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2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012

作者： Fu, Yongquan Wang, Yijie National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Hunan province 410073 China

ISBN: (纸本)9781467326995

distributed social networks have emerged recently. Nevertheless, recommending friends in the distributed social networks has not been exploited fully. We propose FDist, a distributed common-friend estimation scheme that estimates the number of common-friends between any pair of users without disclosing the friends' information. FDist uses privacy-preserving common-friend measurements to collect a small number of common-friend samples, and uses low-dimensional coordinates to estimate the number of common friends to other users. Simulation results on real-world social networks confirm that FDist is both scalable and accurate. © 2012 IEEE.

关键词： Social networking (online)

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AUModel: A conceptual model for adaptive software 5

AUModel: A conceptual model for adaptive software

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2014 5th IEEE International Conference on Software Engineering and Service science, ICSESS 2014

作者： Liu, Hui Ding, Bo Shi, Dianxi Wang, Huaimin National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781479932788

Pervasive software should be able to adapt itself to the changing environments and user requirements. Obviously, it will bring great challenges to the software engineering practice. This paper proposes AUModel, a conceptual model for adaptive software, which takes adaptability as an inherent feature and can act as the foundation of the engineering process. By introducing AUModel, the reuse of software adaptation infrastructure as well as the separation of adaptation concerns are enabled, which can facilitate both the development and maintenance of adaptive software. This paper also presents our initial attempts to realize this model, including a middleware prototype to support this model and an application to validate its effectiveness. © 2014 IEEE.

关键词： Middleware

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MixedCache: Enabling Flow Directed Rule-Caching Scheme based on Heterogeneous Cache for OpenFlow 3

MixedCache: Enabling Flow Directed Rule-Caching Scheme based...

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2020 3rd International Conference on Computer Information science and Artificial Intelligence, CISAI 2020

作者： Yang, Jialun Yang, Yi Liu, Jiahao Liu, Rulin Qiao, Peng Wang, Baosheng College of Computer Science and Technology National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%. © Published under licence by IOP Publishing Ltd.

关键词： Static random access storage

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Efficiently indexing sparse wide tablesin cloud computing

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Information technology Journal 2013年第20期12卷 5415-5423页

作者： Huang, Bin Peng, Yuxing Department of Computer Huaihua University 418008 Huaihua China School of Computer Science Wuhan University 430072 Wuhan China National Laboratory of Parallel and Distributed Processing National University of Defense Technology 410073 Changsha China

For its perception of unlimited resources and infinite scalability, Cloud Computing has emerged as a pervasive paradigm for hosting? data-centric applications in large computing infrastructures. The data produced by these applications are essentially sparse and wide and may change schema frequently, traditional relational data model is inappropriate for their data management. A new data model, called Sparse Wide Table, was introduced for this task. Unfortunately, we have to face many challenges in building the secondary index for Sparse Wide Table in cloud, as the distributed and column-oriented storage which eliminates a number of NULLs. In this study, we present a three-level index scheme for efficient data processing in the Cloud. Our approach can be summarized as follows. First, we build an index for each column by which the records can be rebuilt easily. Second, we build a bitmap index for each storage node which only indexes the data residing on the node. Third, we organize the storage nodes as a structured overlay and each node maintains a portion of the global index for the all different data. The global index is a bitmap index to indicate the node each data resides in. Finally, based on the three-level index scheme, some query algorithms are implemented. We conduct extensive experiments on a LAN and the results demonstrate that our indexing scheme is dynamic, efficient and scalable. © 2013 Asian Network for Scientific Information.

关键词： Cloud computing

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Optimization and parallelization Single Particle Cryo-EM Software RELION with GPU

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2018年第2期55卷 409-417页

作者： Su, Huayou Wen, Wen Li, Dongsheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Single particle cryo-electron microscopy (cryo-EM) is one of the most important methods of macromolecular structure. RELION (regularized likelihood optimization) is an open-source computer program for the refinement of macromolecular structures by single-particle analysis of cryo-EM data. Due to its easy usage and high quality results, RELION has attracted a lot of attentions from researchers. However, the computation requirement of this program is too huge to solve some large molecular structures with CPU, which harpers the popularization of RELION. In this paper, we characterize the algorithm of RELION and parallelize it with GPU. Firstly, the mathematical theory, computer patterns and performance bottlenecks of RELION are analyzed comprehensively. Then, we optimize the program targeting at fine-grained many-core architecture processor, such as GPU. We propose an efficient multi-level parallel model to utilize the powerful computation capacity of many-core processor. In order to achieve high performance, we reconstruct the data structure for GPU continues memory access. To avoid the limitation of GPU memory size, we implement an adaptive framework. The experimental results show that the proposed GPU based algorithm can achieve good performance. When compared with the CPU implementation, the speedup ratio of the application is more than 36 times, while the speedup ratio of compute-intensive algorithm is about 75X. Moreover, the testing results on multi GPUs show that the GPU based implementation has good scalability. © 2018, science Press. All right reserved.

关键词： Graphics processing unit

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Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPUs 22nd

Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPU...

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22nd International Conference on Algorithms and Architectures for parallel Processing, ICA3PP 2022

作者： Zhou, Tianyang Wang, Qinglin Yin, Shangfei Hao, Ruochen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031226762

K-Means algorithm is one of the most common clustering algorithms widely applied in various data analysis applications. Yinyang K-Means algorithm is a popular enhanced K-Means algorithm that avoids most unnecessary calculations using triangle inequality. However, Yinyang K-Means algorithm is time-consuming when the problem size is large. Due to the influence of performance and energy-efficiency, ARM CPUs have appeared in high performance computing. Therefore, it is very interesting to accelerate Yinyang K-Means algorithm on ARM CPUs. In this paper, we propose an efficient parallel implementation of Yinyang K-Means algorithm on ARMv8 many-core CPUs by means of vectorization, NUMA affinity memory optimization and data layout optimization. The experiment on two ARMv8 many-core CPUs has shown that our implementation can achieve up to 5.6 times faster than the open-source multi-threaded one of Yinyang K-Means algorithm. To the best of our knowledge, this is the first work that studies the optimization of Yinyang K-Means algorithms on ARMv8 CPUs. © 2023, Springer Nature Switzerland AG.

关键词： K-means clustering

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A distributed algorithm for SI transactions serializability in cloud computing

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Information technology Journal 2013年第24期12卷 8435-8445页

作者： Huang, Bin Peng, Yuxing Peng, Xiaoning School of Computer Science Wuhan University Wuhan 430072 China Department of Computer Huaihua University Huaihua 418008 China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

There are well known anomalies permitted by snapshot isolation that can lead to violations of data consistency by interleaving transactions that individually maintain consistency. Until now, there are some ways to prevent these anomalies only in single computer and there are not the corresponding solving methods in cloud computing. This paper describes our PDCC algorithm to detect cycles in a snapshot isolation dependency graph and abort transactions to break the cycle in cloud computing. The algorithm ensures serializable executions for SI transactions in cloud computing. Based on the transaction concurrency control of Percolator, we have implemented our algorithm in an open source cloud database system (HBase) and our performance study shows that PDCC throughput and scalability are good. © 2013 Asian Network for Scientific Information.

关键词： Cloud computing

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Fast Nonlocal Diffusion by Stacking Local Filters for Image Denoising 5th

Fast Nonlocal Diffusion by Stacking Local Filters for Image ...

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Qiao, Peng Sun, Weichu Dou, Yong Li, Rongchun Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology ChangshaHunan410073 China University of South China HengyangHunan421000 China

ISBN: (纸本)9789811501203

Many image denoising methods achieve state-of-the-art performance with little consideration of computation efficiency. With the popularity of high-definition imaging devices, these denoising methods do not scale well to the high-definition images. Therefore, a fast image denoising method is proposed to meet the demand of processing high-definition images. Based on the analysis of the distribution of the distance distrs between the similar patches and their reference patches from a semantic aspect, the large distrs s was found to occur while their contribution to the overall distribution was small. Therefore, the nonlocal filters was replaced in trainable non-local reaction diffusion (TNLRD) with local filters. For image with 4096 X 4096 resolution, the proposed method runs about 6 times faster than TNLRD via a single-thread CPU implementation. And the GPU implementation of the proposed method is about 10 times faster than the CPU implementation. Furthermore, the proposed model achieves competing denoising performance compared with TNLRD in terms of PSNR and SSIM. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Image denoising

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parallel Implementation of SHA256 on Multizone Heterogeneous Systems 21

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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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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