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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

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Big data collaborative artificial intelligence and high-perf...

Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Pan Liu Gongjie Liu Jie Liu Chencheng Liu Wenbin Xiong Zhangcun Tang Yinzhao Zhou Yonggang Wang Zhouming Yuan Tianfu Innovation Energy Establishment Chengdu China Beijng PARATERA Technology Corp. Ltd Beijing China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Central South University of Forestry and Technology Changsha China Nuclear and Radiation Safety Center MEE Beijing China University of Electronic Science and Technology of China Chengdu China

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson’s criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach.

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Optimizing Winograd-Based Fast Convolution Algorithm on Phytium Multi-Core CPUs

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2020年第6期57卷 1140-1151页

作者： Wang, Qinglin Li, Dongsheng Mei, Songzhu Lai, Zhiquan Dou, Yong Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China

Convolutional neural networks (CNNs) have been extensively used in artificial intelligence fields such as computer vision and natural language processing. Winograd-based fast convolution algorithms can effectively reduce the computational complexity of convolution operations in CNNs so that they have attracted great attention. With the application of Phytium multi-core CPUs independently developed by the National University of Defense technology in artificial intelligence fields, there is strong demand of high-performance convolution primitives for Phytium multi-core CPUs. This paper proposes a new high-performance parallel Winograd-based fast convolution algorithm after studying architecture characteristics of Phytium multi-core CPUs and computing characteristics of Winograd-based fast convolution algorithms. The new parallel algorithm does not rely on general matrix multiplication routines, and consists of four stages: kernels transformation, input feature maps transformation, element-wise multiplication, and output feature maps inverse transformation. The data movements in all four stages have been collaboratively optimized to improve memory access performance of the algorithm. The custom data layouts, multi-level parallel data transformation algorithms and multi-level parallel matrix multiplication algorithm have also been proposed to support the optimization above efficiently. The algorithm is tested on two Phytium multi-core CPUs. Compared with Winograd-based fast convolution implementations in ARM Computer Library (ACL) and NNPACK, the algorithm can achieve speedup of 1.05~16.11 times and 1.66~16.90 times, respectively. The application of the algorithm in the open source framework Mxnet improves the forward-propagation performance of the VGG16 network by 3.01~6.79 times. © 2020, science Press. All right reserved.

关键词： Program processors

Vision-Language Navigation: A Survey and Taxonomy

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arXiv 2021年

作者： Wu, Wansen Chang, Tao Li, Xinmeng Yin, Quanjun Zhu, Jiancheng The College of Systems Engineering National University of Defense Technology Hunan Changsha410073 China National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

Vision-Language Navigation (VLN) tasks require an agent to follow human language instructions to navigate in previously unseen environments. This challenging field involving problems in natural language processing, computer vision, robotics, etc., has spawn many excellent works focusing on various VLN tasks. This paper provides a comprehensive survey and an insightful taxonomy of these tasks based on the different characteristics of language instructions in these tasks. Depending on whether the navigation instructions are given for once or multiple times, this paper divides the tasks into two categories, i.e., single-turn and multi-turn tasks. For single-turn tasks, we further subdivide them into goal-oriented and route-oriented based on whether the instructions designate a single goal location or specify a sequence of multiple locations. For multi-turn tasks, we subdivide them into passive and interactive tasks based on whether the agent is allowed to question the instruction or not. These tasks require different capabilities of the agent and entail various model designs. We identify progress made on the tasks and look into the limitations of existing VLN models and task settings. Finally, we discuss several open issues of VLN and point out some opportunities in the future, i.e., incorporating knowledge with VLN models and implementing them in the real physical world. © 2021, CC BY.

关键词： Taxonomies

NUMA-aware FFT-based convolution on ARMv8 many-core CPUs

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arXiv 2021年

作者： Huang, Xiandong Wang, Qinglin Lu, Shuyu Hao, Ruochen Mei, Songzhu Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in manycore CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

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arXiv 2024年

作者： Li, Xin Yuan, Kun Pei, Yajing Lu, Yiting Sun, Ming Zhou, Chao Chen, Zhibo Timofte, Radu Sun, Wei Wu, Haoning Zhang, Zicheng Jia, Jun Zhang, Zhichao Cao, Linhan Chen, Qiubo Min, Xiongkuo Lin, Weisi Zhai, Guangtao Sun, JianHui Wang, Tianyi Li, Lei Kong, Han Wang, Wenxuan Li, Bing Luo, Cheng Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Kong, Fangyuan Fan, Haotian Xu, Yifang Xu, Haoran Yang, Mengduo Zhou, Jie Li, Jiaze Wen, Shijie Xu, Mai Li, Da Yao, Shunyu Du, Jiazhi Zuo, Wangmeng Li, Zhibo He, Shuai Ming, Anlong Fu, Huiyuan Ma, Huadong Wu, Yong Xue, Fie Zhao, Guozhi Du, Lina Guo, Jie Zhang, Yu Zheng, Huimin Chen, Junhao Liu, Yue Zhou, Dulan Xu, Kele Xu, Qisheng Sun, Tao Ding, Zhixiang Hu, Yuhang University of Science and Technology of China China Kuaishou Technology China University of Würzburg Germany Shanghai Jiao Tong University China Nanyang Technological University Singapore Xiaohongshu China WeChat China Tencent China Wuhan University China ByteDance Shenzhen China Zhejiang University China Department of Electronic and Information Engineering Beihang University Beijing China Southern University of Science and Technology China Harbin Institute of Technology China Beijing University of Posts and Telecommunications China China Merchants Bank China Shandong Jianzhu University China Key Laboratory for Parallel and Distributed Processing Changsha China Institute of Automation CAS China University of Chinese Academy of Sciences China

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-CVPR-NTIRE2024. Copyright © 2024, The Authors. All rights reserved.

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Outlier detection ensemble with embedded feature delection

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arXiv 2020年

作者： Cheng, Li Wang, Yijie Liu, Xinwang Li, Bin Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

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Frontiers of Computer science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

Operando Selenium Treatment Towards Enhanced Cyclic Stability for Single-Crystal Ni-Rich Cathode at Ultra-High Voltage of 4.7 V

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SSRN

SSRN 2023年

作者： Zhang, Zhi Ding, Xiang He, Xinyou Gong, Yang Xiao, Biaobiao Shen, Jixue Ou, Xing Engineering Research Center The Ministry of Education for Advanced Battery Materials School of Metallurgy and Environment Central South University Changsha410083 China Hunan Provincial Key Laboratory of Water Treatment Functional Materials Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraf College of Chemistry and Materials Engineering Hunan University of Arts and Science Changde415000 China Institute of Advanced Battery Materials and Devices Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China Tianmu Lake Institute of Advanced Energy Storage Technologies Co. Ltd Liyang213300 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China

Developing high-voltage (≥4.3 V vs Li/Li+) single-crystal Ni-rich LiNixCoyMn1-x-yO2 offers an enticing strategy to achieve high energy density for lithium-ion batteries. However, at high-voltage operation, the cathod... 详细信息

关键词： Single crystals

Clustering-preserving Network Flow Sketching

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Clustering-preserving Network Flow Sketching

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yongquan Fu Dongsheng Li Siqi Shen Yiming Zhang Kai Chen Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Network monitoring is vital in modern clouds and data center networks that need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash *** paper presents a clustering-preserving sketch method to be resilient to hash collisions. We provide an equivalence analysis of the sketch in terms of the K-means clustering. Based on the analysis result, we cluster similar network flows to the same bucket array to reduce the estimation variance and use the average to obtain unbiased estimation. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 10 3 to 10 5 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%.

关键词： Monitoring Arrays Estimation error Adaptation models Approximation error Data centers