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IEEE International Conference on Social Computing (SocialCom)

作者： Ehsan Mousavi Khaneghah Seyedeh Leili Mirtaheri Lucio Grandinetti Amir Saman Memaripour Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781479915194

Extraordinary large datasets of high performance computing applications require improvement in existing storage and retrieval mechanisms. Moreover, enlargement of the gap between data processing and I/O operations' throughput will bound the system performance to storage and retrieval operations and remarkably reduce the overall performance of high performance computing clusters. File replication is a way to improve the performance of I/O operations and increase network utilization by storing several copies of every file. Furthermore, this will lead to a more reliable and fault-tolerant storage cluster. In order to improve the response time of I/O operations, we have proposed a mechanism that estimates the required number of replicas for each file based on its popularity. Besides that, the remaining space of storage cluster is considered in the evaluation of replication factors and the number of replicas is adapted to the storage state. We have implemented the proposed mechanism using HDFS and evaluated it using MapReduce framework. Evaluation results prove its capability to improve the response time of read operations and increase network utilization. Consequently, this mechanism reduces the overall response time of read operations by considering files' popularity in replication process and adapts the replication factor to the cluster state.

关键词： Time factors High performance computing Bandwidth Reliability Throughput System performance History

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A mathematical model for empowerment of Beowulf clusters for exascale computing

A mathematical model for empowerment of Beowulf clusters for...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Seyedeh Leili Mirtaheri Ehsan Mousavi Khaneghah Lucio Grandinetti Mohsen Sharifi Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be useful for exascale computing. Processes running these applications may well demand unpredictable requirements and changes to system configuration and capabilities at runtime, thereby requiring fast system response without sacrificing the transparency and integrity of the reconfigured empowered system that is running on a heterogeneous platform. While a challenge in and of itself, platform heterogeneity is both useful and instrumental in the handling of unpredictable requests. The realization of such a dynamically reconfigurable and heterogeneous HPC cluster system for exascale computing requires a model to guide running processes to determine if they need empowerment of the current cluster, and if yes, by how much. To show the feasibility of empowerment of traditional HPC clusters for exascale computing, we have selected Beowulf as a noble candidate cluster and present a mathematical model for the empowerment of Beowulf clusters for exascale computing (EBEC). We have developed the model in line with Beowulf's cluster approach and by using vector space algebra. In contrast to traditional hardware-oriented approaches to improvise the performance of clusters, we use a software approach to the development of the proposed model by emphasizing processes, which act as the creators of the cluster and thus should decide on system (re)configuration, as the principal building blocks of the system. We have also adopted a new approach to heterogeneity by considering heterogeneity at different levels including hardware, system software, application software, and system functionality. In addition to support for heterogeneity and dynamic reconfiguration, the proposed model includes support for scalability that is crucial to exascale computing too.

关键词： Vectors Mathematical model Hardware Runtime Computers Computational modeling

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Mining Software Profile across Multiple Repositories for Hierarchical Categorization

Mining Software Profile across Multiple Repositories for Hie...

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International Conference on Software Maintenance (ICSM)

作者： Tao Wang Huaimin Wang Gang Yin Charles X. Ling Xiang Li Peng Zou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Computer Science The University of Western Ontario London Ontario Canada Academy of Equipment Beijing China

ISBN: (纸本)9781467352185

The large amounts of software repositories over the Internet are fundamentally changing the traditional paradigms of software maintenance. Efficient categorization of the massive projects for retrieving the relevant software in these repositories is of vital importance for Internet-based maintenance tasks such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, which are only verified on relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software maintenance requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles across multiple repositories. We design a SVM-based categorization framework to classify the massive number of software hierarchically. To improve the categorization performance, we aggregate different types of profile attributes from multiple repositories and design a weighted combination strategy which assigns greater weights to more important attributes. Extensive experiments are carried out on more than 18,000 projects across three repositories. The results show that our approach achieves significant improvements by using weighted combination, and the overall precision, recall and F-Measure can reach 71.41%, 65.60% and 68.38% in appropriate settings. Compared to the previous work, our approach presents competitive results with 123 finer-grained and multi-layered categories. In contrast to those using source code or byte code, our approach is more effective for large-scale and language-independent software categorization.

关键词： Databases Collaboration Servers Software maintenance Internet Data mining

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SPICE modeling of memristors with multilevel resistance states

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Chinese Physics B 2012年第9期21卷 594-600页

作者：方旭东唐玉华吴俊杰 National Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology Department of Computer Science and Technology School of ComputerNational University of Defense Technology

With CMOS technologies approaching the scaling ceiling, novel memory technologies have thrived in recent years, among which the memristor is a rather promising candidate for future resistive memory （RRAM）. Memristor＇s potential to store multiple bits of information as different resistance levels allows its application in multilevel cell （MCL） tech- nology, which can significantly increase the memory capacity. However, most existing memristor models are built for binary or continuous memristance switching. In this paper, we propose the simulation program with integrated circuits emphasis （SPICE） modeling of charge-controlled and flux-controlled memristors with multilevel resistance states based on the memristance versus state map. In our model, the memristance switches abruptly between neighboring resistance states. The proposed model allows users to easily set the number of the resistance levels as parameters, and provides the predictability of resistance switching time if the input current/voltage waveform is given. The functionality of our models has been validated in HSPICE. The models can be used in multilevel RRAM modeling as well as in artificial neural network simulations.

关键词： memristor multilevel cell SPICE model

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Kinetics and Modeling of Chemical Leaching of Sphalerite Concentrate Using Ferric Iron in a Redox-controlled Reactor

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Chinese Journal of Chemical Engineering 2013年第8期21卷 933-936页

作者：宋健高玲林建群吴洪斌林建强 State Key Lab of Microbial Technology Shandong University Department of Pharmacy and Biotechnology Zibo Vocational Institute Shandong Provincial Key Lab for Distributed Computer Software Novel Technology School of Information Science and EngineeringShandong Normal University Institute of Agro-products Processing Science and Technology Xinjiang Academy of Agricultural and Reclamation Science

This work presents a study for chemical leaching of sphalerite concentrate under various constant Fe3+ concentrations and redox potential conditions. The effects of Fe3+ concentration and redox potential on chemical leaching of sphalerite were investigated. The shrinking core model was applied to analyze the experimental results. It was found that both the Fe3+ concentration and the redox potential controlled the chemical leaching rate of sphalerite. A new kinetic model was developed, in which the chemical leaching rate of sphalerite was proportional to Fe3+ concentration and Fe3+ /Fe2+ ratio. All the model parameters were evaluated from the experimental data. The model predictions fit well with the experimental observed values.

关键词： sphalerite leaching kinetic model Fe3＋ concentration redox potential LabVIEW

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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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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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PartialRC: A Partial Recomputing Method for Efficient Fault Recovery on GPGPUs

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Journal of Computer science & technology 2012年第2期27卷 240-255页

作者：徐新海杨学军薛京灵林宇斐林一松 National Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology Programming Languages and Compilers Group School of Computer Science and Engineering University of New South Wales

GPGPUs are increasingly being used to as performance accelerators for HPC （High Performance Computing） applications in CPU/GPU heterogeneous computing systems, including TianHe-1A, the world＇s fastest supercomputer in the TOP500 list, built at NUDT （National University of Defense technology） last year. However, despite their performance advantages, GPGPUs do not provide built-in fault-tolerant mechanisms to offer reliability guarantees required by many HPC applications. By analyzing the SIMT （single-instruction, multiple-thread） characteristics of programs running on GPGPUs, we have developed PartialRC, a new checkpoint-based compiler-directed partial recomputing method, for achieving efficient fault recovery by leveraging the phenomenal computing power of GPGPUs. In this paper, we introduce our PartialRC method that recovers from errors detected in a code region by partially re-computing the region, describe a checkpoint-based faulttolerance framework developed on PartialRC, and discuss an implementation on the CUDA platform. Validation using a range of representative CUDA programs on NVIDIA GPGPUs against FullRC （a traditional full-recomputing Checkpoint-Rollback-Restart fault recovery method for CPUs） shows that PartialRC reduces significantly the fault recovery overheads incurred by FullRC, by 73.5% when errors occur earlier during execution and 74.6% when errors occur later on average. In addition, PartialRC also reduces error detection overheads incurred by FullRC during fault recovery while incurring negligible performance overheads when no fault happens.

关键词： GPGPU partial recomputing fault tolerance CUDA checkpointing

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Speculative symbolic execution

Speculative symbolic execution

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2012 IEEE 23rd International Symposium on Software Reliability Engineering, ISSRE 2012

作者： Zhang, Yufeng Chen, Zhenbang Wang, Ji National Laboratory for Parallel and Distributed Processing Department of Computing Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769548883

Symbolic execution is an effective path oriented and constraint based program analysis technique. Recently, there is a significant development in the research and application of symbolic execution. However, symbolic execution still suffers from the scalability problem in practice, especially when applied to large-scale or very complex programs. In this paper, we propose a new fashion of symbolic execution, named Speculative Symbolic Execution (SSE), to speed up symbolic execution by reducing the invocation times of constraint solver. In SSE, when encountering a branch statement, the search procedure may speculatively explore the branch without regard to the feasibility. Constraint solver is invoked only when the speculated branches are accumulated to a specified number. In addition, we present a key optimization technique that enhances SSE greatly. We have implemented SSE and the optimization technique on Symbolic Pathfinder (SPF). Experimental results on six programs show that, our method can reduce the invocation times of constraint solver by 20.7% to 48.7% (with an average of 29.9%), and save the search time from 23.6% to 43.6% (with an average of 30%). © 2012 IEEE.

关键词： Java programming language

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Skyline query processing on interval uncertain data

Skyline query processing on interval uncertain data

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作者： Li, Xiaoyong Wang, Yijie Li, Xiaoling Wang, Guangdong National Lab. for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769546698

Many recent applications involve processing and analyzing uncertain data. Recently, several research efforts have addressed answering skyline queries efficiently on massive uncertain datasets. However, the research lacks methods to compute these queries on uncertain data, where each dimension of the uncertain object is represented as an interval or an exact value. In this paper, we extensively study the problem of skyline query on these interval based uncertain objects, which has never been studied before. We first model the problem of querying the skylines on interval datasets. Typically, we address two efficient algorithms with I/O optimal for the conventional interval skyline queries and constrained interval skyline queries, respectively. Extensive experiments demonstrate the efficiency of all our proposed algorithms. © 2012 IEEE.

关键词： Query processing

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