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Improved algorithm for reconstructing singular connection in multi-block CFD applications

Transactions of Nanjing University of Aeronautics and Astronautics 2013年第SUPPL.1期30卷 51-57页

作者： Wang, Yongxian Zhang, Lilun Che, Yonggang Xu, Chuanfu Liu, Wei Liu, Huayong Wang, Zhenghua College of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China State Key Laboratory of Aerodynamics Mianyang 621000 China

An improved algorithm is proposed for the reconstruction of singular connectivity from the available pairwise connections during preprocessing phase. To evaluate the performance of the algorithm, an in-house computational fluid dynamics (CFD) code is used in which high-order finite-difference method for spatial discretization running on the Tianhe-1A supercomputer is employed. Test cases with a varied amount of mesh points are chosen, and the test results indicate that the improved singular connection reconstruction algorithm can achieve a speedup of 2000× at least when compared with the naive search method adopted in the former version of our code. Moreover, the parallel efficiency can benefit from the strategy of local communication based on the algorithm.

关键词： Finite difference method

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Hierarchical routing algorithm for ad hoc networks using mobile VMN

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International Journal of Autonomous and Adaptive Communications Systems 2016年第1-2期9卷 40-56页

作者： Yan, Guofeng Peng, Yuxing Liu, Junyi Chen, Shuhong School of Computer and Communication Hunan Institute of Engineering Xiangtan411101 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer National University of Defense Technology Changsha410073 China School of Information Science and Engineering Central South University Changsha410083 China

One of the most significant challenges introduced by routing protocol in mobile networks is coping with the unpredictable motion and the unreliable behaviour of mobile nodes. In this paper, we present a hierarchical routing algorithm based on virtual mobile node (VMN). A routing path can be found rapidly by exchanging path information between VMNs without accurate topology information. We discuss the routing process and the implementing details of the proposed routing algorithm. Finally, we evaluate the performance of our hierarchical routing algorithm through simulations, and the results show that the number of mobile WAVE generated by routing algorithm is well satisfied with the quality of service (QoS) requirements of all source and destination nodes. Furthermore, we compare the performance on VMN failure and message delivery ratio using hierarchical and non-hierarchical routing approaches, the results show that we can obtain magnitude better performance by HRA-VMN than hierarchical state routing (HSR) and non-hierarchical routing approach. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Routing algorithms

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Who Should Review this Pull-Request: Reviewer Recommendation to Expedite Crowd Collaboration

Who Should Review this Pull-Request: Reviewer Recommendation...

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Asia-Pacific Conference on Software Engineering

作者： Yue Yu Huaimin Wang Gang Yin Charles X. Ling National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China Department of Computer Science The University of Western Ontario London Ontario Canada

Github facilitates the pull-request mechanism as an outstanding social coding paradigm by integrating with social media. The review process of pull-requests is a typical crowd sourcing job which needs to solicit opinions of the community. Recommending appropriate reviewers can reduce the time between the submission of a pull-request and the actual review of it. In this paper, we firstly extend the traditional Machine Learning (ML) based approach of bug triaging to reviewer recommendation. Furthermore, we analyze social relations between contributors and reviewers, and propose a novel approach to recommend highly relevant reviewers by mining comment networks (CN) of given projects. Finally, we demonstrate the effectiveness of these two approaches with quantitative evaluations. The results show that CN-based approach achieves a significant improvement over the ML-based approach, and on average it reaches a precision of 78% and 67% for top-1 and top-2 recommendation respectively, and a recall of 77% for top-10 recommendation.

关键词： Communities Rails Training Software Encoding Mathematical model Social network services

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Deep Isolation Forest for Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Pang, Guansong Wang, Yijie Wang, Yongjun The College of Computer National University of Defense Technology Changsha410073 China The Science and Technology on Parallel and Distributed Processing Laboratory China The School of Computing and Information Systems Singapore Management University 178902 Singapore

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets;our model also inherits desired scalability from iForest. © 2022, CC BY.

关键词： Anomaly detection

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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IEEE International Conference on Artificial Intelligence and computer Applications (ICAICA)

作者： Zhenpeng Xu Xinhai Chen Lihua Chi Jie Liu Chunye Gong 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)9781728170053

ISBN: (纸本)9781728170060

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.

关键词： Convolution Computational fluid dynamics Convolutional neural networks Jacobian matrices Artificial intelligence distributed processing Software engineering

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Memory Access Analysis of Many-core System with Abundant Bandwidth

Memory Access Analysis of Many-core System with Abundant Ban...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Chuan Tang Dan Liu Zuocheng Xing Peng Yang Zhe Wang Jiang Xu Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781479986712

Many-core system is main architecture trend currently. One of the dominating challenges for on-chip manycore system is the memory wall. However traditional research primarily focus on the limited bandwidth. To solve this problem, many-core system is aided with large cache, and a lot of complex approaches about memory and cache are adopted aiming at relaxing the pressure of bandwidth and improving the efficiency of cache. All these methods generate much cost of area and power. In this paper, we are motivated by the feature of abundant bandwidth and low latency of optical interconnect. We analyze the characteristics of memory access on 64 cores system under the case of high bandwidth which can be assumed to benefit from optical interconnect, considering the sensibility with bandwidth and cache for different benchmarks. Finally, we discuss about promising basic frameworks suitable for manycore system with optical interconnect.

关键词： Bandwidth Benchmark testing Optical interconnections Random access memory Delays Hidden Markov models computers

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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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A Dynamic Replication Mechanism to Reduce Response-Time of I/O Operations in High Performance Computing Clusters

A Dynamic Replication Mechanism to Reduce Response-Time of I...

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IEEE International Conference on Social Computing (SocialCom)

作者： Ehsan Mousavi Khaneghah Seyedeh Leili Mirtaheri Lucio Grandinetti Amir Saman Memaripour Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781479915194

Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations' throughput will bound the system performance to storage and retrieval operations and remarkably reduce the overall performance of high performance computing clusters. File replication is a way to improve the performance of I/O operations and increase network utilization by storing several copies of every file. Furthermore, this will lead to a more reliable and fault-tolerant storage cluster. In order to improve the response time of I/O operations, we have proposed a mechanism that estimates the required number of replicas for each file based on its popularity. Besides that, the remaining space of storage cluster is considered in the evaluation of replication factors and the number of replicas is adapted to the storage state. We have implemented the proposed mechanism using HDFS and evaluated it using MapReduce framework. Evaluation results prove its capability to improve the response time of read operations and increase network utilization. Consequently, this mechanism reduces the overall response time of read operations by considering files' popularity in replication process and adapts the replication factor to the cluster state.

关键词： Time factors High performance computing Bandwidth Reliability Throughput System performance History

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Orthogonal Nonnegative Locally Linear Embedding

Orthogonal Nonnegative Locally Linear Embedding

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lei Wei Naiyang Guan Xiang Zhang Zhigang Luo Dacheng Tao National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China These authors contributed equally to this paper National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China Centre for Quantum Computation and Intelligence Systems and Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479906505

Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance between X and WH. NMF has been widely used in pattern recognition, data mining and computer vision because the non-negativity constraints on both W and H usually yield intuitive parts-based representation. However, NMF suffers from two problems: 1) it ignores geometric structure of dataset, and 2) it does not explicitly guarantee partsbased representation on any datasets. In this paper, we propose an orthogonal nonnegative locally linear embedding (ONLLE) method to overcome aforementioned problems. ONLLE assumes that each example embeds in its nearest neighbors and keeps such relationship in the learned subspace to preserve geometric structure of a dataset. For the purpose of learning parts-based representation, ONLLE explicitly incorporates an orthogonality constraint on the learned basis to keep its spatial locality. To optimize ONLLE, we applied an efficient fast gradient descent (FGD) method on Stiefel manifold which accelerates the popular multiplicative update rule (MUR). The experimental results on real-world datasets show that FGD converges much faster than MUR. To evaluate the effectiveness of ONLLE, we conduct both face recognition and image clustering on real-world datasets by comparing with the representative NMF methods.

关键词： Nonnegative matrix factorization Locally linear embedding Fast gradient descent Stiefel manifold Dataset Nonnegative matrix decomposition geometrical construction NMF protocol images embedding Flue gas desulfurization Nearest neighbor Orthogonal

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A mathematical model for empowerment of Beowulf clusters for exascale computing

A mathematical model for empowerment of Beowulf clusters for...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Seyedeh Leili Mirtaheri Ehsan Mousavi Khaneghah Lucio Grandinetti Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be useful for exascale computing. Processes running these applications may well demand unpredictable requirements and changes to system configuration and capabilities at runtime, thereby requiring fast system response without sacrificing the transparency and integrity of the reconfigured empowered system that is running on a heterogeneous platform. While a challenge in and of itself, platform heterogeneity is both useful and instrumental in the handling of unpredictable requests. The realization of such a dynamically reconfigurable and heterogeneous HPC cluster system for exascale computing requires a model to guide running processes to determine if they need empowerment of the current cluster, and if yes, by how much. To show the feasibility of empowerment of traditional HPC clusters for exascale computing, we have selected Beowulf as a noble candidate cluster and present a mathematical model for the empowerment of Beowulf clusters for exascale computing (EBEC). We have developed the model in line with Beowulf's cluster approach and by using vector space algebra. In contrast to traditional hardware-oriented approaches to improvise the performance of clusters, we use a software approach to the development of the proposed model by emphasizing processes, which act as the creators of the cluster and thus should decide on system (re)configuration, as the principal building blocks of the system. We have also adopted a new approach to heterogeneity by considering heterogeneity at different levels including hardware, system software, application software, and system functionality. In addition to support for heterogeneity and dynamic reconfiguration, the proposed model includes support for scalability that is crucial to exascale computing too.

关键词： Vectors Mathematical model Hardware Runtime computers Computational modeling

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