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International Symposium on parallel and distributed processing (IPDPS)

作者： Chuanfu Xu Lilun Zhang Xiaogang Deng Jianbin Fang Guangxue Wang Wei Cao Yonggang Che Yongxian Wang Wei Liu College of Computer Science National University of Defense Technology Changsha P.R. China parallel and Distributed Systems Group Delft University of Technology Delft The Netherlands State Key Laboratory of Aerodynamics Mianyang P.R. China National University of Defense Technology Changsha Hunan CN

ISBN: (纸本)9781479938018

HOSTA is an in-house high-order CFD software that can simulate complex flows with complex geometries. Large scale high-order CFD simulations using HOSTA require massive HPC resources, thus motivating us to port it onto modern GPU accelerated supercomputers like Tianhe-1A. To achieve a greater speedup and fully tap the potential of Tianhe-1A, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present multiple novel techniques to balance the loads between the store-poor GPU and the store-rich CPU, and overlap the collaborative computation and communication as far as possible. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per Tianhe-1A node for HOSTA by 2.3X, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 Tianhe-1A nodes. With our method, we have successfully simulated China's large civil airplane configuration C919 containing 150M grid cells. To our best knowledge, this is the first paper that reports a CPUGPU collaborative high-order accurate aerodynamic simulation result with such a complex grid geometry.

关键词： Graphics processing units Kernel Collaboration Computational fluid dynamics Computational modeling Memory management Performance evaluation

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Effective top-down active learning for hierarchical text classification

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17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013

作者： Li, Xiao Ling, Charles X. Wang, Huaimin Department of Computer Science University of Western Ontario Canada National Laboratory for Parallel and Distributed Processing National University of Defense Technology China

ISBN: (纸本)9783642374555

Hierarchical text classification is an important task in many real-world applications. To build an accurate hierarchical classification system with many categories, usually a very large number of documents must be labeled and provided. This can be very costly. Active learning has been shown to effectively reduce the labeling effort in traditional (flat) text classification, but few works have been done in hierarchical text classification due to several challenges. A major challenge is to reduce the so-called out-of-domain queries. Previous state-of-the-art approaches tackle this challenge by simultaneously forming the unlabeled pools on all the categories regardless of the inherited hierarchical dependence of classifiers. In this paper, we propose a novel top-down hierarchical active learning framework, and effective strategies to tackle this and other challenges. With extensive experiments on eight real-world hierarchical text datasets, we demonstrate that our strategies are highly effective, and they outperform the state-of-the-art hierarchical active learning methods by reducing 20% to 40% queries. © Springer-Verlag 2013.

关键词： Text processing

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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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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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A peak performance model for Matrix multiplication on general-purpose DSP

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Hunan Daxue Xuebao/Journal of Hunan University Natural sciences 2013年第11 SUPPL.期40卷 148-152页

作者： Liu, Jie Chi, Li-Hua Xie, Lin-Chuan Wang, Yang Gan, Xin-Biao Feng, Hua Hu, Qing-Feng Science and Technology on Parallel and Distributed Processing Laboratory National Univ of Defense Technology Changsha Hunan 410073 China

DSP processor can be used to solve the high performance computation problems, which has the characteristics of high computing performance and low power. Matrix multiplication algorithm is the kernel of many scientific and technology computation, so it is of importance for theorem and practice. Based on general purpose DSP (GPDSP), a new parallel algorithm for matrix multiplication was proposed. And a peak performance model for matrix multiplication was built. From the peak performance model, an architecture of GPDSP was set up, and the parameter of GPDSP with Tflops was given, which includes the number of pipe-line, the number of SIMD registers, the breadth and latency for the hierarchical memories.

关键词： parallel algorithms

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A novel anycast-based integrated routing protocol for wireless sensor networks: Design and implementation

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Journal of Computational Information Systems 2013年第21期9卷 8611-8618页

作者： Yan, Guofeng Peng, Yuxing Chen, Shuhong School of Computer and Communication Hunan Institute of Engineering China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China School of Computer National University of Defense Technology Changsha 410073 China School of Information Science and Engineering Central South University Changsha 410083 China

In this paper, we consider novel anycast-based integrated routing protocol (AIRP) to reduce the cost in delay performance of communications in multihop WSNs. Without tight time synchronization or known geographic information, AIRP provides low-delay cost route. We implement a low-overhead AIRP module in TinyOS kernel by modifying BLIP protocol stack, i. e., the Berkeley Low-power IP stack;as demonstrated, this implementation can be incorporated into existing routing protocols with the least effort. We describe the format of AIRP message, the dynamic updating process of MAP table information, and anycast data flow in detail under TinyOS. And then, we present the anycast group management system. Finally, we show the performance of AIRP where AIRP communication is used to distribute load among a set of servers through a study case. © 2013 Binary Information Press.

关键词： Wireless sensor networks

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Device View Redundancy: an adaptive low-overhead fault tolerance mechanism for many-core system

Device View Redundancy: an adaptive low-overhead fault toler...

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International Workshop on Intelligent Communication and Social Networks

作者： Wentao Jia Chunyuan Zhang Jian Fu National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Institute for Informatics University of Amsterdam

ISBN: (纸本)9781479909735

Continued increasing of fault rate in integrate circuit makes processors more susceptible to errors, especially many-core processor. Meanwhile, most systems or applications do not need full fault coverage, which has excessive overhead. So on-demand fault tolerance is desired for these applications. In this paper, we propose an adaptive low-overhead fault tolerance mechanism for many-core system, called Device View Redundancy (DVR). It treats fault tolerance as a device that can be configured and used by application when high reliability is needed. Nevertheless, DVR exploits the idle resources for low-overhead fault tolerance, which is based on the observation that the utilization of many-core system is low due to lack of parallelism in application. Finally, the experiment shows that the performance overhead of DVR is reduced by 16% to 98% compared with full Dual Modular Redundancy (DMR).

关键词： On-demand redundancy Idle resource exploitation Dynamic coupling Low-overhead Many core system

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GPS: A General Framework for parallel Queries over Data Streams in Cloud

GPS: A General Framework for Parallel Queries over Data Stre...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xiaoyong Li Yijie Wang Yu Zhao Yuan Wang Xiaoling Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

parallel query processing over data streams in cloud computing environments has attracted considerable attention recently in various fields, due to the huge potential value of analyzing massive data or big data in a large number of streaming applications. Nevertheless, existing studies on queries primarily focus on the algorithms for the specific query types with the lack of the general framework for processing various queries. Moreover, existing parallel frameworks in cloud such as MapReduce and its variations are not suitable for many complex queries over complex data streams. In this paper, we extensively discuss the problem of designing the general framework for parallel queries over data streams in cloud. Particularly, we propose and implement a framework called GPS, which can be well adapted to various queries over complex data streams like the uncertain data streams. Furthermore, we further propose a hierarchical and general parallel model for queries over data streams based on the proposed framework, which is more flexible than the MapReduce model. The skyline queries over uncertain data streams based on our proposed framework with real deployment are conducted as an example to verify the performances of our proposals.

关键词： Peer-to-peer computing parallel processing Data models Query processing Global Positioning System Object oriented modeling distributed databases

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NR-MPI: A Non-stop and Fault Resilient MPI

NR-MPI: A Non-stop and Fault Resilient MPI

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International Conference on parallel and distributed Systems (ICPADS)

作者： Guang Suo Yutong Lu Xiangke Liao Min Xie Hongjia Cao State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan Province China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan Province China

Fault resilience has became a major issue for HPC systems, in particular in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. Fault tolerant MPI was proposed to offer support of software level fault tolerance approaches. However, the widely used MPI implementations, such as MPICH and Mvapich2, provide limited support for fault tolerance. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI. NR-MPI implements the semantics of FT-MPI based on MPICH. Specifically, this paper focuses on failure detection in MPI library, online failure recovery of communicators for multiple failures, friendly programming interface extending for NR-MPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup and restore interfaces based on double in-memory checkpoint/restart. We conduct experiments with NPB benchmarks on TH-1A supercomputer. Experimental results show that NR-MPI based fault tolerant programs can recover from failures online without restarting, and the overhead is small even for applications with tens of thousands of cores.

关键词： Fault tolerance Fault tolerant systems Libraries Resource management Context Programming Semantics

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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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