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On Using Pattern Matching Algorithms in MapReduce Applications

On Using Pattern Matching Algorithms in MapReduce Applicatio...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Nikzad Babaii Rizvandi Javid Taheri Albert Y. Zomaya NICTA Sydney NSW Australia Center of Distributed and High Performance Computing School of Information Technologies University of Sydney Australia

In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW)is utilized for such comparison, a correlation analysis is then applied to DTWs' outcomes to produce feasible similarity patterns. Three real applications (Word Count, Exim Mainlogparsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms.

关键词： Time series analysis Databases Pattern matching Correlation Feature extraction Noise Facebook

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An Energy-Efficient Kernel Framework for Large-Scale Data Modeling and Classification

An Energy-Efficient Kernel Framework for Large-Scale Data Mo...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Paul D. Yoo Jason W.P. Ng Albert Y. Zomaya Department Computer Engineering Etisalat British Telecom (BT) Innovation Centre Khalifa University of Science Technology and Research Abu Dhabi UAE Centre of Distributed and High Performance Computing School of Information Technologies (J12) University of Sydney NSW Australia

Energy-efficient computing has now become a key challenge not only for data-center operations, but also for many other energy-driven systems, with the focus on reducing of all energy-related costs, and operational expenses, as well as its corresponding and environmental impacts. Intelligent machine-learning systems are typically performance driven. For instance, most non-parametric model-free approaches are often known to require high computational cost in order to find the global optima. Designing more accurate machine-learning systems to satisfy the market needs will hence lead to a higher likelihood of energy waste due to the increased computational cost. This paper thus introduces an energy-efficient framework for large-scale data modeling and classification. It can achieve a test error comparable to or better than the state-of-the-art machine-learning models, while at the same time, maintaining a low computational cost when dealing with large-scale data. The effectiveness of the proposed approaches has been demonstrated by our experiments with two large-scale KDD datasets: Mtv-1 and Mtv-2.

关键词： Electromyography Machine learning Energy efficiency distributed processing

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An On-line distributed Induction Motor Monitoring System Based-on ARM and CAN Bus

An On-line Distributed Induction Motor Monitoring System Bas...

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international Conference on parallel computing in Electrical Engineering (PARLEC)

作者： Chenhui Wang Yukun Liu Liwei Guo Yong Yue Carsten Maple College of Electrical Engineering and Information Hebei University of Science and Technology Shijiazhuang Hebei China College of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang Hebei China Faculty of Creative Arts Technologies and Science University of Bedfordshire Bedfordshire UK

This paper presents an on-line distributed induction motor monitoring system based-on the ARM (Advanced RISC Machines), which is integrated with the embedded and CAN (Controller Area Network) bus technologies. The hardware structure of the system with the ARM microprocessor S3C2410X and CAN bus controller MCP2510 is introduced, the accomplishment of software of motor on-line monitoring system is also described. Compared to the complicated construction and low integration of traditional motor testing systems, this system has a reasonable structure with less external expansion units, and can carry out data transmission in real time, effectively, and with lower power cost and more reliability.

关键词： Induction motors Monitoring Software Microprocessors Hardware Driver circuits Real time systems

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Detection and Correction of Silent Data Corruption for Large-Scale High-Performance computing

Detection and Correction of Silent Data Corruption for Large...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David Fiala Department of Computer Science North Carolina State University Raleigh NC USA

Faults have become the norm rather than the exception for high-end computing on clusters with 10s/100s of thousands of cores, and this situation will only become more dire as we reach exascale computing. Exacerbating this situation, some of these faults will not be detected, manifesting themselves as silent errors that will corrupt memory while applications continue to operate but report incorrect results. This paper introduces RedMPI, an MPI library residing in the profiling layer of any standards-compliant MPI implementation. RedMPI is capable of both online detection and correction of soft errors that occur in MPI applications without requiring code changes to application source code. By providing redundancy, RedMPI is capable of transparently detecting corrupt messages from MPI processes that become faulted during execution. Furthermore, with triple redundancy RedMPI "votes'' out MPI messages of a faulted process by replacing corrupted results with corrected results from unfaulted processes. We present an evaluation of RedMPI on an assortment of applications to demonstrate the effectiveness and assess associated overheads. Fault injection experiments establish that RedMPI is not only capable of successfully detecting injected faults, but can also correct these faults while carrying a corrupted application to successful completion without propagating invalid data.

关键词： Redundancy Benchmark testing Protocols Libraries Receivers Laboratories Software

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Cyberinfrastructure Usage Modalities on the TeraGrid

Cyberinfrastructure Usage Modalities on the TeraGrid

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Daniel S. Katz David Hart Chris Jordan Amit Majumdar J.P. Navarro Warren Smith John Towns Von Welch Nancy Wilkins-Diehr Computational Institute University of Chicago and Argonne National Laboratory Chicago USA Computational & Information Systems Laboratory National Center for Atmospheric Research Boulder USA Texas Advanced Computing Center University of Texas Austin Austin USA San Diego Supercomputer Center University of California San Diego San Diego USA National Center of Supercomputing Applications University of Illinois Urbana USA Indiana University Bloomington IN USA

This paper is intended to explain how the Tera Grid would like to be able to measure "usage modalities." We would like to (and are beginning to) measure these modalities to understand what objectives our users are pursuing, how they go about achieving them, and why, so that we can make changes in the Tera Grid to better support them.

关键词： Resource management Education USA Councils Instruments Logic gates Couplings

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An Efficient and Scalable Implementation of SNP-Pair Interaction Testing for Genetic Association Studies

An Efficient and Scalable Implementation of SNP-Pair Interac...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Lars Koesterke Dan Stanzione Matt Vaughn Stephen M. Welch Waclaw Kusnierczyk Jinliang Yang Cheng-Ting Yeh Dan Nettleton Patrick S. Schnable Texas Advanced Computing Center University of Technology Austin TX USA Department of Agronomy Kansas State University Manhattan KS USA Iowa State University Ames IA USA

This paper describes a scalable approach to one of the most computationally intensive problems in molecular plant breeding, that of associating quantitative traits with genetic markers. The fundamental problem is to build statistical correlations between particular loci in the genome of an individual plant and the expressed characteristics of that individual. While applied to plants in this paper, the problem generalizes to mapping genotypes to phenotypes across all biology. In this work, a formulation of a statistical approach for identifying pair wise interactions is presented. The implementation, optimization and parallelization of this approach are then presented, with scalability results.

关键词： Bioinformatics Computational modeling Indexes Genomics DNA Electronic mail

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Willow: A Control System for Energy and Thermal Adaptive computing

Willow: A Control System for Energy and Thermal Adaptive Com...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Krishna Kant Muthukumar Murugan David H.C. Du Intel Corporation USA University of Minnesota Minneapolis USA

The increasing energy demand coupled with emerging sustainability concerns requires a re-examination of power/thermal issues in data centers from the perspective of short term energy deficiencies. Such energy deficient scenarios arise for a variety of reasons including variable energy supply from renewable sources and inadequate power, thermal and cooling capacities. In this paper we propose a hierarchical control scheme to adapt assignments of tasks to servers in a way that can cope with the varying energy limitations and still provide necessary QoS. The rescheduling of tasks on different servers has direct (migration related) and indirect (changed traffic patterns) network energy impacts that we also consider. We show the stability of our scheme and evaluate its performance via detailed simulations and experiments.

关键词： Servers Power control Power demand Cooling Thermal management Thermal sensors Supply and demand

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Co-analysis of RAS Log and Job Log on Blue Gene/P

Co-analysis of RAS Log and Job Log on Blue Gene/P

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international symposium on parallel and distributed Processing (IPDPS)

作者： Ziming Zheng Li Yu Wei Tang Zhiling Lan Rinku Gupta Narayan Desai Susan Coghlan Daniel Buettner Department of Computer Science Illinois Institute of Technology Mathematics and Computer Science Division Leadership Computing Facility Argonne National Laboratory

With the growth of system size and complexity, reliability has become of paramount importance for petascale systems. Reliability, Availability, and Serviceability (RAS) logs have been commonly used for failure analysis. However, analysis based on just the RAS logs has proved to be insufficient in understanding failures and system behaviors. To overcome the limitation of this existing methodologies, we analyze the Blue Gene/P RAS logs and the Blue Gene/P job logs in a cooperative manner. From our co-analysis effort, we have identified a dozen important observations about failure characteristics and job interruption characteristics on the Blue Gene/P systems. These observations can significantly facilitate the research in fault resilience of large-scale systems.

关键词： Hardware Laboratories Correlation Kernel Reliability Large-scale systems

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parallel Processing Framework on a P2P System Using Map and Reduce Primitives

Parallel Processing Framework on a P2P System Using Map and ...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Kyungyong Lee Tae Woong Choi Arijit Ganguly David I. Wolinsky P. Oscar Boykin Renato Figueiredo ACIS Lab. Department of ECE. University of Florida USA Samsung SDS Seoul South Korea Amazon Web Service. ***

This paper presents a parallel processing framework for structured Peer-To-Peer (P2P) networks. A parallel processing task is expressed using Map and Reduce primitives inspired by functional programming models. The Map and Reduce tasks are distributed to a subset of nodes within a P2P network for execution by using a self-organizing multicast tree. The distribution latency cost of multicast method is O(log(N)), where N is a number of target nodes for task processing. Each node getting a task performs the Map task, and the task result is summarized and aggregated in a distributed fashion at each node of the multicast tree during the Reduce task. We have implemented this framework on the Brunet P2P system, and the system currently supports predefined Map and Reduce tasks or tasks inserted through Remote Procedure Call (RPC) invocations. A simulation result demonstrates the scalability and efficiency of our parallel processing framework. An experiment result on PlanetLab which performs a distributed K-Means clustering to gather statistics of connection latencies among P2P nodes shows the applicability of our system in applications such as monitoring overlay networks.

关键词： Peer to peer computing parallel processing Clustering algorithms Data mining Google Routing Monitoring

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Towards Intelligent Data Placement for Scientific Workflows in Collaborative Cloud Environment

Towards Intelligent Data Placement for Scientific Workflows ...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Xin Liu Anwitaman Datta School of Computer Engineering Nanyang Technological University Singapore

Recently emerged cloud computing offers a promising platform for executing scientific workflow applications due to its similar performance compared to the grid, lower cost, elasticity and so on. Collaborative cloud environments, which share resources of multiple geographically distributed data centers owned by different organizations enable researchers from all over the world to conduct their large scale data intensive research together through Internet. However, since scientific workflows consume and generate huge amount of data, it is thus essential to manage the data effectively for the purpose of high performance and cost effectiveness. In this paper, we propose intelligent data placement strategy to improve performance of workflows while minimizing data transfer among data centers. Specifically, at the startup stage, the whole dataset is divided into small data items which are then distributed among multiple data centers by considering these data centers' computation capability, storage budget, data item correlation, etc. During the runtime stage, when intermediate data is generated, it is placed on the suitable data centers using linear discriminant analysis by taking into account the same metrics as at the startup stage, as well as data centers' past behaviors (i.e., trustworthiness in terms of task delay). Simulation results demonstrate the promise of our data placement strategy by showing that compared to existing data placement strategies, our proposal effectively places the data to improve computation progress on the whole while minimizing the communication overheads incurred by data movement.

关键词： Correlation distributed databases Runtime Clustering algorithms Cloud computing Delay Organizations

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