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International Conference on Grid and Cooperative Computing Workshops (GCCW)

作者： Kaijun Ren Nong Xiao Junqiang Song Tao Chen Weimin Zhang College of Science National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China

Meteorology grid computing aims to provide scientist with seamless, reliable, secure and inexpensive access to meteorological resources. However, the vision of achieving these goals has been encountered with many challenges such as the degree of easy-to-use and the capability of seamless automation. Fortunately, the emerging semantic Web service and semantic grid technologies bring some significant solutions for the above challenges. Semantic meteorology grid architecture (SMGA) is being developed by us to support automated meteorology grid service discovery, selection, and composition. In this paper, we present a model for semantic annotation and publication of meteorology grid services by combining the expressive power of the present ontology language and the advantages of semantic Web techniques. The presented model is an initial work to support automating the discovery, selection, and workflow composition of semantically described heterogeneous meteorological services, which offers the possibility of facilitating geographically distributed meteorological scientists to resolve complex scientific problems cooperatively

关键词： Meteorology Semantic Web Grid computing Web services Economic forecasting Weather forecasting Ontologies Power generation economics Environmental economics Laboratories

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Enhancing Code Representation Learning for Code Search with Abstract Code Semantics

Enhancing Code Representation Learning for Code Search with ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shaojie Zhang Yiwei Ding Enrui Hu Yue Yu Yu Zhang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Distributed and Parallel Software Lab Huawei Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Code representation learning is an important way to encode the semantics of source code through pre-training. The learned representation supports a variety of downstream tasks, such as natural language code search and code defect detection. Inspired by pre-trained models for natural language representation learning, existing approaches often treat the source code or its structural information (e.g., Abstract Syntax Tree or AST) as a plain token sequence. Unlike natural language, programming language has its unique code unit information (e.g., identifiers and expressions) and logic information (e.g., the functionality of a code snippet). To further explore those properties, we propose Abstract Code Embedding (AbCE), a self-supervised learning method that considers the abstract semantics of code logic. Instead of scattered tokens, AbCE treats an entire node or a subtree in an AST as a basic code unit during pre-training, which preserves the entirety of a coding unit. Moreover, AbCE learns the abstract semantics of AST nodes via a self-distillation way. Experimental results show that it achieves significant improvements over state-of-the-art baselines on code search tasks and comparable performance on code clone detection and defect detection tasks even without using contrastive learning or curriculum learning.

关键词： Representation learning Codes Source coding Semantics Natural languages Cloning Syntactics

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NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiandong Huang Qinglin Wang Shuyu Lu Ruochen Hao Songzhu Mei Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China University of Pittsburgh Pittsburgh USA

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 many-core 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.

关键词： Deep learning Recurrent neural networks Convolution Computer architecture Complexity theory Convolutional neural networks Kernel

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Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning

arXiv

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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Wei, Juhui Jian, Songlei Li, Yizhou Liu, Ning National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology China College of Science National University of Defense Technology China

Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a characteristic – scale – as data labels. By representing varied sub-vectors of data instances, we define scale as the relationship between the dimensionality of original sub-vectors and that of representations. Scales serve as labels attached to transformed representations, thus offering ample labeled data for neural network training. This paper further proposes a scale learning-based anomaly detection method. Supervised by the learning objective of scale distribution alignment, our approach learns the ranking of representations converted from varied subspaces of each data instance. Through this proxy task, our approach models inherent regularities and patterns within data, which well describes data "normality". Abnormal degrees of testing instances are obtained by measuring whether they fit these learned patterns. Extensive experiments show that our approach leads to significant improvement over state-of-the-art generative/contrastive anomaly detection methods. © 2023, CC BY.

关键词： Anomaly detection

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

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

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2022 International Conference on Cloud Computing, Big Data Applications and Software Engineering, CBASE 2022

作者： Liu, Pan Liu, Gongjie Liu, Jie Liu, Chencheng Xiong, Wenbin Tang, Zhangcun Zhou, Yinzhao Wang, Yonggang Yuan, Zhouming Beijng Paratera Technology Corp. Ltd Beijing100000 China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha410073 China National University of Defense Technology Laboratory of Software Engineering for Complex Systems Changsha410073 China Central South University of Forestry and Technology Changsha410000 China Nuclear and Radiation Safety Center Mee Beijing100000 China University of Electronic Science and Technology of China Chengdu610000 China Tianfu Innovation Energy Establishment Chengdu610000 China

ISBN: (纸本)9798350396393

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. © 2022 IEEE.

关键词： Big data

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DMSA: Decentralized and Multi-keyword Selective Data Sharing and Acquisition

DMSA: Decentralized and Multi-keyword Selective Data Sharing...

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International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Moheng Lin Peichang Shi Xiang Fu Feng Jiang Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sharing solutions still fail to address the simultaneous screening needs of both the sender and receiver with multi-keywords. Without the capability to support bilateral simultaneous filtering, the disclosure of reasons for matching failures could inadvertently expose sensitive user data. Therefore, the challenge lies in enabling ciphertexts with multiple keywords and receivers with multiple interests to achieve mutual and simultaneous matching. Based on the technical foundations of SE (Searchable Encryption), MABE (Multi-Attribute Based Encryption), and polynomial fitting, this paper proposes a scheme called DMSA (Decentralized and Multi-keyword selective Sharing and selective Acquisition). This scheme can satisfy soundness, enabling ciphertexts carrying multiple keywords and receivers representing multiple interests to match each other simultaneously. We conducted a security analysis that confirms the security of DMSA against chosen-plaintext attacks. Our experimental results demonstrate a significant efficiency improvement, with a 67% increase over single-keyword data-sharing schemes and a 16% enhancement compared to the existing multi-keyword data-sharing solution.

关键词： distributed processing Filtering Data security Keyword search Fitting Receivers Polynomials Data models Blockchains Encryption

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Blocking LU Decomposition for FPGAs

Blocking LU Decomposition for FPGAs

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Guiming Wu Yong Dou Gregory D. Peterson National Laboratory for Parallel & Distributed Processing National University of Defense Technology Changsha China Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA

To efficiently perform large matrix LU decomposition on FPGAs with limited local memory, the original algorithm needs to be blocked. In this paper, we propose a block LU decomposition algorithm for FPGAs, which is applicable for matrices of arbitrary size. We introduce a high performance hardware design, which mainly consists of a linear array of processing elements (PEs), to implement our block LU decomposition algorithm. A total of 36 PEs can be integrated into a Xilinx Virtex-5 xc5vlx330 FPGA on our self-designed PCI-Express card, reaching a sustained performance of 8.50 GFLOPS at 133 MHz, which outperforms previous work.

关键词： Field programmable gate arrays Matrix decomposition Hardware Power engineering computing Algorithm design and analysis Linear algebra Computer applications Concurrent computing distributed computing Laboratories

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An efficient two-level bitmap index for cloud data management

An efficient two-level bitmap index for cloud data managemen...

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International Conference on Communication Software and Networks, ICCSN

作者： Huang Bin Peng Yu-Xing School of Computer Science Wuhan University of China Wuhan China National Laboratory of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

A Cloud may be seen as a type of flexible computing infrastructure consisting of many compute nodes, where resizable computing capacities can be provided to different customers. To fully harness the power of the Cloud, efficient data management is needed to handle huge volumes of data and support a large number of concurrent end users. To achieve that, a scalable and high-throughput indexing scheme is generally required. Such an indexing scheme must support parallel search to improve scalability. In this paper, we present a bitmap based indexing scheme for efficient data processing in the Cloud. Our approach can be summarized as follows. First, we build a local bitmap index for each compute node which only indexes data residing on the node. Second, we organize the compute nodes as a structured overlay and each node maintains a portion of the global index for the whole different data. The global index is also bitmap index to indicate the node each data resides in. Third, all bitmaps are compressed by adopting run-length coding for reducing storage requirement. We conduct extensive experiments on a LAN, and the results demonstrate that our indexing scheme is dynamic, efficient and scalable.

关键词： TV

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Message from the general co-chairs

Proceedings - Fifth International Conference on Grid and Coo...

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Proceedings - Fifth International Conference on Grid and Cooperative Computing, GCC 2006 2006年 11-11页

作者： Lu, Xicheng Fortes, Jose A. B. Ni, Lionel M. National Laboratory for Parallel and Distributed Processing China University of Florida United States Hong Kong University of Science and Technology Hong Kong Hong Kong

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Towards Robust Ego-Centric Hand Gesture Analysis for Robot Control

Towards Robust Ego-Centric Hand Gesture Analysis for Robot C...

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2016 IEEE International Conference on Signal and Image Processing

作者： Hongyong Song Weijiang Feng Naiyang Guan Xuhui Huang Zhigang Luo Institute of Software College of Computer National University of Defense Technology Department of Computer Science and Technology College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Wearable device with an ego-centric camera would be the next generation device for human-computer interaction such as robot *** gesture is a natural way of egocentric human-computer *** this paper, we present an ego-centric multi-stage hand gesture analysis pipeline for robot control which works robustly in the unconstrained environment with varying *** particular, we first propose an adaptive color and contour based hand segmentation method to segment hand region from the egocentric *** then propose a convex U-shaped curve detection algorithm to precisely detect positions of *** parallelly, we utilize the convolutional neural networks to recognize hand *** on these techniques, we combine most information of hand to control the robot and develop a hand gesture analysis system on an i Phone and a robot arm platform to validate its *** result demonstrates that our method works perfectly on controlling the robot arm by hand gesture in real time.

关键词： ego-centric vision hand detection and segmentation fingertips detection hand gesture recognition robot control human-computer interaction

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