale data infrastructures. This work not only paves the way for faster ML computations but also offers a blueprint for the next generation of scalable, parallel ML systems. © 2023 IEEE.

关键词： Big Data Distributed Systems Machine Learning Acceleration parallel computing Scalability

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