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The influences of model parameters on the characteristics of memristors

Chinese Physics B 2012年第4期21卷 576-585页

作者：周静黄达 National Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense TechnologyChangsha 410073China

As the fourth passive circuit component, a memristor is a nonlinear resistor that can ＂remember＂ the amount of charge passing through it. The characteristic of ＂remembering＂ the charge and non-volatility makes memristors great potential candidates in many fields. Nowadays, only a few groups have the ability to fabricate memristors, and most researchers study them by theoretic analysis and simulation. In this paper, we first analyse the theoretical base and characteristics of memristors, then use a simulation program with integrated circuit emphasis as our tool to simulate the theoretical model of memristors and change the parameters in the model to see the influence of each parameter on the characteristics. Our work supplies researchers engaged in memristor-based circuits with advice on how to choose the proper parameters.

关键词： memristor I-V characteristics simulation program with integrated circuit emphasis

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A Dynamic Replication Mechanism to Reduce Response-Time of I/O Operations in High Performance Computing Clusters

A Dynamic Replication Mechanism to Reduce Response-Time of I...

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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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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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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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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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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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Recent advances on trusted computing in China

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Chinese science Bulletin 2012年第35期57卷 4529-4532页

作者： DONG Wei CHEN LiQian School of Computer National University of Defense TechnologyChangsha 410073China National Laboratory for Parallel and Distributed Processing Changsha 410073China

This article highlights some recent research advances on trusted computing in China,focusing mainly on the methodologies and technologies related to trusted computing module,trusted computing platform,trusted network ... 详细信息

关键词：可信计算平台中国计算模块网络连接软件

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Accurate and simplified prediction of L2 cache vulnerability for cost-efficient soft error protection

Accurate and simplified prediction of L2 cache vulnerability...

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作者： Cheng, Yu Ma, Anguo Zhang, Minxuan National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology China

Soft errors caused by energetic particle strikes in on-chip cache memories have become a critical challenge for microprocessor design. Architectural vulnerability factor (AVF), which is defined as the probability that a transient fault in the structure would result in a visible error in the final output of a program, has been widely employed for accurate soft error rate estimation. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures (i.e., issue queue, reorder buffer). Considering large on-chip L2 cache, redundancy-based protection techniques (such as ECC) have been widely employed for L2 cache data integrity with high costs. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to the over-protection, thus incurring high overheads. Therefore, designing AVF-aware protection techniques would be attractive for designers to achieve a cost-efficient protection for caches, especially at early design stage. In this paper, we propose an improved AVF estimation framework for conducing comprehensive characterization of dynamic behavior and predictability of L2 cache vulnerability. We propose to employ Bayesian Additive Regression Trees (BART) method to accurately model the variation of L2 cache AVF and to quantitatively explain the important effects of several key performance metrics on L2 cache AVF. Then we employ bump hunting technique to extract some simple selecting rules based on several key performance metrics for a simplified and fast estimation of L2 cache AVF. Using the simplified L2 cache AVF estimator, we develop an AVF-aware ECC technique as an example to demonstrate the cost-efficient advantages of the AVF prediction based dynamic fault tolerant techniques. Experimental results show that compared with traditional full ECC technique, AVF-aware ECC technique reduces the L2 cache acc

关键词： Cache memory

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Providing information services for wireless sensor networks through cloud computing

Providing information services for wireless sensor networks ...

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2012 7th IEEE Asia-Pacific Services Computing Conference, APSCC 2012

作者： You, Pengfei Peng, Yuxing Gao, Hang National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769548975

Wireless sensor networks (WSN) is a critical technology for information gathering covering many areas, including health-care, transportation, air traffic control and environment monitoring. Despite wide use, the fast increasing data emanating from WSN is not fully utilized due to the limitation for structure of WSN itself. Along with the further development of WSN, the data form which is not be efficiently managed and applied to supply information services for users. As the emerging IT technology, cloud computing supplies powerful utilization ability for IT resources, which makes many traditional applications migrate to cloud computing. In this paper, we propose a framework integrating cloud computing paradigm and WSN, which fully uses data process ability and service model for cloud computing. In the framework, data form WSN are efficiently utilized and managed, depending on which, information services for WSN are well provided to users. © 2012 IEEE.

关键词： Virtualization

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A logistic regression model for Semantic Web service matchmaking

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science China(Information sciences) 2012年第7期55卷 1715-1720页

作者： WEI DengPing 1 ,WANG Ting 1 & WANG Ji 2 1 school of computer,National University of Defense technology,Changsha 410073,China 2 National laboratory for parallel and distributed processing,Changsha 410073,China 1. School of Computer National University of Defense Technology Changsha 410073 China2. National Laboratory for Parallel and Distributed Processing Changsha 410073 China

Semantic Web service matchmaking,as one of the most challenging problems in Semantic Web services (SWS),aims to filter and rank a set of services with respect to a service query by using a certain matching *** this paper,we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration (e.g.,the weighted sum) for SWS *** logistic regression model is trained on training data derived from binary relevance assessments of existing test collections,and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching *** are then ranked according to the probabilities of relevance with respect to each *** method is evaluated on two main test collections,SAWSDL-TC2 and Jena Geography Dataset(JGD).Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service,and hence can improve the effectiveness of service matchmaking.

关键词： Semantic Web service matchmaking logistic regression

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