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International Joint Conference on Neural Networks (IJCNN)

作者： Rui Xia Jihu Guo Huajian Zhang Shun Yang Qinglin Wang Jie Liu College of Computer Science and Techonology National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various reordering algorithms, the selection of suitable reordering methods for various matrices has become an important research topic. In this paper, we propose a method to predict the optimal reordering method by visualizing sparse matrices in chunks in a parallel manner and feeding them into a deep convolutional neural network. The results show that the theoretical performance can reach 95% of the optimal performance, the prediction accuracy of the method can reach up to 85%, the parallel framework achieves an average speedup ratio of 11.35 times over the serial framework, and the performance is greatly improved compared with the traversal selection method on large sparse matrices.

关键词： Deep learning Visualization Accuracy Costs Neural networks parallel processing Prediction algorithms

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A Multi-agent Simulation of Escape Panic

A Multi-agent Simulation of Escape Panic

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International Joint Conference on Computational sciences and Optimization, CSO

作者： Chuanjun Ren Chenghui Yang Shiyao Jin National Laboratory for Parallel & Distributed Processing College of Computer Science National University of Defense Technology Changsha China National Laboratory for Parallel & Distributed Processinz National University of Defense Technology Changsha China National University of Defense Technology Changsha Hunan CN

The modeling and simulation on evacuation has recently become a topic of great interest. We present an agent-based model to construct crowd evacuations for emergency response from an area under an explosion. Various types of agents are designed as well as the interactions of them are concerned in contrast to traditional models in which the total populations are considered to be consisted of identical individuals and the interactions between them are omitted. Different cases are taken into account to test the effect of our model by iterative simulations. At last, plenty simulation results suggest several effective ways to minimize the harmful consequences of such life-threaten events.

关键词： Explosions Computational modeling Computer simulation Testing Accidents Humans Analytical models Concurrent computing Distributed computing Distributed processing

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International Conference on Computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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AUXILIARY-TASKS LEARNING FOR PHYSICS-INFORMED NEURAL NETWORK-BASED PARTIAL DIFFERENTIAL EQUATIONS SOLVING

arXiv

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arXiv 2023年

作者： Yan, Junjun Chen, Xinhai Wang, Zhichao Zhou, Enqiang 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

Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to capture solution-related features through neural networks. However, original PINNs often suffer from bottlenecks, such as low accuracy and non-convergence, limiting their applicability in complex physical contexts. To alleviate these issues, we proposed auxiliary-task learning-based physics-informed neural networks (ATL-PINNs), which provide four different auxiliary-task learning modes and investigate their performance compared with original PINNs. We also employ the gradient cosine similarity algorithm to integrate auxiliary problem loss with the primary problem loss in ATL-PINNs, which aims to enhance the effectiveness of the auxiliary-task learning modes. To the best of our knowledge, this is the first study to introduce auxiliary-task learning modes in the context of physics-informed learning. We conduct experiments on three PDE problems across different fields and scenarios. Our findings demonstrate that the proposed auxiliary-task learning modes can significantly improve solution accuracy, achieving a maximum performance boost of 96.62% (averaging 28.23%) compared to the original single-task PINNs. The code and dataset are open source at https://***/junjun-yan/ATL-PINN. Copyright © 2023, The Authors. All rights reserved.

关键词： Partial differential equations

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A Novel Deep Neural Network Model for Credit Risk Prediction of Chinese Farmers

A Novel Deep Neural Network Model for Credit Risk Prediction...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Yalong Xie Aiping Li Ziniu Liu Kai Chen Hongkui Tu College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665418164

China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic development and the improvement of people's standard of living that how to reasonably use funds to subsidize the agricultural population and reduce the risk of rural loans. At present, credit risk prediction of farmers mainly depends on the experience of experts in the business field, and there is little published research on using artificial intelligence methods to solve this problem. This paper presents a complete set of methods, including data collection, feature selection, etc. We propose a novel deep neural network model named DNN-CRP for credit risk prediction of Chinese framers. Experiments on an actual credit loan dataset of Chinese farmers are presented, and experimental results show that the comprehensive performance of the DNN-CRP model is better than current state-of-the-art models. It is believed that the DNN-CRP model proposed in this paper can help banks improve the efficiency of the credit loan business of farmers and reduce credit risks.

关键词： Deep learning Sensitivity Neural networks Sociology Predictive models Data collection Data models

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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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A distributed paging RAM grid system for wide-area memory sharing 06

A distributed paging RAM grid system for wide-area memory sh...

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International Symposium on parallel and Distributed processing (IPDPS)

作者： Rui Chu Nong Xiao Yongzhen Zhuang Yunhao Liu Xicheng Lu Distributed Processing National Key Laboratory for Parallel Changsha Hunan China Science and Technology Hong Kong University Kowloon Hong Kong China

ISBN: (纸本)9781424400546

Memory-intensive applications often suffer from the poor performance of disk swapping when memory is inadequate. Remote memory sharing schemes, which provide a remote memory that is faster than the local hard disk, are able to improve the performance of such applications. Due to the limitation of being applicable within single clusters only, however, most of the previous remote memory mechanisms, such as the network memory scheme, fail to be extendable into a large scale, distributed, heterogeneous, and dynamic environment. In this work, we propose a service-oriented grid memory sharing scheme, distributed paging RAM grid (DPRG). We study the properties and criteria of large scale memory sharing, and then design major operations and optimizations to fit the usage of grid systems. We collect trace from our grid environment, and evaluate DPRG through comprehensive trace-driven simulations. Results show that DPRG significantly outperforms existing remote memory sharing schemes and supports grid computing applications effectively

关键词： Read-write memory Random access memory Hard disks Meteorology Large-scale systems Grid computing Scientific computing Computer networks Distributed computing Laboratories

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

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

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2021年第四届算法、计算和人工智能国际会议

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

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 Co NLL04 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.

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

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A Survey on Approaches and Applications of Knowledge Representation Learning

A Survey on Approaches and Applications of Knowledge Represe...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Chenchen Li Aiping Li Ye Wang Hongkui Tu Yichen Song College of Computer National University of Defense Technology ChangSha China College of Computer Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology ChangSha China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized.

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A Novel Run-time Load Balancing Method for MapReduce

A Novel Run-time Load Balancing Method for MapReduce

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International Conference on Computer science and Network technology

作者： Zhihong Liu Yaping Liu Baosheng Wang Zhenghu Gong College of Computer National University of Defense Technology Changsha Hunan China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781467381741

In recent years, many companies are embracing the Hadoop MapReduce system for large-data processing with completion time constrains. However, exiting Hadoop schedulers still suffer from the reducer load imbalancing problem. In this paper, we present a novel run-time load balancing method for MapReduce. Our approach predicts the workload of each reduce task at run-time, and assigns the reduce tasks to specified machines based on the estimated workload of reduce tasks dynamically. Therefore, our approach can achieve load balance among machines. The experimental results show that our approach achieves high accuracy while predicting the workload of reduce tasks, and improves the job completion time by up to 23.15%.

关键词： MapReduce load balancing task scheduling Load balancing TASK SCHEDULING Completion time Runtime Workload Religious Missions

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