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8th Annual International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2020

作者： Xu, Yunpeng Peng, Yuanxi Long, Jian State Key Laboratory of High-Performance Computing College of Computer National University of Defense Technology Changsha410073 China

How to generate a color image with as much information as possible from hyperspectral images on a trichromatic display is a subject of great concern. In a sense, effective bands containing important information can be extracted from hyperspectral data for image visualization through the method of band selection. In this paper, two improved methods are proposed for the linear prediction (LP) band selection *** to the information entropy, the band with the greatest difference in information domain is selected as initial ***, the max pooling method is used to select the pixel which can better reflect the image features. Finally, the band similarity is used to select the most distinctive band for hyperspectral image visualization. The experimental results are measured by the metrics such as feature separability, standard deviation, operation efficiency and image displaying *** final experimental results show that fast linear prediction (LP) algorithm is an effective method for hyperspectral image visualization. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Data visualization

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A Policy Gradient Path Planning Algorithm for Wireless Rechargeable Sensor Networks 4

A Policy Gradient Path Planning Algorithm for Wireless Recha...

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4th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2021

作者： Li, Baicai Xiao, Xiaoqiang Tang, Shun Ning, Weixun National University of Defense Technology State Key Laboratory of High Performance Computing Computer School Hunan Changsha China

ISBN: (纸本)9781665428392

Wireless rechargeable sensor networks (WRSN) can prolong the lifetime of wireless sensor networks (WSN). In WRSN, mobile charger (MC) is used to charge low-power sensor node (SN). How to plan an efficient charging path for MC is a NP-hard problem. The task is to plan a path for MC that can not only charge the SN requesting charging in time, but also reduce the driving distance, so as to reduce the energy loss of MC. In this paper, policy gradient path planning (PGPP) algorithm based on deep reinforcement learning is proposed to solve this problem. In PGPP, the SNs in WRSN are treated as the environment, and the base station and MC are seen as the sensors and actuators of the agent. A reward function which can measure the energy efficiency of MC is designed, and the policy gradient algorithm is used to learn the strategy. Simulation experiments show that compared with other MC charging path planning algorithms, PGPP algorithm has certain advantages in the speed of path planning and the energy efficiency of WRSN. © 2021 IEEE.

关键词： Energy efficiency

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Deep Learning Optimization for Many-Core Virtual Platforms 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Cai, Hengyu Ning, Chengming Zheng, Qilong University of Science and Technology of China Hefei China AnHui Province Key Laboratory of High Performance Computing Hefei China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

The rapid development of deep learning technology has made deep learning models widely used in image processing, speech recognition, and target tracking. However, the model becomes larger and larger, and it is difficult to deploy on a stand-alone device, usually on a distributed computing platform. As a high-performance digital signal processor developed by the 38th Research Institute of China Electronics technology Group, HXDSP has strong computing power and rich computing resources, and is suitable for computing-intensive applications such as deep learning. Design the many-core virtual platform based on the HXDSP simulator, and provide the parallel communication interface MPIRIO to realize fast communication and task synchronization between the HXDSPs, and provide basic conditions for the deployment of deep learning models. At the same time, the parallel computing capability and pipeline mechanism provided by the virtual platform are used to accelerate the operation of the model. Aiming at the problem that the traditional gradient descent algorithm needs to be manually set, the meta-learning optimization algorithm is used to realize the adaptive fine-tuning of the model on the virtual platform, forming a deep learning optimization framework based on the CPU/HXDSP heterogeneous system. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Digital signal processors

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Scheme for braiding Majorana zero modes in vortices using an STT-matrix

arXiv

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arXiv 2024年

作者： Huang, Guangyao Zhang, Xinfang Yi, Xiaofeng Fu, Jibang Wang, Weichen Deng, Mingtang Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Hefei National Laboratory Hefei230088 China

Recently conducted experiments on two-dimensional topological superconductors have revealed various indications of Majorana zero modes (MZMs). However, progress in the manipulation of MZM braiding has been limited, impeding the realization of topological quantum computing. In this study, we propose a potential braiding scheme based on a spintronics device matrix. This scheme involves utilizing a matrix composed of spin-transfer torque devices (STT-matrix) alongside a two-dimensional topological superconductor material. By programming the ON/OFF states of the spintronics devices within the STT-matrix, it becomes possible to manipulate vortices hosting MZMs in the two-dimensional topological superconductor. To further investigate this concept, we construct a time-dependent Ginzburg-Landau model and perform numerical simulations to analyze vortex-driving dynamics, MZM braiding processes, and MZM fusion phenomena. Our findings demonstrate that this system exhibits high versatility and flexibility in manipulating vortices. With advancements in spintronics device technology, our proposed scheme offers a feasible and practical method for operating MZMs within vortices present in topological superconductors. © 2024, CC BY.

关键词： Topology

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Research on Hybrid Genetic Algorithm based on Double structure coding

Research on Hybrid Genetic Algorithm based on Double structu...

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2023 International Conference on Algorithms, high performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Wang, Fan Yan, Renjun Qiu, Yu 1School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan China 2Key Laboratory of High Performance Ship Technology Wuhan University of Technology Ministry of Education Wuhan China

ISBN: (纸本)9781510671881

The 0-1knapsack problem is well known to be NP-hard in optimization algorithms. There are many commonly used methods to optimize such problems, such as dynamic programming, enumeration, pruning and bounding, greedy algorithms, backtracking, etc. With the advent of some modern heuristics, such as swarm intelligent optimization algorithms, more options have been provided for solving such problems. The Gene-tic algorithm is one of the most commonly used algorithms. The idea of this paper is as follows: firstly, the mathematical model of 0-1 knapsack problem is given, combining the characteristics of the model, the idea of double structure is introduced, combined with the greedy algorithm, the two-step method of value factor is proposed, and the secondary selection coding is carried out. In the selection operation, recombination is used to improve the convergence of the following algorithm. Compared with the traditional binary genetic algorithm, the genetic algorithm with penalty terms and the standard genetic algorithm (SGA), the convergence of the improved binary genetic algorithm is significantly improved. © 2023 SPIE. All rights reserved.

关键词： Genetic algorithms

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable Data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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Large-Scale Thermo-Hydraulic Analysis of Fuel Rod Bundles Based on YH-ACT

Large-Scale Thermo-Hydraulic Analysis of Fuel Rod Bundles Ba...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Min Song Chao Li Xiaowei Guo Jie Liu Huajian Zhang Rui Xia Laboratory of Digitizing Software for Frontier Equipment Changsha China Institution of Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In the design process of nuclear reactors, high-fidelity numerical simulations of thermo-hydraulic processes play a crucial role in evaluating the feasibility and safety of design schemes. These simulations involve complex physical processes such as coolant flow, thermal conduction, and heat transfer, necessitating the use of high-precision numerical algorithms and efficient parallel optimization methods. While mainstream commercial software is widely used in reactor design, it often falls short when it comes to meeting customized experimental scenarios and faces limitations in parallel scalability and physical model adaptability. Hence, the development of independent thermo-hydraulic parallel applications becomes essential. This paper focuses on conducting large-scale refined thermo-hydraulic analysis of core fuel rod bundles using the self-developed software YHACT. The paper elucidates the model algorithm design, parallel implementation, and analysis of test results. Two typical cases were selected for verification by comparing the obtained results with those from FLUENT. Emphasis was placed on the large-scale parallel simulation of fluid-solid coupled convection heat transfer phenomena between fuel rod bundles, utilizing a mesh scale exceeding 100 million cells. This breakthrough overcomes the scale limitations encountered with commercial software, and promises to foster further research and development in related fields.

关键词： Couplings Analytical models Adaptation models Scalability Software algorithms Software Thermal analysis Numerical models Fuels Security

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FlowDNN:a physics-informed deep neural network for fast and accurate flow prediction

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Frontiers of Information technology & Electronic Engineering 2022年第2期23卷 207-219页

作者： Donglin CHEN Xiang GAO Chuanfu XU Siqi WANG Shizhao CHEN Jianbin FANG Zheng WANG College of Computer National University of Defense TechnologyChangsha 410073China State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China School of Computing University of LeedsLeeds LS29JTUK

For flow-related design optimization problems,e.g.,aircraft and automobile aerodynamic design,computational fluid dynamics(CFD)simulations are commonly used to predict flow fields and analyze *** important,CFD simulations are a resource-demanding and time-consuming iterative *** expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive *** this paper,we propose Flow DNN,a novel deep neural network(DNN)to efficiently learn flow representations from CFD *** DNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry *** DNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow *** approach not only improves the prediction accuracy,but also preserves the physical consistency of the predicted flow fields,which is essential for *** metrics are derived to evaluate Flow DNN with respect to the whole flow fields or regions of interest(RoIs)(e.g.,boundary layers where flow quantities change rapidly).Experiments show that Flow DNN significantly outperforms alternative methods with faster inference and more accurate *** speeds up a graphics processing unit(GPU)accelerated CFD solver by more than 14000×,while keeping the prediction error under 5%.

关键词： Deep neural network Flow prediction Attention mechanism Physics-informed loss

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Optimizing Blocked-Davidson Method based on Adaptive Algorithm using the New Sunway Supercomputer 2

Optimizing Blocked-Davidson Method based on Adaptive Algorit...

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2022 2nd Conference on high performance computing and Communication Engineering, HPCCE 2022

作者： Yang, Chaochao Liu, Anjun Liu, Weifeng Hou, Jie Guo, Meng Shandong Computer Science Center Qilu University of Technology China Jinan Key Laboratory of High Performance Industrial Software China Institute of Applied Physics and Computational Mathematics China Jinan Institute of Supercomputing Technology China

ISBN: (纸本)9781510663374

The Davidson method is a traditional large-scale iterative diagonalization method in computational quantum chemistry. The Blocked-Davidson method is a classical version of the Davidson method, which is an important part of VASP software, occupying 22.9% of the calculation time. When VASP is transplanted to the new Sunway supercomputer, it is incompatible with the many-core heterogeneous environment of Sunway processor. The Blocked-Davidson method does not use slave-core accelerators. This paper designs collaborative computing, double buffer, store variables in slave-core LDM, LDM Level 2 Access, and segmented transmission methods of Blocked-Davidson method based on Sunway many-core architecture. The test results showed that the function in the Blocked-Davidson method accelerated 30.65 times with a single core group in the silica test case. However, negative optimization of the Blocked-Davidson method happens in some test cases such as ZrTiAlVSiN5 and Hg50. Therefore, an adaptive algorithm of automatic selection of less time-consuming optimization paths is designed to solve the negative optimization. The maximum acceleration is 23.6 times compared with not using the adaptive algorithm. © 2023 SPIE.

关键词： Supercomputers

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Revitalizing the forgotten on-chip DMA to expedite data movement in NVM-based storage systems 23

Revitalizing the forgotten on-chip DMA to expedite data move...

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Proceedings of the 21st USENIX Conference on File and Storage Technologies

作者： Jingbo Su Jiahao Li Luofan Chen Cheng Li Kai Zhang Liang Yang Sam H. Noh Yinlong Xu University of Science and Technology of China University of Science and Technology of China and Anhui Province Key Laboratory of High Performance Computing SmartX UNIST and Virginia Tech

ISBN: (纸本)9781939133328

Data-intensive applications executing on NVM-based storage systems experience serious bottlenecks when moving data between DRAM and NVM. We advocate for the use of the long-existing but recently neglected on-chip DMA to expedite data movement with three contributions. First, we explore new latency-oriented optimization directions, driven by a comprehensive DMA study, to design a high-performance DMA module, which significantly lowers the I/O size threshold to observe benefits. Second, we propose a new data movement engine, Fastmove, that coordinates the use of the DMA along with the CPU with judicious scheduling and load splitting such that the DMA's limitations are compensated, and the overall gains are maximized. Finally, with a general kernel-based design, simple APIs, and DAX file system integration, Fastmove allows applications to transparently exploit the DMA and its new features without code change. We run three data-intensive applications MySQL, Graph-Walker, and Filebench atop NOVA, ext4-DAX, and XFS-DAX, with standard benchmarks like TPC-C, and popular graph algorithms like PageRank. Across single- and multisocket settings, compared to the conventional CPU-only NVM accesses, Fastmove introduces to TPC-C with MySQL 1.13- 2.16× speedups of peak throughput, reduces the average latency by 17.7-60.8%, and saves 37.1-68.9% CPU usage spent in data movement. It also shortens the execution time of graph algorithms with GraphWalker by 39.7-53.4%, and introduces 1.12-1.27× throughput speedups for Filebench.

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