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2nd China Power Supply Society Electromagnetic Compatibility Conference, CPEMC 2024

作者： Jie, Huamin Zhao, Zhenyu Chang, Yongqi Wang, Changdong Jiang, Shiqi Gao, Richard Xian-Ke See, Kye Yak School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of High Performance Computing A*STAR Singapore Singapore

ISBN: (纸本)9798331509552

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz. ©2024 IEEE.

关键词： Magnetic flux

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Yue Liu Li Wang Yanyan Chen Qi Liu Jingshan Pan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor’s storage structure; combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency; optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication; employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation.

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DUNE: Improving Accuracy for Sketch-INT Network Measurement Systems

arXiv

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arXiv 2022年

作者： Wei, Zhongxiang Tian, Ye Chen, Wei Gu, Liyuan Zhang, Xinming The Anhui Key Laboratory of High Performance Computing School of Computer Science and Technology University of Science and Technology of China Anui Hefei230026 China

In-Band Network Telemetry (INT) and sketch algorithms are two representative methodologies for measuring network traffics in real time. To combine sketch with INT and to keep their advantages, the "reconstructing sketch at end-host" approach, which uses INT to send small pieces of switch sketch (i.e., sketchlet) to end-host for reconstructing an identical sketch, is a promising direction. However, we reveal that the naive column sketchlet is not efficient, and inaccuracies arise because of the invalid and stale measurement data in the end-host reconstructed sketch. In this paper, we present an innovative sketch-INT measurement system named DUNE. DUNE is lightweight by following the "reconstructing the sketch at end-host" approach, and to improve the measurement accuracy, we make two innovations: First, we design a novel sketchlet named scatter sketchlet that is more efficient in transferring measurement data by allowing a switch to select individual sketch buckets to add to sketchlet;Second, we develop data structures for tracing "freshness" of the sketch buckets, and present algorithms for smartly selecting buckets that contain valuable measurement data to send to end-host. We theoretically prove that our proposed methods are superior comparing with the existing solution, and implement a prototype on the commodity Barefoot Tofino switches. We conduct extensive experiments on DUNE, and the evaluation results show that the system considerably improves measurement accuracies at negligible costs. In particular, with less than 0.36% loss of the packet forwarding rate, DUNE avoids up to 60% errors in the end-host reconstructed sketch. Copyright © 2022, The Authors. All rights reserved.

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PIOD: An Efficient Parallel Iterative Algorithm for Solving Over-determined Equations

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Zhenguo Wei Yanlong Li Wei Du Lei Xiao Chaoshuai Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%.

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Qi Liu Yanlong Li Wei Du Zenghui Ren Yue Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%.

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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SPM-GCN: An adaptive reordering algorithm for sparse LU factorization via GCN

SPM-GCN: An adaptive reordering algorithm for sparse LU fact...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Huazeng Liu Junjie Wang Qi Liu Wei Du Zanjun Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it’s difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU.

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An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks 21st

An Automata-Theoretic Approach to Synthesizing Binarized Ne...

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21st International Symposium on Automated Technology for Verification and Analysis, ATVA 2023

作者： Tao, Ye Liu, Wanwei Song, Fu Liang, Zhen Wang, Ji Zhu, Hongxu College of Computer Science and Technology National University of Defense Technology Changsha China School of Information Science and Technology ShanghaiTech University Shanghai China Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China Automotive Software Innovation Center Shanghai China Institute for Quantum Information and State Key Laboratory for High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9783031453281

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training. Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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high-rate discretely-modulated continuous-variable quantum key distribution using quantum machine learning

arXiv

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arXiv 2023年

作者： Liao, Qin Liu, Jieyu Huang, Anqi Huang, Lei Fei, Zhuoying Fu, Xiquan College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

We propose a high-rate scheme for discretely-modulated continuous-variable quantum key distribution (DM CVQKD) using quantum machine learning technologies, which divides the whole CVQKD system into three parts, i.e., the initialization part that is used for training and estimating quantum classifier, the prediction part that is used for generating highly correlated raw keys, and the data-postprocessing part that generates the final secret key string shared by Alice and Bob. To this end, a low-complexity quantum k-nearest neighbor (QkNN) classifier is designed for predicting the lossy discretely-modulated coherent states (DMCSs) at Bob's side. The performance of the proposed QkNN-based CVQKD especially in terms of machine learning metrics and complexity is analyzed, and its theoretical security is proved by using semi-definite program (SDP) method. Numerical simulation shows that the secret key rate of our proposed scheme is explicitly superior to the existing DM CVQKD protocols, and it can be further enhanced with the increase of modulation variance. Copyright © 2023, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Generating Coupling Interfaces for Multiphysics Simulations with ExaStencils and waLBerla

Generating Coupling Interfaces for Multiphysics Simulations ...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Richard Angersbach Sebastian Kuckuk Harald Köstler Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg National High Performance Computing Center Friedrich-Alexander-Universität Erlangen-Nürnberg

Domain-specific languages (DSLs) and code generation technology are adopted more and more by the high performance computing (HPC) community to tackle the performance, portability, and productivity requirements for HPC software. They share their inherent specialization in specific scientific domains with traditional HPC frameworks. In this regard, multiphysics simulations pose a strong challenge as they usually require components from multiple domains. A promising option for retaining these requirements is the combination of code generation with HPC frameworks through automatically created couplings. This work demonstrates the fusion of waLBerla with generated code from ExaStencils. waLBerla is a massively parallel C++ framework focused on computational fluid dynamics (CFD) simulations using the Lattice Boltzmann method (LBM). ExaStencils generates whole simulation codes using geometric multigrid solvers from specifications in its own DSL. We showcase the coupling of generated solvers with LBM simulations, including the mapping of data structures and augmentations to convert quantities units on-the-fly.

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