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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Wan, Qian Wei, Luona Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Hunan Changsha China College of Systems Engineering National University of Defense Technology Hunan Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9781450385053

Fully capturing contextual information and analyzing the association between entity semantics and type is helpful for joint extraction task: 1) The context can reflect the part of speech and semantics of entity. 2) The entity type is closely related to the relation between entities. Previous research used to simply embed the contextual information into shallow layer of the model, ignoring the association between entity semantics and type. In this paper, we propose a graph network with full-information modeling to explicitly model different-level information in the text. The contextual information of entity is dynamically embedded in each span representation to improve the reasoning ability. To capture the fine-grained association between the semantics and type of entity, the graph network uses the feature of entity types to generate edge information between different nodes. Experimental results show that our model outperforms previous models on the CoNLL04 dataset and obtains competitive results on the SciERC dataset in both entity recognition and relation extraction. Extensive additional experiments further verify the effectiveness of the model. © 2021 ACM.

关键词： Natural language processing systems

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Optimizing Irregular-Shaped Matrix-Matrix Multiplication on Multi-Core DSPs

arXiv

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arXiv 2022年

作者： Yin, Shangfei Wang, Qinglin Hao, Ruochen Zhou, Tianyang Mei, Songzhu Liu, Jie 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

General Matrix Multiplication (GEMM) has a wide range of applications in scientific simulation and artificial intelligence. Although traditional libraries can achieve high performance on large regular-shaped GEMMs, they often behave not well on irregular-shaped GEMMs, which are often found in new algorithms and applications of high-performance computing (HPC). Due to energy efficiency constraints, low-power multi-core digital signal processors (DSPs) have become an alternative architecture in HPC systems. Targeting multi-core DSPs in FTm7032, a prototype CPU-DSPs heterogeneous processor for HPC, an efficient implementation - ftIMM - for three types of irregular-shaped GEMMs is proposed. FtIMM supports automatic generation of assembly micro-kernels, two parallelization strategies, and auto-tuning of block sizes and parallelization strategies. The experiments show that ftIMM can get better performance than the traditional GEMM implementations on multi-core DSPs in FT-m7032, yielding on up to 7.2× performance improvement, when performing on irregular-shaped GEMMs. And ftIMM on multi-core DSPs can also far outperform the open source library on multi-core CPUs in FT-m7032, delivering up to 3.1× higher efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Digital signal processors

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Optimizing All-to-All Collective Communication on Tianhe Supercomputer

Optimizing All-to-All Collective Communication on Tianhe Sup...

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

作者： Jintao Peng Jie Liu Yi Dai Min Xie Chunye Gong National University of Defense Technology ChangSha China Science and Technology on Parallel and Distributed Processing Laboratory NUDT Changsha China Changsha and Laboratory of Software Engineering for Complex System NUDT Changsha China

All-to-all communication has a wide range of ap-plications in parallel applications like FFT. On most supercom-puters, there are multiple cores in a node. Message aggregation is an efficient method for smaller messages. Using multi-leader to aggregate message show significant improvement in intra-node overhead. However, compared to one-leader aggregation, existing multi-leader design incurs more message count and less aggregated message size. This paper proposes an Overlapped Multi-worker Multi-port all-to-all (OVALL) to scale the message size and parallelism of the aggregation algorithm. The algorithm exploits the all-to-all multi-core parallelism, concurrent commu-nication, and overlapping capabilities. Experiment results show that, compared to systems built-in MPI, OVALL'implementation achieves up to 5.9x or 18x speedup compared to MPI on different HPC systems. For the Fast Fourier Transform (FFT) application, OVALL is up to 2.7x (8192 cores, system A) or 5.6x (4800 cores, system B) faster compared to built-in MPI on peak performance.

关键词： Multicore processing Fast Fourier transforms Scalability Aggregates Supercomputers Libraries Pipeline processing

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科学研究的第五范式——以智能驱动的材料设计为例

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Engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li 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 National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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OHTMA:an optimized heuristic topology-aware mapping algorithm on the Tianhe-3 exascale supercomputer prototype

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Frontiers of Information technology & Electronic Engineering 2020年第6期21卷 939-949页

作者： Yi-shui LI Xin-hai CHEN Jie LIU Bo YANG Chun-ye GONG Xin-biao GAN Sheng-guo LI Han XU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China

With the rapid increase of the size of applications and the complexity of the supercomputer architecture,topology-aware process mapping becomes increasingly *** communication cost has become a dominant constraint of the performance of applications running on the *** avoid a bad mapping strategy which can lead to terrible communication performance,we propose an optimized heuristic topology-aware mapping algorithm(OHTMA).The algorithm attempts to minimize the hop-byte metric that we use to measure the mapping *** incorporates a new greedy heuristic method and pair-exchange-based *** reduces the number of long-distance communications and effectively enhances the locality of the *** results on the Tianhe-3 exascale supercomputer prototype indicate that OHTMA can significantly reduce the communication costs.

关键词： High-performance computing Topology mapping Heuristic algorithm

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Effective Anomaly Detection Based on Reinforcement Learning in Network Traffic Data

Effective Anomaly Detection Based on Reinforcement Learning ...

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International Conference on Parallel and distributed Systems (ICPADS)

作者： Zhongyang Wang Yijie Wang Hongzuo Xu Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo

关键词： Knowledge engineering Machine learning algorithms Conferences distributed databases Reinforcement learning Telecommunication traffic Network security

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Entropy and Autoencoder-Based Outlier Detection in Mixed-Type Network Traffic Data

Entropy and Autoencoder-Based Outlier Detection in Mixed-Typ...

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

作者： Zhongyang Wang Yijie Wang Zhenyu Huang Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data containing categorical and numerical features are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, the existing general algorithms cannot combine the characteristics of outliers in specific fields, leading to an unsatisfying performance in actual scenarios, such as the field of network security. This paper proposes a novel Entropy and Autoencoder-based Outlier Detection in mixed-type network traffic data, termed EAOD, which combines characteristics of outliers in specific fields and machine learning models to detect outliers. EAOD utilizes the expert rules made by domain knowledge summarized based on characteristics of existing outliers to label known outlier data. It performs holoentropy and deep autoencoder for the category and numerical feature spaces, respectively, in unlabeled data to obtain outlier scores integrated to get the final outlier scores via a dynamic integration strategy. Especially in the numerical feature space, to fully mine known outlier behavior patterns, deep autoencoders of outlier and normal types are constructed separately to capture unknown outliers jointly. Experiments show that EAOD significantly outperforms eight state-of-the-art outlier detectors on seven real network traffic datasets.

关键词： Telecommunication traffic Machine learning Detectors Network security Feature extraction Entropy Data models

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Parallelization of Fast Monte Carlo Dose Calculation for Radiotherapy Treatment Planning on the ARMv8 Architecture

Parallelization of Fast Monte Carlo Dose Calculation for Rad...

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International Conference on Information science and technology (ICIST)

作者： Chi Jin Qinglin Wang Yang Zhao Yong Dou Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Monte Carlo (MC) simulation plays a key role in radiotherapy. Since the simulation time of the MC program cannot fully meet the clinical requirements, we use the ARM-based FT-2000+ multi-core processor for parallelization, which provides an effective solution for accelerating MC dose calculation. In this paper, we implement and verify FT-DPM, which is an OpenMP-based MC Dose Planning Method on FT-2000+. FT-DPM utilizes the parallelism of MC simulation and the advantage of ARM architecture to achieve the parallelization on ARM architecture. Meanwhile, we optimize the original DPM program in terms of memory allocation, data structure and data type. The experiments show that, compared with the original DPM code, FT-DPM obtains very accurate results and reaches the maximum speedups of 155.94 times for the electron case. The parallel program based on FT-2000+ only takes 44.1 seconds to simulate the particle transport of 100 million times, showing good clinical application potential. In addition, the speedup and efficiency of FT-DPM running on different core counts are also discussed.

关键词： Monte Carlo methods Multicore processing Simulation Scalability Parallel processing Data structures Load management

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Attention-based Encoder-Decoder Recurrent Neural Networks for HTTP Payload Anomaly Detection

Attention-based Encoder-Decoder Recurrent Neural Networks fo...

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

作者： Shang Wu Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Attack payloads are often short segments hidden in HTTP requests; thus they can be found by HTTP payload anomaly detection. Deep learning models learn data features during training without manual feature extraction, and better performance has received more attention. Recurrent Neural Network models process sequences directly, which are widely used in payload anomaly detection. However, due to the gradient vanishing problem, RNN has limits on processing the long sequences. Meanwhile, RNN uses its final hidden state for detection and pays more attention to the content of the end of the payload. Besides, deep learning generally lacks interpretability. The paper proposes an unsupervised deep learning model for HTTP payload Anomaly Detection, namely Attention-based Encoder-Decoder Recurrent Neural Networks Anomaly Detection model (AEDRAD). AEDRAD utilizes the encoder-decoder RNN and attention mechanism to detect anomalies by reconstructing the original sequences. AEDRAD filters the fields of HTTP protocol that cannot exist anomalies, focusing on the suspicious segments. Through the encoder-decoder network, the normal payload can be well-reconstructed while the anomaly payload fails. With the attention mechanism, AEDRAD generates practical features for further anomaly detection from a global perspective. Meanwhile, it marks abnormal fragments visually, which is conducive to a subsequent analysis by experts. The experimental results show that AEDRAD significantly outperforms three state-of-the-art unsupervised algorithms on two real datasets.

关键词： Deep learning Training Recurrent neural networks Protocols Focusing Manuals Feature extraction

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Modeling Full Information with Graph Network for Joint Entity-Relation Extraction 21

Modeling Full Information with Graph Network for Joint Entit...

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Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Qian Wan Luona Wei Xinhai Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China College of Systems Engineering National University of Defense Technology China Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

ISBN: (纸本)9781450385053

关键词： Natural Language processing Named Entity Recognition Graph Network Deep Learning Relation Extraction

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