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Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming

作者： Lu, Kai Zhou, Xu Bergan, Tom Wang, Xiaoping Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China University of Washington Computer Science and Engineering United States

ISBN: (纸本)9781450326568

Multithreaded programs execute nondeterministically on conventional architectures and operating systems. This complicates many tasks, including debugging and testing. Deterministic multithreading (DMT) makes the output of a multithreaded program depend on its inputs only, which can totally solve the above problem. However, current DMT implementations suffer from a common inefficiency: they use frequent global barriers to enforce a deterministic ordering on memory accesses. In this paper, we eliminate that inefficiency using an execution model we call deterministic lazy release consistency (DLRC). Our execution model uses the Kendo algorithm to enforce a deterministic ordering on synchronization, and it uses a deterministic version of the lazy release consistency memory model to propagate memory updates across threads. Our approach guarantees that programs execute deterministically even when they contain data races. We implemented a DMT system based on these ideas (RFDet) and evaluated it using 17 parallel applications. Our implementation targets C/C++ programs that use POSIX threads. Results show that RFDet gains nearly 2x speedup compared with DThreads-a start-of-the-art DMT system. Copyright © 2014 ACM.

关键词： C++ (programming language)

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Efficient deterministic multithreading without global barriers

Efficient deterministic multithreading without global barrie...

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Multithreaded programs execute nondeterministically on conventional architectures and operating systems. This complicates many tasks, including debugging and testing. Deterministic multithreading (DMT) makes the output of a multithreaded program depend on its inputs only, which can totally solve the above problem. However, current DMT implementations suffer from a common inefficiency: they use frequent global barriers to enforce a deterministic ordering on memory accesses. In this paper, we eliminate that inefficiency using an execution model we call deterministic lazy release consistency (DLRC). Our execution model uses the Kendo algorithm to enforce a deterministic ordering on synchronization, and it uses a deterministic version of the lazy release consistency memory model to propagate memory updates across threads. Our approach guarantees that programs execute deterministically even when they contain data races. We implemented a DMT system based on these ideas (RFDet) and evaluated it using 16 parallel applications. Our implementation targets C/C++ programs that use POSIX threads. Results show that RFDet gains nearly 2x speedup compared with DThreads-a start-of-the-art DMT system. © 2014 ACM.

关键词： C++ (programming language)

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CLASSIFICATION OF LAND COVER BASED ON DEEP BELIEF NETWORKS USING POLARIMETRIC RADARSAT-2 DATA

CLASSIFICATION OF LAND COVER BASED ON DEEP BELIEF NETWORKS U...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Baoliang Li National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

ISBN: (纸本)9781479953141

Urban land use and land cover (LULC) classification is one of the core applications in Geographic Information System (GIS). In this paper, a novel classification approach based on Deep Belief Network (DBN) for detailed urban mapping is proposed. Deep Belief Network (DBN) is a widely investigated and deployed deep learning model. By applying the DBN model, effective spatio-temporal mapping features can be automatically extracted to improve the classification performance. Six-date RADARSAT-2 Polarimetric SAR (PolSAR) data over the Great Toronto Area were used for evaluation. Experimental results showed that the proposed method can outperform SVM and contextual approaches using adaptive MRF.

关键词： Land cover classification Deep learning Restricted Boltzmann Machines (RBMs) Deep Belief Network (DBN) PolSAR

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Location-Aware Multi-user Resource Allocation in distributed Clouds

Location-Aware Multi-user Resource Allocation in Distributed...

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10th Annual Conference of Advanced computer Architecture, ACA 2014

作者： Li, Jiaxin Li, Dongsheng Zheng, Jing Quan, Yong National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Information Center of Logistics Department Beijing China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9783662444900

Resource allocation for multi-user across multiple data centers is an important problem in cloud computing environments. Many geographically-distributed users may request virtualized resources simultaneously. And the distances from users to allocated resources have much impact on the quality of service (QoS) in multiple data centers environment. Most existing methods do not take all these factors into account when allocating resources. They usually result in poor runtime performance of users' virtual computing environment and the remarkable difference of users' QoS. In this paper, we propose RAMD, a resource allocation algorithm based on multi-stage decision in multiple data centers. The RAMD algorithm allocate VMs to users, taking into account the correlation and interaction between multiple users, so as to minimize the sum of all users' service distances (i.e. determined by user location and network distance of virtual machines). Experimental results show that the algorithm can effectively deal with the cloud resource allocation for multi-user across multiple data centers. It can improve the runtime performance of users' virtualized resources and reduce the difference of QoS. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Resource allocation

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IWFR:Exploiting Full-duplex in Wireless Relay Networks

IWFR:Exploiting Full-duplex in Wireless Relay Networks

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2014 4th IEEE International Conference on Information Science and Technology

作者： Yong Lu Shaohe Lv Xiaodong Wang Xingming Zhou National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology

Traditional wireless relay networks have large endto-end time delay and low throughput because of the limit that it can't receive and forward at the same *** this paper,we proposed IWFR:Immediate Wireless Full-Duplex Relay which exploits the advantages of full-duplex to shorten the end-to-end time delay and improve the *** the same time,we designed a new implicit acknowledgement mechanism,which can eliminate the ACK overheads and evidently improve the throughput of the *** implement IWFR,we also modified the full-duplex node architecture to make it support for immediate *** shows that IWFR shortens the end-to-end time delay by 60%on average and improves the throughput to 240%of the original relay.

关键词： Relay IWFR:Exploiting Full-duplex in Wireless Relay Networks

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Analysis and evaluation method for linpack benchmark

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Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 102-107页

作者： Du, Yun-Fei Yang, Can-Qun Wang, Feng Yi, Hui-Zhan School of Computer National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

The basic algorithm of HPL was introduced. Two optimization methods of communication, i.e., advanced-lookahead and dynamic broadcasting algorithm, were proposed. The performances of the two optimization methods were evaluated on the Tianhe-2 system, and compared with the performance of traditional HPL. The Linpack evaluating methods were discussed, the choice methods of the key parameters affecting the performance were given, and the effect of the high-speed interconnection network on Linpack performance was analyzed. The results showed that the higher the node performance, the greater the effect of the network bandwidth and latency on the performances of HPL. ©, 2014, Northeastern University. All right reserved.

关键词： Large scale systems

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IWFR: Exploiting full-duplex in wireless relay networks

IWFR: Exploiting full-duplex in wireless relay networks

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International Conference on Information Science and Technology (ICIST)

作者： Yong Lu Shaohe Lv Xiaodong Wang Xingming Zhou National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China

Traditional wireless relay networks have large end-to-end time delay and low throughput because of the limit that it can't receive and forward at the same time. In this paper, we proposed IWFR: Immediate Wireless Full-Duplex Relay which exploits the advantages of full-duplex to shorten the end-to-end time delay and improve the throughput. At the same time, we designed a new implicit acknowledgement mechanism, which can eliminate the ACK overheads and evidently improve the throughput of the relay. To implement IWFR, we also modified the full-duplex node architecture to make it support for immediate relay. Simulation shows that IWFR shortens the end-to-end time delay by 60% on average and improves the throughput to 240% of the original relay.

关键词： Wireless communication Throughput Delay effects Wireless sensor networks Interference Relay networks (telecommunications)

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DKNNS:Scalable and accurate distributed K nearest neighbor search for latency-sensitive applications

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Science China(Information Sciences) 2013年第3期56卷 123-139页

作者： FU YongQuan WANG YiJie National Key Laboratory for Parallel and Distributed Processing School of Computer ScienceNational University of Defense Technology

To reduce the access latencies of end hosts,latency-sensitive applications need to choose suitably close service machines to answer the access requests from end *** K nearest neighbor search locates K service machines closest to end hosts,which can efficiently optimize the access latencies for end *** work has weakness in terms of the accuracy and *** to the scalable and accurate K nearest neighbor search problem,we propose a distributed K nearest neighbor search method called DKNNS in this *** machines are organized into a locality-aware multilevel *** first locates a service machine that starts the search process based on a farthest neighbor search scheme,then discovers K nearest service machines based on a backtracking approach within the proximity region containing the target in the latency *** analysis,simulation results and deployment experiments on the PlanetLab show that,DKNNS can determine K approximately optimal service machines,with modest completion time and query ***,DKNNS is also quite stable that can be used for reducing frequent searches by caching found nearest neighbors.

关键词： latency sensitive network applications K nearest neighbor search network coordinate

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Soft-Constrained Nonnegative Matrix Factorization via Normalization

Soft-Constrained Nonnegative Matrix Factorization via Normal...

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International Joint Conference on Neural Networks

作者： Long Lan Naiyang Guan Xiang Zhang Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Semi-supervised clustering aims at boosting the clustering performance on unlabeled samples by using labels from a few labeled samples. Constrained NMF (CNMF) is one of the most significant semi-supervised clustering methods, and it factorizes the whole dataset by NMF and constrains those labeled samples from the same class to have identical encodings. In this paper, we propose a novel soft-constrained NMF (SCNMF) method by softening the hard constraint in CNMF. Particularly, SCNMF factorizes the whole dataset into two lower-dimensional factor matrices by using multiplicative update rule (MUR). To utilize the labels of labeled samples, SCNMF iteratively normalizes both factor matrices after updating them with MURs to make encodings of labeled samples close to their label vectors. It is therefore reasonable to believe that encodings of unlabeled samples are also close to their corresponding label vectors. Such strategy significantly boosts the clustering performance even when the labeled samples are rather limited, e.g., each class owns only a single labeled sample. Since the normalization procedure never increases the computational complexity of MUR, SCNMF is quite efficient and effective in practices. Experimental results on face image datasets illustrate both efficiency and effectiveness of SCNMF compared with both NMF and CNMF.

关键词： Matrix decomposition Encoding Vectors Clustering methods Clustering algorithms Time complexity Face

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Box-constrained Projective Nonnegative Matrix Factorization via Augmented Lagrangian Method

Box-constrained Projective Nonnegative Matrix Factorization ...

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International Joint Conference on Neural Networks

作者： Xiang Zhang Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Projective non-negative matrix factorization (PNMF) projects a set of examples onto a subspace spanned by a non-negative basis whose transpose is regarded as the projection matrix. Since PNMF learns a natural parts-based representation, it has been successfully used in text mining and pattern recognition. However, it is non-trivial to analyze the convergence of the optimization algorithms for PNMF because its objective function is non-convex. In this paper, we propose a Box-constrained PNMF (BPNMF) method to overcome this deficiency of PNMF. In particular, BPNMF introduces an auxiliary variable, i.e., the coefficients of examples, and incorporates the following two types of constraints: 1) each entry of the basis is non-negative and upper-bounded, i.e., box-constrained, and 2) the coefficients equal to the projected points of the examples. The first box constraint makes the basis to be bound and the second equality constraint keeps its equivalence to PNMF. Similar to PNMF, BPNMF is difficult because the objective function is non-convex. To solve BPNMF, we developed an efficient algorithm in the frame of augmented Lagrangian multiplier (ALM) method and proved that the ALM-based algorithm converges to local minima. Experimental results on two face image datasets demonstrate the effectiveness of BPNMF compared with the representative methods.

关键词： Accuracy Linear programming Training Convergence Optimization Face Educational institutions

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