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The three-dimensional meshfree numerical manifold method based on parallel computing 认领 引用
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作者 Keqin Zhang Wei Wu +2 位作者 Danfeng Zhang Yanfei Kang Hehua Zhu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期360-371,共12页
The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challe... The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challenge,the meshfree numerical manifold method is developed by integrating the moving least-squares method into the numerical manifold method,effectively bypassing the need for meshing complex geometric objects.However,the implementation of the moving least-squares method introduces computational efficiency issues.To mitigate these,parallel computing methods have been incorporated,resulting in a tenfold increase in the speed of assembling the stiffness matrix with central processing unit parallelism,and a twentyfold increase with graphics processing unit parallelism.The static mechanical system equations for the meshfree numerical manifold method are derived using the Galerkin method.The method’s effectiveness and accuracy are then validated through a series of numerical experiments.The experiments demonstrated that the meshfree numerical manifold method achieves a high precision with minimal nodes and integration points.Additionally,positioning nodes outside the domain significantly improves computational accuracy at the boundaries. 展开更多
关键词 Meshfree numerical manifold method Three-dimensional computation Parallel computation Elastostatics Moving least-squares
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Privacy-Preserving Parallel Non-Negative Matrix Factorization with Edge Computing 认领 引用
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作者 Wenxuan Yu Wenjing Gao +2 位作者 Jiuru Wang Rong Hao Jia Yu 《Computers, Materials & Continua》 SCIE EI 2026年第6期498-518,共21页
Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy compu... Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers,effectively solving the problem of local computing difficulties.However,the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation,resulting in low efficiency on the server side.In order to improve the efficiency of outsourcing computation,we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers.We adopt the matrix blocking technique to divide the computation task into multiple subtasks,and design the NMF parallel computation algorithm based on the multiplication updating rule.The proposed scheme implements the parallel outsourcing of non-negative matrix factorization based on multiple edge servers.We use random permutation matrices to encrypt original matrix,thereby protecting data privacy.In addition,we utilize the iterative nature of the NMF algorithm for result verification.Theoretical analysis and experimental results prove the advantages of the proposed scheme. 展开更多
关键词 Secure outsourcing computation non-negative matrix factorization parallel outsourcing edge computing
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Efficient rock joint detection from large-scale 3D point clouds using vectorization and parallel computing approaches 认领 引用
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作者 Yunfeng Ge Zihao Li +2 位作者 Huiming Tang Qian Chen Zhongxu Wen 《Geoscience Frontiers》 SCIE CAS CSCD 2025年第5期1-15,共15页
The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the ... The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the high quality of data it provides.However,as research extends to address more regional and complex geological challenges,the demand for algorithms that are both robust and highly efficient in processing large datasets continues to grow.This study proposes an advanced rock joint identification algorithm leveraging artificial neural networks(ANNs),incorporating parallel computing and vectorization of high-performance computing.The algorithm utilizes point cloud attributes—specifically point normal and point curvatures-as input parameters for ANNs,which classify data into rock joints and non-rock joints.Subsequently,individual rock joints are extracted using the density-based spatial clustering of applications with noise(DBSCAN)technique.Principal component analysis(PCA)is subsequently employed to calculate their orientations.By fully utilizing the computational power of parallel computing and vectorization,the algorithm increases the running speed by 3–4 times,enabling the processing of large-scale datasets within seconds.This breakthrough maximizes computational efficiency while maintaining high accuracy(compared with manual measurement,the deviation of the automatic measurement is within 2°),making it an effective solution for large-scale rock joint detection challenges.©2025 China University of Geosciences(Beijing)and Peking University. 展开更多
关键词 Rock joints Pointclouds Artificialneuralnetwork High-performance computing Parallel computing Vectorization
Virtual QPU:A Novel Implementation of Quantum Computing 认领 引用
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作者 Danyang Zheng Jinchen Xv +1 位作者 Xin Zhou Zheng Shan 《Computers, Materials & Continua》 SCIE EI 2026年第4期1008-1029,共22页
The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing r... The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform. 展开更多
关键词 Quantum computing scheduling parallel computing computational paradigm
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A Hierarchical Task Graph Parallel Computing Framework for Chemical Process Simulation 认领 引用
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作者 Shifeng Qu Shaoyi Yang +3 位作者 Wenli Du Zhaoyang Duan Feng Qian Meihong Wang 《Engineering》 SCIE EI CSCD 2025年第8期229-239,共11页
Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,convention... Sequential-modular-based process flowsheeting software remains an indispensable tool for process design,control,and optimization.Yet,as the process industry advances in intelligent operation and maintenance,conventional sequential-modular-based process-simulation techniques present challenges regarding computationally intensive calculations and significant central processing unit(CPU)time requirements,particularly in large-scale design and optimization tasks.To address these challenges,this paper proposes a novel process-simulation parallel computing framework(PSPCF).This framework achieves layered parallelism in recycling processes at the unit operation level.Notably,PSPCF introduces a groundbreaking concept of formulating simulation problems as task graphs and utilizes Taskflow,an advanced task graph computing system,for hierarchical parallel scheduling and the execution of unit operation tasks.PSPCF also integrates an advanced work-stealing scheme to automatically balance thread resources with the demanding workload of unit operation tasks.For evaluation,both a simpler parallel column process and a more complex cracked gas separation process were simulated on a flowsheeting platform using PSPCF.The framework demonstrates significant time savings,achieving over 60%reduction in processing time for the simpler process and a 35%–40%speed-up for the more complex separation process. 展开更多
关键词 Parallel computing Process simulation Task graph parallelism Sequential modular approach
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ParaGraph:a parallel graph computing accelerator based on software-hardware collaboration 认领 引用
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作者 DENG Junyong KANG Yuchun +2 位作者 YE Zekun ZHU Yun JIA Yanting 《High Technology Letters》 EI CAS 2025年第2期105-117,共13页
Graph computing has become pervasive in many applications due to its capacity to represent complex relationships among different objects in the big data era.However,general-purpose architectures are computationally in... Graph computing has become pervasive in many applications due to its capacity to represent complex relationships among different objects in the big data era.However,general-purpose architectures are computationally inefficient for graph algorithms,and dedicated architectures can provide high efficiency,but lack flexibility.To address these challenges,this paper proposes ParaGraph,a reduced instruction set computing-five(RISC-V)-based software-hardware co-designed graph computing accelerator that can process graph algorithms in parallel,and also establishes a performance evaluation model to assess the efficiency of co-acceleration.ParaGraph handles parallel processing of typical graph algorithms on the hardware side,while performing overall functional control on the software side with custom designed instructions.ParaGraph is verified on the XCVU440 field-programmable gate array(FPGA)board with E203,a RISC-V processor.Compared with current mainstream graph computing accelerators,ParaGraph consumes 7.94%less block RAM(BRAM)resources than ThunderGP.Its power consumption is reduced by 86.90%,24.90%,and 76.38%compared with ThunderGP,HitGraph,and GraphS,respectively.The power efficiency of connected components(CC)and degree centrality(DC)algorithms is improved by an average of 6.50 times over ThunderGP,2.51 times over HitGraph,and 3.99 times over GraphS.The software-hardware co-design acceleration performance indicators H/W.Cap for CC and DC are 13.02 and 14.02,respectively. 展开更多
关键词 graph computing software-hardware co-design reduced instruction set computing-five(RISC-V) parallel accelerator
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ACP-based social computing and parallel intelligence: Societies 5.0 and beyond 认领 引用 被引量：26
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作者 XiaoWang Lingxi Li +2 位作者 Yong Yuan Peijun Ye Fei-Yue Wang 《CAAI Transactions on Intelligence Technology》 2016年第4期377-393,共17页
Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements includ... Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved. 展开更多
关键词 Social computing Societies 5.0 Parallel intelligence Knowledge automation Cyber-physical-social system Artificial societies Computational ex-periments Parallel execution
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The Parallel Computing of GPS Ray-shooting Model 认领 引用 被引量：4
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作者 李树勇 王斌 张昕 《Advances in Atmospheric Sciences》 SCIE CAS 2001年第6期1185-1191,共7页
The Global Positioning System (GPS) ray-shooting model is a self-sufficient observation operator in GPS/ MET (Meteorology) data variational assimilation linking up the GPS observation data and the atmospheric state va... The Global Positioning System (GPS) ray-shooting model is a self-sufficient observation operator in GPS/ MET (Meteorology) data variational assimilation linking up the GPS observation data and the atmospheric state variables. But its huge computations make it impracticable in real data assimilation so far. In order to overcome this default, a parallel version of the GPS ray-shooting model has been developed, and has been running successfully on the PC cluster manufactured under the support of the China National Key Development Planning Project for Basic Research: The Large Scale Scientific Computation Research. High speed-up and Efficiency as well as good scalability are obtained. This is an important step for this GPS observation operator to become practicable. Key words GPS ray-shooting - Parallel computing - Efficiency - Scalability This research was supported by the National Natural Science Foundation of China(Grant No. 49825109), the National Key Development Planning Project for Basic Research (Grant No. 1999032801) and the CAS Key Innovation Direction Project (Grant No.KZCX2208). 展开更多
关键词 GPS ray-shooting Parallel computing Efficiency Scalability
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Parallel Computing of a Variational Data Assimilation Model for GPS/MET Observation Using the Ray-Tracing Method 认领 引用 被引量：5
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作者 张昕 刘月巍 +1 位作者 王斌 季仲贞 《Advances in Atmospheric Sciences》 SCIE EI CAS 2004年第2期220-226,共7页
The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. V... The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. Verified by radiosonde, including GPS/MET observations into the analysis makes an overall improvement to the analysis variables of temperature, winds, and water vapor. However, the variational model with the ray-tracing method is quite expensive for numerical weather prediction and climate research. For example, about 4 000 GPS/MET refraction angles need to be assimilated to produce an ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time on the NCEP's Cray C90 computer. Although efforts have been taken to reduce the computational cost, it is still prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallel processors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and is implemented by message passing. The authors present the principles for the code's design and examine the performance on the state-of-the-art parallel computers in China. The results show that this parallel model scales favorably as the number of processors is increased. With the Memory-IO technique implemented by the author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from 45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to be useful in numerical weather prediction (NWP) and climate studies. 展开更多
关键词 parallel computing variational data assimilation GPS/MET
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Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing 认领 引用 被引量：3
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作者 Jiabao Wen Jiachen Yang +2 位作者 Tianying Wang Yang Li Zhihan Lv 《Digital Communications and Networks》 SCIE CSCD 2023年第2期473-482,共10页
To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel c... To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing. 展开更多
关键词 Wireless sensor network Parallel computation Task allocation Genetic algorithm Ant colony optimization algorithm Energy-efficient Load balancing
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Parallel Computing of the Underwater Explosion Cavitation Effects on Full-scale Ship Structures 认领 引用 被引量：7
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作者 Zhi Zong Yanjie Zhao +2 位作者 Fan Ye Haitao Li Gang Chen 《Journal of Marine Science and Application》 2012年第4期469-477,共9页
As well as shock wave and bubble pulse loading, cavitation also has very significant influences on the dynamic response of surface ships and other near-surface marine structures to underwater explosive loadings. In th... As well as shock wave and bubble pulse loading, cavitation also has very significant influences on the dynamic response of surface ships and other near-surface marine structures to underwater explosive loadings. In this paper, the acoustic-structure coupling method embedded in ABAQUS is adopted to do numerical analysis of underwater explosion considering cavitation. Both the shape of bulk cavitation region and local cavitation region are obtained, and they are in good agreement with analytical results. The duration of reloading is several times longer than that of a shock wave. In the end, both the single computation and parallel computation of the cavitation effect on the dynamic responses of a full-scale ship are presented, which proved that reloading caused by cavitation is non-ignorable. All these results are helpful in understanding underwater explosion cavitation effects. 展开更多
关键词 underwater explosion cavitation parallel computation full-scale ship
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Programming for scientific computing on peta-scale heterogeneous parallel systems 认领 引用 被引量：1
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作者 杨灿群 吴强 +2 位作者 唐滔 王锋 薛京灵 《Journal of Central South University》 SCIE EI CAS 2013年第5期1189-1203,共15页
Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to co... Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains （e.g., physics and biology） rather than MPI and OpenME This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-IA, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems. 展开更多
关键词 heterogeneous parallel system programming framework scientific computing GPU computing molecular dynamic
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PARALLEL COMPUTING FOR STATIC RESPONSE ANALYSIS OF STRUCTURES WITH UNCERTAIN-BUT-BOUNDED PARAMETERS 认领 引用 被引量：2
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作者 Zhiping Qiu Xiaojun Wang Xu Zhang 《Acta Mechanica Solida Sinica》 SCIE EI 2008年第5期472-482,共11页
The vertex solution for estimation on the static displacement bounds of structures with uncertain-but-bounded parameters is studied in this paper.For the linear static problem,when there are uncertain interval paramet... The vertex solution for estimation on the static displacement bounds of structures with uncertain-but-bounded parameters is studied in this paper.For the linear static problem,when there are uncertain interval parameters in the stiffness matrix and the vector of applied forces,the static response may be an interval.Based on the interval operations,the interval solution obtained by the vertex solution is more accurate and more credible than other methods(such as the perturbation method).However,the vertex solution method by traditional serial computing usually needs large computational efforts,especially for large structures.In order to avoid its disadvantages of large calculation and much runtime,its parallel computing which can be used in large-scale computing is presented in this paper.Two kinds of parallel computing algorithms are proposed based on the vertex solution.The parallel computing will solve many interval problems which cannot be resolved by traditional interval analysis methods. 展开更多
关键词 vertex solution interval analysis UBB parallel computing
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Joint wireless and cloud resource allocation based on parallel auction for mobile edge computing 认领 引用 被引量：2
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作者 Lan Zhuorui Xia Weiwei +2 位作者 Wu Siyun Yan Feng Shen Lianfeng 《Journal of Southeast University(English Edition)》 EI CAS 2019年第2期153-159,共7页
A joint resource allocation algorithm based on parallel auction(JRAPA)is proposed for mobile edge computing(MEC).In JRAPA,the joint allocation of wireless and cloud resources is modeled as an auction process,aiming at... A joint resource allocation algorithm based on parallel auction(JRAPA)is proposed for mobile edge computing(MEC).In JRAPA,the joint allocation of wireless and cloud resources is modeled as an auction process,aiming at maximizing the utilities of service providers(SPs)and satisfying the delay requirements of mobile terminals(MTs).The auction process consists of the bidding submission,winner determination and pricing stages.At the bidding submission stage,the MTs take available resources from SPs and distance factors into account to decide the bidding priority,thereby reducing the processing delay and improving the successful trades rate.A resource constrained utility ranking(RCUR)algorithm is put forward at the winner determination stage to determine the winners and losers so as to maximize the utilities of SPs.At the pricing stage,the sealed second-price rule is adopted to ensure the independence between the price paid by the buyer and its own bid.The simulation results show that the proposed JRAPA algorithm outperforms other existing algorithms in terms of the convergence rate and the number of successful trades rate.Moreover,it can not only achieve a larger average utility of SPs but also significantly reduce the average delay of MTs. 展开更多
关键词 parallel auction mobile edge computing joint resource allocation fast matching
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A Hybrid Parallel Strategy for Isogeometric Topology Optimization via CPU/GPU Heterogeneous Computing 认领 引用 被引量：1
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作者 Zhaohui Xia Baichuan Gao +3 位作者 Chen Yu Haotian Han Haobo Zhang Shuting Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1103-1137,共35页
This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstr... This paper aims to solve large-scale and complex isogeometric topology optimization problems that consumesignificant computational resources. A novel isogeometric topology optimization method with a hybrid parallelstrategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equationsolving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency ofCPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload betweenCPU and GPU. To illustrate the advantages of the proposedmethod, three benchmark examples are tested to verifythe hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster thanserial CPU and parallel GPU, while the speedups can be up to two orders of magnitude. 展开更多
关键词 Topology optimization high-efficiency isogeometric analysis CPU/GPU parallel computing hybrid OpenMPCUDA
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An incompressible flow solver on a GPU/CPU heterogeneous architecture parallel computing platform 认领 引用 被引量：1
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作者 Qianqian Li Rong Li Zixuan Yang 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2023年第5期387-393,共7页
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 points.To solve the Poisson equation,t... 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 points.To 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 preconditioner.The 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 2083.In 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 cores.The developed solver is then tested in the context of a two-dimensional lid-driven cavity flow and three-dimensional Taylor-Green vortex flow.The results are consistent with previous results in the literature. 展开更多
关键词 GPU Acceleration Parallel computing Poisson equation Preconditioner
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FTCSEM—A FORTRAN-based parallelized 1D CSEM forward and inversion program for arbitrary source-receiver geometry 认领 引用
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作者 Wei-ying Chen Si-xu Han +2 位作者 Wan-ting Song Yu-lian Zhu Zheng Liu 《Applied Geophysics》 SCIE CSCD 2026年第2期693-709,870,共17页
This study introduces FTCSEM,a FORTRAN-based,parallelized one-dimensional controlledsource electromagnetic(CSEM)forward modeling and inversion software capable of accommodating arbitrary source-receiver confi guration... This study introduces FTCSEM,a FORTRAN-based,parallelized one-dimensional controlledsource electromagnetic(CSEM)forward modeling and inversion software capable of accommodating arbitrary source-receiver confi gurations.In comparison to existing one-dimensional CSEM tools,FTCSEM incorporates several signifi cant enhancements:it supports transmitters of diverse shapes,quantities,and spatial locations;permits receivers to be positioned flexibly on the surface,subsurface,or in the atmosphere;facilitates simulations and inversions in both frequency and time domains;integrates an adaptive regularized inversion algorithm with multiple model constraints;and leverages GPU-accelerated parallel computing to attain high computational efficiency.Validation through numerical experiments and field data inversion confirms the program’s accuracy and practical applicability.The findings indicate that FTCSEM performs robustly in complex geoelectric environments,multi-source and multi-receiver arrangements,as well as multi-component joint inversion scenarios,thereby offering a versatile and powerful tool for advancing CSEM research and applications. 展开更多
关键词 CSEM forward modeling regularized inversion parallel computing program
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A high-performance parallel algorithm based on problem independent machine learning(PIML)for large-scale topology optimization 认领 引用
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作者 Xinyu Ma Mengcheng Huang +4 位作者 Zongliang Du Yilin Guo Chang Liu Yue Mei Xu Guo 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期197-213,共17页
Although supplying extensive design space,the curse of dimensionality restricts the widespread application of largescale topology optimization in practical engineering.Various acceleration techniques have been integra... Although supplying extensive design space,the curse of dimensionality restricts the widespread application of largescale topology optimization in practical engineering.Various acceleration techniques have been integrated with topology optimization,achieving significant attention and progress in large-scale problems.This work aims to investigate how much benefit can be obtained by combining parallel computing and machine learning techniques to enhance the efficiency of large-scale topology optimization algorithms.Accordingly,a parallel problem independent machine learning(PIML)-enhanced topology optimization method is proposed.The PIML model substantially reduces the dimension of the condensed stiffness matrix and its computational cost,and parallel computing reduces the workload per process and enables the application of a parallel multigrid solver.Besides,several techniques,such as matrix-free implementation,direct condensation of uniform coarse elements,and adjusting computational resource limits,have been developed to enhance computational efficiency.The weak scaling efficiency,strong scaling speedup,and maximum achievable efficiency of the proposed method are validated across multiple numerical examples,showing significant improvement in the tractable problem size and solution efficiency compared to traditional topology optimization algorithms. 展开更多
关键词 Topology optimization Large-scale Problem independent machine learning(PIML) Parallel computing
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A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model 认领 引用
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作者 Jawad Ahmad Mimonah Al Qathrady +3 位作者 Mohammed SAlshehri Yazeed Yasin Ghadi Mujeeb Ur Rehman Syed Aziz Shah 《Computers, Materials & Continua》 SCIE EI 2023年第8期1325-1341,共17页
Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to empl... Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption techniques.While 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical application.In this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure cryptosystem.The randomness of the chaotic map is assessed through standard analysis.The 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 space.Additionally,a fast image encryption technique utilizing the new chaotic system is proposed.The novelty of the approach is confirmed through time complexity analysis.To further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key generation.Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure communication.Moreover,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. 展开更多
关键词 Hindmarsh-rose model image encryption SHA-256 parallel computing
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Dynamic Distribution Model with Prime Granularity for Parallel Computing 认领 引用
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作者 孙济洲 张绍敏 李小图 《Transactions of Tianjin University》 EI CAS 2005年第5期343-347,共5页
Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simulta... Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing. 展开更多
关键词 granularity parallel computing load balancing dynamic distribution model
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