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Resource Allocation with Dynamic Substrate Network in Data Centre Networks

China Communications 2013年第9期10卷 130-142页

作者：李小玲王怀民丁博李小勇刘惠 National Key Laboratory for Parallel and Distributed Processing School of Computer ScienceNational University of Defense Technology

The resource allocation problem in data centre networks refers to a map of the workloads provided by the cloud users/tenants to the Substrate Network(SN)which are provided by the cloud *** studies consider the dynamic arrival and departure of the workloads,while the dynamics of the substrate are *** this paper,we first propose the resource allocation with the dynamic SN,and denote it as ***,we propose an efficient mapping algorithm for *** performance of the proposed algorithm is evaluated by performing simulation *** results show that the proposed algorithm can effectively solve the GraphMap-DS.

关键词： resource allocation data centrenetwork workload substrate algorithm

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SAWSDL service discovery based on Fine-Grained data semantics

SAWSDL service discovery based on Fine-Grained data semantic...

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作者： Wei, Dengping Wang, Ting Wang, Ji School of Computer National University of Defense Technology 410073 Changsha China National Laboratory for Parallel and Distributed Processing 410073 Changsha China

With the aim to improve the effectiveness of SAWSDL service discovery, this paper proposes a novel discovery method for SAWSDL services, which is based on the matchmaking of so-called finegrained data semantics that is defined via sets of atomic elements with built-in data types. The fine-grained data semantics can be obtained by a transformation algorithm that decomposes parameters at message level into a set of atomic elements, considering the characteristics of SAWSDL service structure and semantic annotations. Then, a matchmaking algorithm is proposed for the matching of fine-grained data semantics, which avoids the complex and expensive structural matching at the message level. The fine-grained data semantics is transparent to the specific data structure of message-level parameters, therefore, it can help to match successfully similar Web services with different data structures of parameters. Moreover, a comprehensive measure is proposed by considering together several important components of SAWSDL service descriptions at the same time. Finally, this method is evaluated on SAWSDL service discovery test collection SAWSDL-TC2 and compared with other SAWSDL matchmakers. The experimental results show that our method can improve the effectiveness of SAWSDL service discovery with low average query response time. The results imply that fine-grained parameters fit to represent the data semantics of SAWSDL services, especially when data structures of parameters are not important for semantics. © 2011 The Institute of Electronics, Information and Communication Engineers.

关键词： Data structures

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Coordinate strip-mining and kernel fusion to lower power consumption on GPU

Coordinate strip-mining and kernel fusion to lower power con...

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作者： Wang, Guibin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9783981080179

Although general purpose GPUs have relatively high computing capacity, they also introduce high power consumption compared with general purpose CPUs. Therefore low-power techniques targeted for GPUs will be one of the most hot topics in the future. On the other hand, in several application domains, users are unwilling to sacrifice performance to save power. In this paper, we propose an effective kernel fusion method to reduce the power consumption for GPUs without performance loss. Different from executing multiple kernels serially, the proposed method fuses several kernels into one larger kernel. Owing to the fact that most consecutive kernels in an application have data dependency and could not be fused directly, we split large kernel into multiple slices with strip-mining method, then fuse independent sliced kernels into one kernel. Based on the CUDA programming model, we propose three different kernel fusion implementations, with each one targeting for a special case. Based on the different strip-ming methods, we also propose two fusion mechanisms, which are called invariant-slice fusion and variant-slice fusion. The latter one could be better adapted to the requirements of the kernels to be fused. The experimental results validate that the proposed kernel fusion method could effectively reduce the power consumption for GPU. © 2011 EDAA.

关键词： Electric power utilization

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Learning from internet: Handling uncertainty in robotic environment modeling 17

Learning from internet: Handling uncertainty in robotic envi...

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Li, Yiying Wang, Huaimin Ding, Bo Che, Huimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)1595930361

Uncertainty is a great challenge for environment perception of autonomous robots. For instance, while building semantic maps (i.e., maps with semantic labels such as object names), the robot may encounter unexpected objects of which it has no knowledge. It will lead to inevitable failures in traditional environment modeling software. The abundant knowledge being accumulated on the Internet has the potential to assist robots to handle such kind of uncertainty. However, existing researches have not touched this issue yet. This paper proposes a cloud-based semantic mapping engine named SemaCloud, which can not only augment robot's environment modeling capability by the rich cloud resources but also cope with uncertainty by utilizing the Internet knowledge on necessary. It adopts a state-of-art Deep Neural Network (DNN) for real-time and accurate recognition of pre-trained objects. If an object is beyond the knowledge of this DNN, a special mechanism named QoS-aware cloud phase transition is triggered to seek help from existing recognition services on the Internet. By a set of carefullydesigned algorithms, it can maximize benefits and minimize the negative impacts on the Quality of Service (QoS) properties of robotic applications, which is essential to many robot scenarios. The experiments on both open datasets and real robots show that our work can handle uncertainty successfully in robotic semantic mapping without sacrificing critical real-time constraints. © 2017 Association for Computing Machinery.

关键词： Deep neural networks

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms

CD-Sched: An Automated Scheduling Framework for Accelerating...

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2023 International Conference on Power, Communication, Computing and Networking Technologies, PCCNT 2023

作者： Xiao, Yuanyuan Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology Changsha China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%. © 2023 ACM.

关键词： Deep neural networks

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Detecting duplicate pull-requests in GitHub 17

Detecting duplicate pull-requests in GitHub

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Li, Zhixing Yin, Gang Yu, Yue Wang, Tao Wang, Huaimin National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)1595930361

The widespread use of pull-requests boosts the development and evolution for many open source software projects. However, due to the parallel and uncoordinated nature of development process in GitHub, duplicate pull-requests may be submitted by different contributors to solve the same problem. Duplicate pull-requests increase the maintenance cost of GitHub, result in the waste of time spent on the redundant effort of code review, and even frustrate developers' willing to offer continuous contribution. In this paper, we investigate using text information to automatically detect duplicate pull-requests in GitHub. For a new-arriving pull-request, we compare the textual similarity between it and other existing pull-requests, and then return a candidate list of the most similar *** evaluate our approach on three popular projects hosted in GitHub, namely Rails, Elasticsearch and ***. The evaluation shows that about 55.3% - 71.0% of the duplicates can be found when we use the combination of title similarity and description similarity. © 2017 Association for Computing Machinery.

关键词： Open source software

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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Geng, Mingyang Li, Yiying Ding, Bo Wang, And Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)9781509060146

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learningbased approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot visionbased trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the singlerobot system. © 2018 IEEE.

关键词： Multipurpose robots

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Unsupervised feature learning for human activity recognition using smartphone sensors 2

Unsupervised feature learning for human activity recognition...

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2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014

作者： Li, Yongmou Shi, Dianxi Ding, B. Liu, Dongbo National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783319138169

Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from nonadaptability to the particular dataset. To alleviate the problems of handcrafted features, we present a feature extraction framework which exploits different unsupervised feature learning techniques to learning useful feature representation from accelerometer and gyroscope sensor data for human activity recognition. The unsupervised learning techniques we investigate include sparse auto-encoder, denoising auto-encoder and *** evaluate the performance on a public human activity recognition dataset and also compare our method with traditional features and another way of unsupervised feature learning. The results show that the learned features of our framework outperform the other two methods. The sparse auto-encoder achieves better results than other two techniques within our framework. © Springer International Publishing Switzerland 2014.

关键词： Signal encoding

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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parallelization of the training for face detection with transactional memory

Parallelization of the training for face detection with tran...

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2011 International Conference on Automation and Robotics, ICAR 2011

作者： Zeng, Kun Key Laboratory and Technology for National Defence of Parallel and Distributed Processing School of Computer National Univiersity of Defense Technology ChangSha Hunan China

ISBN: (纸本)9783642256455

The development of multi-core processor makes the parallelization of traditional sequential algorithms increasingly important. Meanwhile, transactional memory serves a good parallel programming model. This paper takes the advantage of software transactional memory to parallelize the Multi-Exit Asymmetric Adaboost algorithm for face detection. The parallel version is evaluated on three different implementations of software transactional memory. The experiment results show that the transactional memory based parallelization outperforms the traditional lock based approach. A speedup of nearly seven is achieved on a eight-core machine on an eight-core system. © 2011 Springer-Verlag.

关键词： Face recognition

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