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Supercomputing Frontiers and Innovations 2016年第1期3卷 4-21页

作者： Suo, Guang Lu, Yutong Liao, Xiangke Xie, Min Cao, Hongjia 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, particularly, in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. MPI-level fault tolerant constructs, such as ULFM, are being proposed to support software level fault tolerance. However, there are few systematic evaluations by application programmers using benchmarks or pseudo applications. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI, supporting programmer defined data backup and restore. To help programmers write fault tolerant programs, NR-MPI provides a set of friendly programming interfaces and a state transition diagram for data backup and restore. This paper focuses on design, implementation and evaluation of NR-MPI. Specifically,this paper puts emphases on failure detection in MPI library, friendly programming interface extending for NR-MPI and examples of fault tolerant programs based NRMPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup interfaces based on double in-memory checkpoint/*** conduct experiments with both NPB benchmarks and Sweep3D on TH supercomputer in NSCC-TJ. 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. © The Authors 2016.

关键词： Message passing

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Efficient detection of dangling pointer error for C/C++ programs

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Journal of Physics: Conference Series 2017年第1期887卷

作者： Wenzhe Zhang Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Laboratory of High-end Server & Storage Technology College of Computer National University of Defense Technology Changsha PR China

Dangling pointer error is pervasive in C/C++ programs and it is very hard to detect. This paper introduces an efficient detector to detect dangling pointer error in C/C++ programs. By selectively leave some memory accesses unmonitored, our method could reduce the memory monitoring overhead and thus achieves better performance over previous methods. Experiments show that our method could achieve an average speed up of 9% over previous compiler instrumentation based method and more than 50% over previous page protection based method.

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High Performance Interconnect Network for Tianhe System

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Journal of Computer Science & Technology 2015年第2期30卷 259-272页

作者：廖湘科庞征王克非卢宇彤谢旻夏军董德尊所光 College of Computer National University of Defense Technology Changsha 410073 Science and Technology on Parallel and Distributed Processing Laboratory National Changsha 410073 China China University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China

In this paper, we present the Tianhe-2 interconnect network and message passing services. We describe the architecture of the router and network interface chips, and highlight a set of hardware and software features effectively supporting high performance communications, ranging over remote direct memory access, collective optimization, hardwareenable reliable end-to-end communication, user-level message passing services, etc. Measured hardware performance results are also presented.

关键词： Tianhe-2 supercomputer interconnect network router architecture network interface architecture user-level message passing

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Ranking Open Source Software Based on Crowd Wisdom

Ranking Open Source Software Based on Crowd Wisdom

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Qiang Fan Huaimin Wang Gang Yin Tao Wang National Laboratory of Parallel and Distributed Computing School of Computer National University of Defense Technology

Software reuse is critical in open source based software development, but it is very difficult to find a excellent reusable from large amount of similar candidate software in communities. Currently, lots of research works evaluate software by analyzing artifacts created by software developers, few of them reveals the power of feedbacks generated by software users, which we believe very valuable for software ranking. In this paper, we connect open source software from different communities with user feedbacks in Stack Overflow, and explore the correlation between the popularity of posts and time. Finally we rank open source software through using information of connected posts in Stack Overflow and compare our ranking result with several influential ranking results like DB-Engines and personal blogs. The comparison results show that our approach can amazingly give similar ranking results to that given by experienced professionals or commercial ranking systems.

关键词： Software ranking Open Source Crowd Wisdom Open Source Community

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Ranking open source software based on crowd wisdom

Ranking open source software based on crowd wisdom

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Qiang Fan Huaimin Wang Gang Yin Tao Wang National Laboratory of Parallel and Distributed Computing National University of Defense Technology ChangSha Hunan China

ISBN: (纸本)9781479983544

Software reuse is critical in open source based software development, but it is very difficult to find a excellent reusable from large amount of similar candidate software in communities. Currently, lots of research works evaluate software by analyzing artifacts created by software developers, few of them reveals the power of feedbacks generated by software users, which we believe very valuable for software ranking. In this paper, we connect open source software from different communities with user feedbacks in StackOverflow, and explore the correlation between the popularity of posts and time. Finally we rank open source software through using information of connected posts in StackOverflow and compare our ranking result with several influential ranking results like DB-Engines and personal blogs. The comparison results show that our approach can amazingly give similar ranking results to that given by experienced professionals or commercial ranking systems.

关键词： Open source software Data models Joining processes Software reusability Internet

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OSSEAN: Mining Crowd Wisdom in Open Source Communities

OSSEAN: Mining Crowd Wisdom in Open Source Communities

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2015 IEEE Symposium on Service-Oriented System Engineering

作者： Gang Yin Tao Wang Huaimin Wang Qiang Fan Yang Zhang Yue Yu Cheng Yang National Laboratory of Parallel and Distributed Computing National University of Defense Technology ChangSha Hunan China

Nowadays open source software represents a successful crowd-based software production model and is becoming an ecosystem combining huge amounts of software producers (such as software developers) and consumers (such as software users and customers). Lots of research work has been conducted on analyzing software artifacts created by producers, but few of them reveal the power of feedback from consumers which we believe is very important for the evaluation and evolution of open source software. This paper introduces OSSEAN, a platform for Open Source Software Evaluating, Analyzing and Networking. OSSEAN divides the open source communities into two groups: software production communities and software consumption communities. The former contain structured software artifacts such as projects, source code and issues, while the latter are full of textual documents with rich semantics of user feedback. We show the power of OSSEAN with some interesting demos by analyzing more than 200 thousands of open source projects and 10 million documents.

关键词： Communities Production Data mining Crawlers Open source software Ecosystems

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MatrixMap: Programming Abstraction and Implementation of Matrix Computation for Big Data Applications

MatrixMap: Programming Abstraction and Implementation of Mat...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Yaguang Huangfu Jiannong Cao Hongliang Lu Guanqing Liang Department of Computing The Hong Kong Polytechnic University Parallel and Distributed Processing Laboratory National University of Defense Technology

The computation core of many big data applications can be expressed as general matrix computations, including linear algebra operations and irregular matrix operations. However, existing parallel programming systems such as Spark do not have programming abstraction and efficient implementation for general matrix computations. In this paper, we present MatrixMap, a unified and efficient data-parallel system for general matrix computations. MatrixMap provides powerful yet simple abstraction, consisting of a distributed data structure called bulk key matrix and a computation interface defined by matrix patterns. Users can easily load data into bulk key matrices and program algorithms into parallel matrix patterns. MatrixMap outperforms current state-of-the-art systems by employing three key techniques: matrix patterns with lambda functions for irregular and linear algebra matrix operations, asynchronous computation pipeline with optimized data shuffling strategies for specific matrix patterns and in-memory data structure reusing data in iterations. Moreover, it can automatically handle the parallelization and distribute execution of programs on a large cluster. The experiment results show that MatrixMap is 12 times faster than Spark.

关键词： Data structures Programming Linear algebra Machine learning algorithms Sparks Sparse matrices Computational modeling

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Constrained Projective Non-negative Matrix Factorization for Semi-supervised Multi-label Learning

Constrained Projective Non-negative Matrix Factorization for...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xiang Zhang Naiyang Guan Zhigang Luo Xuejun Yang College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China The State key Laboratory of High Performance Computing National University of Defense Technology Changsha China

This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.

关键词： Conferences

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RAFlow: Read Ahead Accelerated I/O Flow through Multiple Virtual Layers

RAFlow: Read Ahead Accelerated I/O Flow through Multiple Vir...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Zhaoning Zhang Kui Wu Huiba Li Jinghua Feng Yuxing Peng Xicheng Lu National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China Computer Science Department University of Victoria B.C. Canada National Super Computing Center Tianjin China

Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devices are chained together, the system may be subject to severe performance degradation. While the read-ahead (RA) mechanism in storage devices plays a very important role to improve I/O performance, RA may not be effective as expected for multiple virtualization layers, since it is originally designed for one layer only. When I/O requests are passed through a long I/O path, they may trigger a chain reaction and lead to unnecessary data transmission and thus bandwidth waste. In this paper, we study the dynamic behavior of RA through multiple I/O layers and demonstrate that if controlled well, RA can greatly accelerate I/O speed. We present RAFlow, a RA control mechanism, to effectively improve I/O performance by strategically expanding RA window at each layer. Our real-world experiments show that it can achieve 20% to 50% performance improvement in I/O paths with up to 8 virtualized storage devices.

关键词： Radio access networks Acceleration distributed databases Performance evaluation Synchronization Benchmark testing Degradation

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distributed Relation Discovery in Internet of Things

Distributed Relation Discovery in Internet of Things

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International Conference on Cloud computing and Big Data (CCBD)

作者： Weiping Zhu Hongliang Lu Xiaohui Cui International School of Software Wuhan University China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781479966226

Internet of things is an emerging technology that aims to connect various smart objects in our daily life. It facilitates the information exchange and control among objects. In the Internet of things, it is important to discover various relations among objects for analyzing and mining useful knowledge. Existing works on relation discovery mainly focus on centralized processing. It is not suitable for Internet of things due to unavailable of server, one-point failure, computation bottleneck, and security and business concerns. In this paper, we propose a distributed approach to discover the relations among objects in Internet of things. We first build the distritbuted system model which may include multiple relation discovery tasks. Based on that, we design an approach utilizing distributed spanning tree to extract the relations. Rather than sending all the information to the server, the objects only need to send the information to a local leader object in our algorithm. We also discuss more about how to improve the performance of the proposed approach and how to relax the system constraints. Extensive simulation have been done and the results show that the proposed approach outperforms existing approaches in terms of the data amount of transmission.

关键词： Internet of things Synchronization Buildings Servers Algorithm design and analysis Delays

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