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作者： Chen, Xing Lin, Junxin Lin, Bing Xiang, Tao Zhang, Ying Huang, Gang College of Mathematics and Computer Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China College of Physics and Energy Fujian Normal University Fuzhou China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China

In the presence of scale, dynamism, uncertainty, and elasticity, cloud engineers face several challenges when allocating resources for cloud-based software services. They should allocate appropriate resources in order to guarantee good quality of services as well as low cost of resources. Self-adaptive ability is needed in this process because engineers' intervention is difficult. Traditional self-adaptive resource allocation methods are policy-driven. Thus, cloud engineers usually have to develop separate sets of rules for each systems in order to allocate resources effectively, which leads to high administrative cost and implementation complexity. Machine learning has made great achievements in many fields, and it can be also applied to resource allocation. In this paper, we present a self-learning and self-adaptive approach to resource allocation for cloud-based software services. For a given cloud-based software service, its QoS model is firstly trained on history data, which is capable to predict the QoS value as output by using the information on workload and allocated resources as inputs. Then, on-line decision-making on resource allocation can be carried out automatically based on genetic algorithm, which is aimed to search reasonable resource allocation plan by using the QoS model. We evaluate our approach on RUBiS benchmark, demonstrating the accuracy of the QoS model over 90% and the improvement of resource utilization by 10%-30%. © 2018 John Wiley & Sons, Ltd.

关键词： Resource allocation

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Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms

arXiv

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arXiv 2020年

作者： Mo, Haimiao Ding, Shuai Yang, Shanlin Vasilakos, Athanasios V. Zheng, Xi The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory of Big Data Distribution and Exchange Technologies China Grimstad Norway The College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China The School of Computing Macquarie University Sydney Australia

Monitoring the respiratory rate is crucial for helping us identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from the face and monitor breathing. However, the current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract high-quality respiratory signals. By gathering the data and choosing several regions of interest on the face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than rival approaches while transferring fewer data. © 2020, CC BY.

关键词： Edge computing

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Learning compact target-oriented feature representations for visual tracking

arXiv

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arXiv 2019年

作者： Li, Chenglong Huang, Yan Wang, Liang Tang, Jin Lin, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Center for Research on Intelligent Perception and Computing NLPR CASIA School of Data and Computer Science Sun Yat-Sen University

Many state-of-the-art trackers usually resort to the pre-trained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Target tracking

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Machine learning for modelling unstructured grid data in computational physics: a review

arXiv

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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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A two-phase approach for locally differentially private fuzzy co-clustering

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Information sciences 2025年 718卷

作者： Zhang, Pengfei An, Jianlong Cheng, Xiang Zhu, Youwen Zhang, Ji Zhang, Zhikun State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining No. 168 Shungeng Road Tianjiaan District Huainan232001 China School of Computer Science and Engineering Anhui University of Science and Technology No. 168 Shungeng Road Tianjiaan District Huainan232001 China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics No. 237 Longquan Road Yunnan Kunming650221 China No. 3501 Daxue Road Shandong Jinan255020 China Key Laboratory of Equipment Data Security and Guarantee Technology Guilin University of Electronic Technology No. 1 Jinji Road Guangxi Guilin541010 China State Key Laboratory of Networking and Switching Technology No. 10 Xitucheng Road Haidian District Beijing100876 China Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District Beijing100876 China College of Computer and Data Science Nanjing University of Aeronautics and Astronautics No. 29 Yudao Street Qinhuai District Jiangsu Nanjing210016 China School of Mathematics Physics and Computing University of Southern Queensland West Street ToowoombaQLD4350 Australia College of Computer Science and Technology Zhejiang University No. 38 Zheda Road Xihu District Hangzhou310027 China

Clustering analysis is important for providing personalized services across various user experience optimization. As an advanced clustering technique, Fuzzy Co-Clustering (FCC) excels at handling high-dimensional data by simultaneously grouping both samples and attributes while allowing objects to belong to multiple clusters. To alleviate the potential privacy risks posed by directly using raw data, researchers often employ local differential privacy (LDP) techniques for protection. However, current clustering studies under LDP either directly perform clustering on the high-dimensional data, which necessitates splitting the privacy budget heavily, leading to excessive noise injection, or fail to adequately address the impact of Laplacian noise on both objective function modeling and distance metric selection, resulting in reduced clustering accuracy. To address these limitations, we present a two-phase approach SONY, which allocates part of the privacy budget to evaluate attribute importance and perform dimensionality reduction accordingly, and then utilizes the remaining budget to conduct noise-aware clustering on the pruned data. Our key technical contributions include: (1) a Noise-aware Similarity Calculation method (NSC) that replaces traditional Euclidean distance with a specialized metric which accounts for the statistical properties of Laplacian noise;(2) an importance-driven dimension reduction and budget allocation method (IDRBA) which optimizes both attribute selection and privacy budget allocation;and (3) an outlier-aware objective function calculation method (OOC) which enhances robustness against potential noise-induced outliers. We provide theoretical guarantees for SONY's convergence, privacy preservation, utility bounds, and computational complexity. Experimental evaluations conducted on two real-world datasets and one synthetic dataset, demonstrate that SONY outperforms the state-of-the-arts by at least 14% in F-Measure and 10% in Entropy while main

关键词： data privacy

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CMRxRecon2024: A Multi-Modality, Multi-View K-Space dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Wang, Fanwen Qin, Chen Lyu, Jun Ouyang, Cheng Wang, Shuo Li, Yan Yu, Mengyao Zhang, Haoyu Guo, Kunyuan Shi, Zhang Li, Qirong Xu, Ziqiang Zhang, Yajing Li, Hao Hua, Sha Chen, Binghua Sun, Longyu Sun, Mengting Li, Qin Chu, Ying-Hua Bai, Wenjia Qin, Jing Zhuang, Xiahai Prieto, Claudia Young, Alistair Markl, Michael Wang, He Wu, Lianming Yang, Guang Qu, Xiaobo Wang, Chengyan Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Bioengineering and Imperial-X Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom Department of Electrical and Electronic Engineering & Imperial-X Imperial College London London United Kingdom Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computing Department of Brain Sciences Imperial College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China GE Healthcare Beijing China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Siemens Healthineers Ltd. Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University Shanghai China School of Engineering Pontificia Universidad Católica de Chile Santiago Chile School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Millenium Institute for Intelligent Healthcare Engineering Santiago Chile Department of Radiology Feinberg School of Medicine Northwestern University Chicago Unit

The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each o... 详细信息

关键词： Adversarial machine learning

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Multiview feature selection combining latent space and similarity structure learning

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Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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ADAM CHALLENGE: DETECTING AGE-RELATED MACULAR DEGENERATION FROM FUNDUS IMAGES

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Fu, Huazhu Sun, Xu Cao, Xingxing Lin, Fengbin Son, Jaemin Kim, Sunho Quellec, Gwenole Matta, Sarah Shankaranarayana, Sharath M. Chen, Yi-Ting Wang, Chuen-Heng Shah, Nisarg A. Lee, Chia-Yen Hsu, Chih-Chung Xie, Hai Lei, Baiying Baid, Ujjwal Innani, Shubham Dang, Kang Shi, Wenxiu Kamble, Ravi Singhal, Nitin Wang, Ching-Wei Lo, Shih-Chang Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The ADAM Challenge State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore CONICET Yatiris lab of Pladema Institute Tandil Argentina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Inserm UMR 1101 BrestF-29200 France Univ Bretagne Occidentale BrestF-29200 France Zasti India Pvt. Ltd. India Graphen.Inc Muen Biomedical and Optoelectronics Technologies Inc Taiwan Department of Electrical Engineering Indian Institute of Technology Rajasthan Jodhpur India Department of Electrical Engineering National United University Taiwan Institute of Data Science Department of Statistics National Cheng Kung University Taiwan Shenzhen University Shenzhen China SGGS Institute of Engineering and Technology Nanded India Suzhou Tisu Information Technology Co. Ltd China AIRA Matrix Mumbai India Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models. © 2022, CC BY-NC-ND.

关键词： Cost effectiveness

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An auto code generator for stencil on SW26010 21

An auto code generator for stencil on SW26010

引用

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhu, Xiaomin Zeng, Yunhui Wei, Yanjie Feng, Shengzhong Liu, Weiguo Balaji, Pavan Joint Engineering Research Center Health Big Data Intelligent Analysis Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shandong Computer Science Center National SuperComputer Center in Jinan Jinan China Sclool of Software Shandong University Jinan China Argonne National Laboratory United States

ISBN: (纸本)9781728120584

Stencil is a basic building block widely used in many HPC areas and applications. It generally dominates the time cost and is critical to the overall performance. Given that heterogeneous many-core is frequently adopted to build super computers, porting and optimizing stencil codes on modern accelerator-based architectures is important. Porting is non-trivial, and optimization is more difficult as it requires better understanding of the underlying architecture. As a result, auto tuning targeting on accelerators such as GPU becomes a hot research topic. In this paper, we focus on tuning stencil automatically on SW26010, which is a heterogeneous many-core CPU equipped in Sunway TaihuLight SuperComputer, and mainly work on 2D stencil variants code in ROMS (Regional Ocean Modeling System). The code generator can generate codes running on slave cores for 50+ different FORTRAN stencil-alike loops in ROMS in seconds, and optimization methods are integrated into the generator as well. The performance is better than the directive based method (OpenACC) and the hand-drafted code by a junior programmer, and it is comparable compared with the code written by a senior programmer with several years of programming experience. © 2019 IEEE.

关键词： Supercomputers

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