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A Compression Framework for Multidimensional Scientific Datasets

A Compression Framework for Multidimensional Scientific Data...

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

作者： Tekin Bicer Gagan Agrawal Ohio State University

ISBN: (纸本)9781479913725

Scientific simulations and instruments can generate tremendous amount of data in short time periods. Since the generated data is used for inferring new knowledge, it is important to efficiently store and provide it to the scientific endeavors. Although parallel and distributed systems can help to ease the management of such data, the transmission and storage are still challenging problems. Compression is a popular approach for reducing data transfer overheads and storage requirements. However, effectively supporting compression for scientific simulation data and integrating compression algorithms with simulation applications remain a challenge. In this work, we focus on management of multidimensional scientific datasets using domain specific compression algorithms. We propose a compression framework and methodology in order to maximize the bandwidth and storage utilization. We port our framework into PnetCDF and present our preliminary experimental results.

关键词： Compression algorithms Meteorology Throughput Data models distributed databases Optimization Data compression

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MapReduce Algorithms for GIS Polygonal Overlay Processing

MapReduce Algorithms for GIS Polygonal Overlay Processing

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

作者： Satish Puri Dinesh Agarwal Xi He Sushil K. Prasad Department of Computer Science Georgia State University Atlanta USA

ISBN: (纸本)9781479913725

Polygon overlay is one of the complex operations in computational geometry. It is applied in many fields such as Geographic Information systems (GIS), computer graphics and VLSI CAD. Sequential algorithms for this problem are in abundance in literature but there is a lack of distributed algorithms especially for MapReduce platform. In GIS, spatial data files tend to be large in size (in GBs) and the underlying overlay computation is highly irregular and compute intensive. The MapReduce paradigm is now standard in industry and academia for processing large-scale data. Motivated by the MapReduce programming model, we revisit the distributed polygon overlay problem and its implementation on MapReduce platform. Our algorithms are geared towards maximizing local processing and minimizing the communication overhead inherent with shuffle and sort phases in MapReduce. We have experimented with two data sets and achieved up to 22x speedup with dataset 1 using 64 CPU cores.

关键词： Geographic information systems Partitioning algorithms Spatial databases Algorithm design and analysis Vectors Data structures Libraries

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Symmetric comparison-based fault diagnosis of multiprocessor and distributed systems using nonlinear support vector machines

Symmetric comparison-based fault diagnosis of multiprocessor...

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2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013

作者： Elhadef, Mourad College of Engineering and Computer Science Abu-Dhabi University PO.Box 59911 Abu-Dhabi United Arab Emirates

ISBN: (纸本)9780769549798

In this paper, the problem of identifying the set of permanent faulty nodes using partial syndromes, i.e., when not all the comparison outcomes are available prior to initiating the diagnosis phase, is considered. A new diagnosis approach, using nonlinear support vector machines (SVMs), is described. We consider the symmetric comparison diagnosis model which assumes that nodes are assigned a set of tasks and their outcomes are compared, and that at most t nodes can fail simultaneously. Based on the agreements and disagreements among the nodes' outputs, the diagnosis algorithm must identify all faulty nodes. The new nonlinear SVM-based fault identification algorithm is first trained using various syndromes with known fault sets. Then, it is extensively tested using randomly generated diagnosable systems of different sizes and under various fault scenarios. Simulations showed that the nonlinear SVM-based diagnosis performed efficiently, i.e. the diagnosis algorithm correctly identified almost all the faulty nodes even when at most half of the comparison outcomes are missing. In addition, results from the thorough simulation study demonstrate the effectiveness of the nonlinear SVM-based fault identification algorithm, in terms of diagnosis correctness, latency, and scalability. © 2013 IEEE.

关键词： Fault tolerance

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Usability in Machine Learning at Scale with GraphLab 13

Usability in Machine Learning at Scale with GraphLab

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22nd ACM international Conference on Information and Knowledge Management (CIKM)

作者： Guestrin, Carlos Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450322638

Today, machine learning (ML) methods play a central role in industry and science. The growth of the Web and improvements in sensor data collection technology have been rapidly increasing the magnitude and complexity of the ML tasks we must solve. This growth is driving the need for scalable, parallel ML algorithms that can handle "Big Data."In this talk, we will focus on: Examining common algorithmic patterns in distributed ML *** the challenges of implementing these algorithms in real distributed *** computational frameworks for implementing these algorithms at *** a significant core challenge to large-scale ML -- enabling the widespread adoption of machine learning beyond *** the latter part, we will focus mainly on the GraphLab framework, which naturally expresses asynchronous, dynamic graph computations that are key for state-of-the-art ML algorithms. When these algorithms are expressed in our higher-level abstraction, GraphLab will effectively address many of the underlying parallelism challenges, including data distribution, optimized communication, and guaranteeing sequential consistency, a property that is surprisingly important for many ML algorithms. On a variety of large-scale tasks, GraphLab provides 20-100x performance improvements over Hadoop. In recent months, GraphLab has received many tens of thousands of downloads, and is being actively used by a number of startups, companies, research labs and universities.

关键词： Machine Learning Big Data Graphs distributed systems Data Mining databases

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Read optimisations for append storage on flash 13

Read optimisations for append storage on flash

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17th international Database Engineering and Applications symposium, IDEAS 2013

作者： Gottstein, Robert Petrov, Ilia Buchmann, Alejandro Databases and Distributed Systems Group TU-Darmstadt Germany Data Management Lab. School of Informatics Reutlingen University Germany

ISBN: (纸本)9781450320252

Append-/Log-based Storage Managers (LbSM) for database systems represent a good match for the characteristics and behaviour of Flash technology. LbSM alleviate random writes reducing the impact of Flash read/write asymmetry, increasing endurance and performance. A recently proposed combination of Multi-Versioning database approaches and LbSM called SIAS [9] offers further benefits: it substantially lowers the write rate due to tuple version append granularity and therefore improves the performance. In SIAS a page contains versions of tuples of the same table. Once appended such a page is immutable. The only allowable operations are reads (lookups, scans, version visibility checks) in tuple version granularity. Optimising for them offers an essential performance increase. In the present work-in-progress paper we propose two types of read optimisations: Multi-Version Index and Ordered Log Storage. Benefits of Ordered Log Storage: (i) Read efficiency due to the use of parallel read streams;(ii) Write efficiency since larger amounts of data are appended sequentially;(iii) fast garbage collection: read multiple sorted runs, filter dead tuples and write one single, large (combined) sorted run. (iv) possible cache-efficiency optimisations (for large scans) Benefits of Multi-Version Indexing: (i) index only visibility checks;(ii) postponing of index reorganisations;(iii) no invalid tuple bits in the index (in-place updates);(iv) pre-filtering of invisible tuple versions;(v) facilitate easy identification of tuple versions to be garbage collected. Benefits of the combination of both approaches: (i) Index and ordered access;(ii) Facilitate range searches in sorted runs;(iii) on the fly garbage collection (checking of one bit). © 2013 ACM.

关键词： Visibility

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distributed Query Processing in an Ad-hoc Semantic Web Data Sharing System

Distributed Query Processing in an Ad-hoc Semantic Web Data ...

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

作者： Jing Zhou Gregor V. Bochmann Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ontario Canada

ISBN: (纸本)9781479913725

Sharing the Semantic Web data in proprietary datasets in which data is encoded in RDF triples in a decentralized environment calls for efficient support from distributed computing technologies. The highly dynamic ad-hoc settings that would be pervasive for Semantic Web data sharing among personal users in the future, however, pose even more demanding challenges for the enabling technologies. We extend previous work on a hybrid P2P architecture for an ad-hoc Semantic Web data sharing system which better models the data sharing scenario by allowing data to be maintained by its own providers and exhibits satisfactory scalability owing to the adoption of a two-level distributed index and hashing techniques. Additionally, we propose efficient distributed processing of SPARQL queries in such a context and explore optimization techniques that build upon distributed query processing for database systems and relational algebra optimization. We anticipate that our work will become an indispensable, complementary approach to making the Semantic Web a reality by delivering efficient data sharing and reusing in an ad-hoc environment.

关键词： Peer-to-peer computing Resource description framework Indexes Query processing distributed databases Optimization

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Mass Information Index Design Based on MapReduce

Mass Information Index Design Based on MapReduce

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international symposium on Computational Intelligence and Design, ISCID

作者： Wei Zheng Xuguang Wang Tao Jiang Jing Li Chongqing Power Information&Communication Branch Company China North China Electric Power University Baoding

With the rapid development of social informatization, there is a lot of new data generated every day, the focus of the IT industry is how to quickly find out the information you need. Inverted index based on MapReduce technology can achieve rapid retrieval, and it is a common index structure in the index engine information retrieval systems. At the same time, mass data need to be in a distributed platform for storage and processing, the corresponding file inverted index structure also needs to be run in a distributed platform, and MapReduce is currently the most common used technology of inverted index structure. Mass information index based on MapReduce is quicker, more convenient and suitable than the traditional index.

关键词： Indexes Data models Computational modeling Programming distributed databases parallel processing Computers

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Measuring Power Consumption on IBM Blue Gene/Q

Measuring Power Consumption on IBM Blue Gene/Q

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

作者： Sean Wallace Venkatram Vishwanath Susan Coghlan Zhiling Lan Michael E. Papka Argonne National Laboratory Argonne IL USA Illinois Institute of Technology Chicago IL USA

ISBN: (纸本)9781479913725

In addition to pushing what is possible computationally, state-of-the-art supercomputers are also pushing what is acceptable in terms of power consumption. Despite hardware manufacturers researching and developing efficient system components (e.g., processor, memory, etc.), the power consumption of a complete system remains an understudied research area. Because of the complexity and unpredictable workloads of these systems, estimating the power consumption of a full system is a nontrivial *** this paper, we provide system-level power usage and temperature analysis of early access to Argonne's latest generation of IBM Blue Gene supercomputers, the Mira Blue Gene/Q system. The analysis is provided from the point of view of jobs running on the system. We describe the important implications these system level measurements have as well as the challenges they present. Using profiling code on benchmarks, we will also look at the new tools this latest generation of supercomputer provides and gauge their usefulness and how well they match up against the environmental data.

关键词： Power demand Temperature sensors Power measurement databases Hardware Temperature measurement

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Proactive Support for Large-Scale Data Exploration

Proactive Support for Large-Scale Data Exploration

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

作者： Mark Hereld Tanu Malik Venkatram Vishwanath Computation Institute Argonne National Laboratory Computation Institute The University of Chicago Argonne National Laboratory

ISBN: (纸本)9781479913725

Computational science is generating increasingly unwieldy datasets created by complex and high-resolution simulations of physical, social, and economic systems. Traditional post processing of such large datasets requires high bandwidth to large storage resources. In situ processing approaches can reduce I/O requirements but steal processing cycles from the simulation and forsake interactive data exploration. The Fusion project aims to develop a new approach for exploring large-scale scientific datasets wherein the system actively assists the user in the data exploration process. A key component of the system is a software assistant that evaluates the stated and implied analysis goals of the scientist, observes the environment, models and proposes actions to be taken, and orchestrates the generation of analysis and visualization products for the user. These products are managed and made available to the scientist through an interactive space consisting of a database and a visual interface. The scientist sifts through and explores the available analysis products while indicating preferences that are translated into goals, completing the feedback loop that steers the assistant in its future actions.

关键词： Data visualization Data models Analytical models Visualization databases Information management Data handling

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On partitioning and reordering problems in a hierarchically parallel hybrid linear solver

On partitioning and reordering problems in a hierarchically ...

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2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013

作者： Yamazaki, Ichitaro Li, Xiaoye S. Rouet, Francois-Henry Ucar, Bora Electrical Engineering and Computer Science Department University of Tennessee Knoxville United States Computational Research Division Lawrence Berkeley National Laboratory United States ENSEEIHT-IRIT Lawrence Berkeley National Laboratory United States CNRS and LIP UMR CNRS-ENS-INRIA-UCBL Lyon France

ISBN: (纸本)9780769549798

PDSLin is a general-purpose algebraic parallel hybrid (direct/iterative) linear solver based on the Schur complement method. The most challenging step of the solver is the computation of a preconditioner based on the global Schur complement. Efficient parallel computation of the preconditioner gives rise to partitioning problems with sophisticated constraints and objectives. In this paper, we identify two such problems and propose hyper graph partitioning methods to address them. The first problem is to balance the work loads associated with different sub domains to compute the preconditioner. We first formulate an objective function and a set of constraints to model the preconditioner computation time. Then, to address these complex constraints, we propose a recursive hyper graph bisection method. The second problem is to improve the data locality during the parallel solution of a sparse triangular system with multiple sparse right-hand sides. We carefully analyze the objective function and show that it can be well approximated by a standard hyper graph partitioning method. Moreover, an ordering compatible with a post ordering of the sub domain elimination tree is shown to be very effective in preserving locality. To evaluate the two proposed methods in practice, we present experimental results using linear systems arising from some applications of our interest. First, we show that in comparison to a commonly-used nested graph dissection method, the proposed recursive hyper graph partitioning method reduces the preconditioner construction time, especially when the number of sub domains is moderate. This is the desired result since PDSLin is based on a two-level parallelization to keep the number of sub domains small by assigning multiple processors to each sub domain. We also show that our second proposed hyper graph method improves the data locality during the sparse triangular solution and reduces the solution time. Moreover, we show that partitioning time can be

关键词： Linear systems

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