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21st IEEE International Symposium on parallel and Distributed processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yongtao Liu, Jie Xiao, Tiaojie Gong, Chunye National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Program for Frontier Equipment Changsha China National Supercomputer Center in Tianjin Tianjin China

ISBN: (纸本)9798350329223

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability. © 2023 IEEE.

关键词： Hash functions

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration 6

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

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Zhang, Zhe Liu, Jie Chi, Lihua Chen, Xinhai 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: (纸本)9781728186351

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%. © 2020 IEEE.

关键词： Recurrent neural networks

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Contrastive Learning with Diverse Samples 26

Contrastive Learning with Diverse Samples

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Lilei 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

ISBN: (纸本)9781643684369

Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent contrastive learning achievements. Current work mainly adopts instance discrimination as the pretext task, which treats every single instance as a different class (negative) and uses a collection of data augmentation techniques to generate more examples (positive) for each class. The idea is straightforward and efficient but will generally cause similar instances to be classified into different classes. Such problem has been defined as 'class collision' in some previous works and is shown to hurt the representation ability. Motivated by this observation, we present a solution to address this issue by filtering similar negative examples from each mini-batch. Concretely, we model the problem as a Determinantal Point Process (DPP) so that similar instances can be filtered stochastically, and diverse samples are expected to be sampled for contrastive training. Besides, we further introduce a priority term for each instance, which indicates the hardness of its positives, so that instances with more hard positives are more likely to be sampled for contributing to the optimization. Our sampling can be efficiently implemented in a feed-forward manner and further accelerated by our encouraged complement DPP. Extensive experimental results demonstrate our priority over the standard setup of contrastive learning. © 2023 The Authors.

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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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2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011

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

ISBN: (纸本)9781612844855

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. © 2011 IEEE.

关键词： Information management

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A construction technique of constant degree P2P systems towards efficient complex queries

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2011年第3期48卷 374-381页

作者： Wang, Xiaohai Peng, Yuxing Li, Dongsheng Key Laboratory of Science and Technology for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

Constant degree peer-to-peer (P2P) system is turning into the P2P domain's promising hotspot due to constant degree digraphs having good propertis. However, it is often hard to convert a stardard constant degree digraph to a DHT schema. Thus, most researches focus on DHT's construction and maintenance, while leaving optimization and supporting to complex query behind. Underlying topology affects upper-layers' character a lot. For constant degree P2P topologies, their inherent property makes a constant degree P2P system built using classical technique be poor in the data locality, and unfit for efficient, low-cost complex queries. Aiming at this shortage, a general-purpose construction technique towards efficient complex queries is proposed, which adds an embedding transformation layer between data layer and DHT overlay. In this way, adjacent data are stored in overlay's adjacent peers and the data locality is improved, so that the number of peers referred in complex queries can be minimized with a limited time overhead. To validate this technology, the first constant degree P2P system based on Kautz digraph FissionE is reconstructed as a typical example, which includes re-allocating of resources, query algorithm and locality maintenance strategies. Experimental results show that this construction technique can ensure data locality, reduce query cost and lead to systems' efficiency without changing the underlying DHT layer.

关键词： Peer to peer networks

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A knowledge graph embedding method based on neural network 5

A knowledge graph embedding method based on neural network

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

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

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

As the basis of many knowledge graph completion tasks, the embedding representation of entities and relations in knowledge graph (KG) is an important task in the fields of Natural Language processing (NLP) and Artificial Intelligence (AI). While most of the existing knowledge graph embedding (KGE) models based on convolutional neural network (CNN) can obtain abundant feature embedding, they may ignore an important fact that the triples in the KG come from the text, as they simply learn about the feature embedding of entities and relations without considering contextual information. Therefore, in this paper, we propose an effective KGE model based on neural network. First of all, we convert the triple (h, r, t) of the KG into a sentence [h r t]. Then, the LSTM neural network is used to learn the long-term dependence of sentences from the input feature vectors. Then, on this basis, the two-layer convolutional neural network with several different filters is used to extract different local features. Finally, the obtained feature vectors are connected together, and the inner product is carried out with the weight vectors to obtain the score of the triple, so as to judge the validity of the given triple. We evaluate our model on two benchmark datasets FB15k-237 and WN18RR, the experimental results show that the model can effectively improve the accuracy of link prediction, achieving better results compared with other baseline models. © 2020 IEEE.

关键词： Knowledge graph

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A survey on approaches and applications of knowledge representation learning 5

A survey on approaches and applications of knowledge represe...

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

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

ISBN: (纸本)9781728195582

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. © 2020 IEEE.

关键词： Knowledge representation

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80GBaud time division multiplexing optical transceiver

80GBaud time division multiplexing optical transceiver

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Integrated Photonics Research, Silicon and Nanophotonics, IPRSN 2016

作者： Zhang, Luo Mingche, Lai Zhengbin, Pang Liquan, Xiao Weixia, Xu School of Computer Science National University of Defense Technology Changsha410073 China Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

High baud rate optical transceiver based on time division multiplexing technology is proposed. A communication channel at 80GBoud with 4 bit streams at 20Gbps is realized by 4-stage cascaded high speed switches with s... 详细信息

ISBN: (纸本)9781943580149

关键词： Photonics

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Tree-structured bloom filters for joint optimization of false positive probability and transmission bandwidth

Tree-structured bloom filters for joint optimization of fals...

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ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2015

作者： Fu, Yongquan Biersack, Ernst Science and Technology Laboratory of Parallel and Distributed Processing School of Computer National Univ. of Defense Technology China Www.e-biersack.eu France

ISBN: (纸本)9781450334860

Bloom filters are frequently used to perform set queries that test the existence of some items. However, Bloom filters face a dilemma: the transmission bandwidth and the accuracy cannot be optimized simultaneously. This dilemma is particularly severe for transmitting Bloom filters to remote nodes when the network bandwidth is limited. We propose a novel Bloom filter BloomTree that consists of a tree-structured organization of smaller Bloom filters, each one using a set of independent hash functions. BloomTree spreads items across levels that are compressed to reduce the transmission bandwidth need. We investigate in detail under which conditions BloomTree performs better than the compressed Bloom filter and the standard Bloom filter. © Copyright 2015 ACM.

关键词： Hash functions

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Implementation of parallel sparse Cholesky factorization on GPU

Implementation of parallel sparse Cholesky factorization on ...

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International Conference on Computer science and Network technology (ICCSNT)

作者： Dan Zou Yong Dou National Laboratory for Parallel and Distribution Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467329637

Direct methods for solving large sparse symmetric positive-definite linear systems of equations are popular because of their generality and robustness. The main bottleneck is the sparse Cholesky factorization, which exhibits irregular memory access behavior and unbalanced workload. In the past 10 years, many sparse Cholesky factorization algorithms have emerged, exploiting new architectural features. However, programming techniques currently employed on these platforms are not sufficient to implement sparse Cholesky factorization on many-core graphics processing units (GPUs) due to mismatches between irregular problem structures and single-instruction multiple-thread GPU architectures. In the present paper, we propose a task-based software approach for the parallel sparse Cholesky factorization aimed at heterogeneous computing platforms with GPU accelerators. The tasks are generated by CPU. An efficient task-scheduling mechanism guarantees the correct ordering of task execution and ensures a load balanced execution on GPU. Comparisons are made with the existing solver using problems arising from a range of practical applications. The experiment results show that the proposed approach can substantially improve the performance of sparse Cholesky factorization on GPU with 2.7×-4× speedup.

关键词： Sparse Cholesky factorization GPU Cholesky factorisation Graphics processing Unit GRAPPER PICK UP Task execution

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