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Electronic Information Engineering and Computer science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms

arXiv

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arXiv 2022年

作者： Zhuang, Jia-Xin Huang, Xiansong Yang, Yang Chen, Jiancong Yu, Yue Gao, Wei Li, Ge Chen, Jie Zhang, Tong Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Electronic and Computer Engineering Peking University China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China

In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSpore implementations and results on several benchmark datasets. The source codes and models are available at https://***/ OpenMedIA. © 2022, CC BY-NC-SA.

关键词： Open systems

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance Computers

SSRN

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SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 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 School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

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DRAM failure prediction in AIOps: Empirical evaluation, challenges and opportunities

arXiv

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arXiv 2021年

作者： Wu, Zhiyue Xu, Hongzuo Pang, Guansong Yu, Fengyuan Wang, Yijie Jian, Songlei Wang, Yongjun Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology China University of Adelaide Australia

DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multi-source dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formulate the problem as a multi-class classification task and exhaustively evaluate seven popular/state-of-the-art classifiers on both the individual and multiple data sources. We then formulate the problem as an unsupervised anomaly detection task and evaluate three state-of-the-art anomaly detectors. Further, based on the empirical results and our experience of attending this competition, we discuss major challenges and present future research opportunities in this task. © 2021, CC BY.

关键词： Web services

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Deep learning for social network information cascade analysis: A survey 5

Deep learning for social network information cascade analysi...

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5th IEEE International Conference on Data science in Cyberspace, DSC 2020

作者： Gao, Liqun Zhou, Bin Jia, Yan Tu, Hongkui Wang, Ye Chen, Chenguang Wang, Haiyang Zhuang, Hongwu National University of Defense Technology College of Computer ChangSha China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (纸本)9781728195582

The phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data becoming overwhelmed in SNICA. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. In this paper, we provide a comprehensive survey of recent progress in applying deep learning techniques for SNICA. We first introduce related concepts and summarize the advantages of deep learning technology in SNICA. Then, we propose a general framework for deep learning technology, which applies to SNICA. Then, different SNICA application scenarios in the framework are classified and discussed according to user behavior analysis, information cascade analysis, rumor detection, and social network event analysis. Finally, we discuss the limitations of current work and suggest future directions. © 2020 IEEE.

关键词： Data mining

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A regularized magnetotelluric inversion with a minimum support gradient constraint

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Earthquake science 2020年第3期33卷 130-140页

作者： Junjun Zhou Xiangyun Hu Tiaojie Xiao Institute of Geophysics and Geomatics China University of GeosciencesWuhan 430000China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China

Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable *** traditional stabilizing functionals,in which a low-order differential operator is used,yield a smooth solution that may not be appropriate when anomalies occur in block *** some cases the focused imaging of a sharp electrical boundary is *** though various experiments have used stabilizing functionals that are suitable to obtain a clear and sharp boundary,such as the minimum support(MS)and the minimum gradient support(MGS)functionals,there are still some limitations in *** this paper,the minimum support gradient(MSG)is proposed as the stabilizing *** the uniform regularization framework,a regularized inversion with a variety of stabilizing functionals is performed and the inversion results are *** study shows that MSG inversion can not only obtain a clearly focused inversion but also a quite stable and robust one.

关键词： magnetotelluric focus inversion sharp boundary regulafization

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Neutronic calculations of the China dual-functional lithium–lead test blanket module with the parallel discrete ordinates code Hydra

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Nuclear science and Techniques 2020年第8期31卷 1-12页

作者： Guang-Chun Zhang Jie Liu Liang-Zhi Cao Hong-Chun Wu Xian-Bao Yuan Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China College of Mechanical and Power Engineering China Three Gorges UniversityYichang 443002China

The China dual-functional lithium–lead test blanket module(DFLL-TBM) is a liquid Li Pb blanket concept developed by the Institute of Nuclear Energy Safety technology of the Chinese Academy of sciences for testing in ITER to validate relevant tritium breeding and shielding technologies. In this study, neutronic calculations of DFLL-TBM were carried out using a massively parallel three-dimensional transport code, Hydra, with the Fusion Evaluated Nuclear Data Library/MG. Hydra was developed by the Nuclear Engineering Computational Physics Lab based on the discrete ordinates method and has been devoted to neutronic analysis and shielding evaluation for nuclear facilities. An in-house Monte Carlo code(MCX) was employed to verify the discretized calculation model used by Hydra for the DFLL-TBM calculations. The results showed two key aspects:(1) In most material zones,Hydra solutions are in good agreement with the reference MCX results within 1%, and the maximal relative difference of the neutron flux is merely 3%, demonstrating the correctness of the calculation model;(2) while the current DFLL-TBM design meets the operation shielding requirement of ITER for 4 years, it does not satisfy the tritium self-sufficiency requirement. Compared to the two-step approach, Hydra produces higher accuracies as it does not rely on the homogenization technique during the calculation process. The parallel efficiency tests of Hydra using the DFLL-TBM model also showed that this code maintains a high parallel efficiency on O(100) processors and, as a result, is able to significantly improve computing performance through parallelization. Parameter studies have been carried out by varying the thickness of the beryllium armor layer and the tritium breeding zone to understand the influence of the beryllium layer and breeding zone thickness on tritium breeding performance. This establishes a foundation for further improvement in the tritium production performance of DFLL-TBM.

关键词： Discrete ordinates method DFLL-TBM Neutronic analysis Tritium breeding performance

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MixedCache: Enabling Flow Directed Rule-Caching Scheme based on Heterogeneous Cache for OpenFlow 3

MixedCache: Enabling Flow Directed Rule-Caching Scheme based...

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2020 3rd International Conference on Computer Information science and Artificial Intelligence, CISAI 2020

作者： Yang, Jialun Yang, Yi Liu, Jiahao Liu, Rulin Qiao, Peng Wang, Baosheng College of Computer Science and Technology National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%. © Published under licence by IOP Publishing Ltd.

关键词： Static random access storage

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JointCloud Cross-chain Verification Model of Decentralized Identifiers

JointCloud Cross-chain Verification Model of Decentralized I...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Tao Zhong Peichang Shi Junsheng Chang Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Institute of Computer Technology College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665443326

When multiple entities communicate or collaborate in JointCloud, identities are the very prior basis to build trust with each other. Decentralized identifier (DID) can provide a trusted identity with blockchain technology and a complete method of identity verification based on verifiable credentials, which solves problems of conventional centralized identity. However, current DIDs can only conduct verification within a single blockchain, which limits the interoperability of DIDs on different blockchains. Network isolation hinders the verification of DIDs on different blockchains and thus there is a need to break the barrier between blockchains. In this paper, we propose a model to conduct cross-chain verification of DIDs. We build a system of credit evaluation to describe the credibility of DIDs in a unified way and deploy smart contracts to implement cross-chain verification of DIDs. Experimental results verifies the feasibility of the model, which realizes cross-chain verification of DIDs in the networks of blockchain.

关键词： Couplings Computational modeling Scalability Conferences Smart contracts Prototypes Data structures

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