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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Gao, Xiuwu Jiang, Jun Huang, Liangming Wei, Hongmei Jiangnan Institute of Computing Technology Wuxi China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665478571

Data prefetching is a widely used technique to alleviate "memory wall"problem by fetching the data that may be touched in the near future in advance. Generally, data prefetching is classified into hardware prefetching and software prefetching. Compared to hardware prefetching, software prefetching is more flexible, and typically achieves higher prefetch accuracy. Currently, Sunway multiple-core processor only supports hardware prefetching. To study how software prefetching perform on Sunway processor, in this paper, we first migrate the software data prefetching in the GCC complier to Sunway processor. Then we tune the loop-level prefetching cost model according to Sunway processor's hardware features. Finally, we conduct experiments to evaluate the effectiveness of the tuned software prefetching. Results show that, compared to the baseline where no prefetching is applied, software prefetching delivers an average speedup of 1.08x (up to 2.46x) and 1.16x (up to 1.88x) for SPECint 2006 and SPECfp 2006 benchmark suite, respectively. Moreover, software prefetching outperforms hardware prefetching for both benchmark suites. This demonstrates the efficacy of software data prefetching. © 2022 IEEE.

关键词： Degradation Costs Multicore processing Prefetching Computational modeling Benchmark testing Signal processing

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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5 ExaFlop/s HPL-MxP Benchmark with Linear Scalability on the 40-Million-Core Sunway Supercomputer 23

5 ExaFlop/s HPL-MxP Benchmark with Linear Scalability on the...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Lin, Rongfen Yuan, Xinhui Xue, Wei Yin, Wanwang Yao, Jienan Shi, Junda Sun, Qiang Song, Chaobo Wang, Fei National Research Center of Parallel Computer Engineering & Technology Beijing China Tsinghua University BNRist Beijing China Qinghai University Xining China Tsinghua University Beijing China

ISBN: (纸本)9798400701092

HPL-MxP is an emerging high performance benchmark used to measure the mixed-precision computing capability of leading supercomputers. In this work, we present our efforts on the new Sunway that linearly scales the benchmark to over 40 million cores, sustains an overall mixed-precision performance exceeding 5 ExaFlop/s, and achieves over 85% of peak performance, which is the highest efficiency reached among all heterogeneous systems on the HPL-MxP list. The optimizations of our HPL-MxP implementation include the following: (1) a Two-Direction Look-Ahead and Overlap algorithm that enables overlaps of all communications with computation;(2) a multi-level process-mapping and communication scheduling method that uses the entire network as best as possible while maintaining conflict-free algorithm-flow;and (3) a CG-Fusion computing framework that eliminates up to 60% of inter-chip communications and removes the memory access bottleneck while serving both computation and communication simultaneously. This work could also provide useful insights for tuning cutting-edge applications on Sunway supercomputers as well as other heterogeneous supercomputers. © 2023 Owner/Author(s).

关键词： Linear algebra

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Multilevel Load Balancing Algorithm for Domestic Heterogeneous Manycore Architecture

Multilevel Load Balancing Algorithm for Domestic Heterogeneo...

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International Symposium on parallel and Distributed Processing with Applications, ISPA

作者： Yi Ma Xin Chen Heng Guo Fang Li Xin Liu Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Load imbalance often occurs in particle-in-cell simulations on parallel computing, which seriously affects the efficiency of applications. Due to the characteristics of multilevel parallelism and communication asymmetry of compute nodes in domestic heterogeneous manycore architecture, the impact of load imbalance is more prominent. The paper proposes a multilevel load-balancing algorithm for domestic heterogeneous manycore architecture. Inside the supernode, computing tasks are redivided based on manycore acceleration. Between the supernodes, a greedy-based communication mode is designed to minimize communication across supernodes. The experimental results show that the proposed algorithm achieves almost ideal dynamic load balance, and improves the performance of the evaporation module in two-phase flow simulation by 10.9-19.7 times for the 50 million-sized grid.

关键词： Heuristic algorithms Computational modeling computer architecture parallel processing Load management Nonhomogeneous media Supercomputers Evaporation Optimization Load modeling

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Flocking Modeling and Robustness Evaluation Based on Heterogeneous Network

Flocking Modeling and Robustness Evaluation Based on Heterog...

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Chinese Control and Decision Conference, CCDC

作者： Yude Fu Xiaojun Duan Xiang Li Xu Han Juan Deng Qizi Huangpeng College of Science National University of Defense Technology Changsha China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Considering the complexity and heterogeneity of agents in the flocking system and the diversity of relationships between agents with different dynamics. This paper initially proposes a flocking system modeling method based on heterogeneous networks, grounded in the Cucker-Smale flocking model with a leader-follower mechanism. Simulation experiments verify the superiority and reliability of the modeling method on three network topologies. Then, a flocking system robustness metric, improved Meta-Path(Im-MP), is proposed by combining the concept of Meta-Paths in heterogeneous networks. Finally, a comparison is made with the commonly used robustness metrics, and it is verified that the Im-MP metric is more suitable for evaluating the robustness of flocking systems.

关键词： Measurement Network topology Diversity reception Systems modeling Robustness Heterogeneous networks Biology

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Big Data Mining of Maritime Shipping Flows Based on Dependency Relationships

Big Data Mining of Maritime Shipping Flows Based on Dependen...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yude Fu Xiang Li Xiongyi Lu Mengjun Yu Qizi Huangpeng Xiaojun Duan College of Science National University of Defense Technology Changsha China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This research utilizes higher-order dependency relationships for the modeling of higher-order dependency networks from voluminous maritime flow data and introduces higher-order dependency mining to uncover critical port information in the China-Southeast Asia region as well as the coopetition relationships between ports. Analysis of maritime data from the years 2018 and 2023 indicates that global ports maintain stability and competitiveness through their evolution, with Singapore serving as the central hub connecting China and Southeast Asia. Subsequent analysis employing dependency paths indicates that in recent years, the dependency relationships among ports between China and Southeast Asia have achieved higher orders and greater complexity.

关键词： Asia Seaports Strategic planning Signal processing Data models Stability analysis Trajectory Data mining Optimization Logistics

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Short Range Visible Light Communication: LED Based Applications 5th

Short Range Visible Light Communication: LED Based Applicati...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Mishra, Sumita Grover, Jyoti Gupta, Nishu Electronics and Communication Engineering Department Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus India Computer Science and Engineering Department Malaviya National Institute of Technology Jaipur India VTT Technical Research Centre of Finland Ltd. Oulu Finland

ISBN: (纸本)9783031770746

Visible Light Communication (VLC) systems are capable of providing significantly higher data rates compared to traditional wireless technologies. Apart from high data rate, other advantages offered by VLC are license free spectrum, improved security, and no environmental radiation hazards. Light Emmiting Diode (LED) based VLC systems allow visible light to be used for indoor communications and high-precision positioning. But widespread deployment of VLC is limited due to several challenges such as layout modification to the current infrastructure, limited bandwidth of optical source, and the need to combat problems caused by ambient light and shadowing in VLC applications. This paper explores the current state of VLC and its various short range applications and presents an inclusive review on design challenges and their potential solutions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Radiation hazards

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Rapid simulations of atmospheric data assimilation of hourly-scale phenomena with modern neural networks 23

Rapid simulations of atmospheric data assimilation of hourly...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Li, Yiyuan Ju, Xiting Xiao, Yi Jia, Qilong Zhou, Yongxiao Qian, Simeng Lin, Rongfen Yang, Bin Shi, Shupeng Liu, Xin Gao, Jie Wang, Zhen Liu, Sha Tan, Jian Wang, Xuan Hu, Zhengding Yan, Limin Xue, Wei Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China National Research Center of Parallel Computer Engineering and Technology Wuxi China University of Science and Technology of China Hefei China Beijing Sankuai Online Technology Co. Ltd Beijing China Department of Computer Technology and Application Qinghai University Xining China

ISBN: (纸本)9798400701092

Atmospheric data assimilation is essential for numerical weather prediction. Ensemble data assimilation connects multiple instances of an atmospheric model through a Kalman filter-based algorithm, which is regarded as a challenging computing task today. In this work, we build a fast, low-cost, and scalable atmospheric data assimilation prototype, DIDA, for the new-generation Sunway supercomputer, including: (1) a framework that enables flexible deployment of components, and manages and optimizes data communication among modules, achieving maximum resource efficiency;(2) an accurate, robust, UNet-based surrogate model for atmospheric dynamic simulation to generate the background ensemble;(3) a batch-LETKF algorithm with high-performance eigenvalue decomposition, which is up to 7.37 times faster than existing numerical libraries while exhibiting almost linear scalability. Experimental evaluations show that our AI-integrated ensemble data assimilation prototype can complete hour-cycle assimilation in minutes, maintain linear scalability, and save an order of magnitude of computing resources, compared with the traditional method. © 2023 Owner/Author(s).

关键词： Supercomputers

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Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach

arXiv

引用

arXiv 2022年

作者： Yang, Yizhou Liu, Xin Zhejiang Lab China National Research Centre of Parallel Computer Engineering and Technology

Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising solution to automating the process of penetration tests by reducing human effort and improving reliability. Existing RL solutions focus on finding a specific attack path to impact the target hosts. However, in reality, a diverse range of attack variations are needed to provide comprehensive assessments of the target network's security level. Hence, the attack agents must consider multiple objectives when penetrating the network. Nevertheless, this challenge is not adequately addressed in the existing literature. To this end, we formulate the automatic penetration testing in the Multi-Objective Reinforcement Learning (MORL) framework and propose a Chebyshev decomposition critic to find diverse adversary strategies that balance different objectives in the penetration test. Additionally, the number of available actions increases with the agent consistently probing the target network, making the training process intractable in many practical situations. Thus, we introduce a coverage-based masking mechanism that reduces attention on previously selected actions to help the agent adapt to future exploration. Experimental evaluation on a range of scenarios demonstrates the superiority of our proposed approach when compared to adapted algorithms in terms of multi-objective learning and performance efficiency. © 2022, CC BY.

关键词： Deep learning

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