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IEEE 30th international conference on Data Engineering (ICDE)

作者： Sadoghi, Mohammad Jacobsen, Hans-Arno IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Univ Toronto Middleware Syst Res Grp Toronto ON M5S 1A1 Canada

ISBN: (纸本)9781479934805

The efficient processing of large collections of patterns expressed as Boolean expressions over event streams plays a central role in major data intensive applications ranging from user-centric processing and personalization to real-time data analysis. On the one hand, emerging user-centric applications, including computational advertising and selective information dissemination, demand determining and presenting to an end-user the relevant content as it is published. On the other hand, applications in real-time data analysis, including push-based multi-query optimization, computational finance and intrusion detection, demand meeting stringent subsecond processing requirements and providing high-frequency event processing. We achieve these event processing requirements by exploiting the shift towards multi-core architectures by proposing novel adaptive parallel compressed event matching algorithm (A-PCM) and online event stream re-ordering technique (OSR) that unleash an unprecedented degree of parallelism amenable for highly parallel event processing. In our comprehensive evaluation, we demonstrate the efficiency of our proposed techniques. We show that the adaptive parallel compressed event matching algorithm can sustain an event rate of up to 233,863 events/second while state-of-the-art sequential event matching algorithms sustains only 36 events/second when processing up to five million Boolean expressions.

关键词： Data analysis Encoding Indexes Instruction sets Phase change materials Real-time systems Subscriptions

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A distributed Polygon Retrieval Algorithm using MapReduce 10

A Distributed Polygon Retrieval Algorithm using MapReduce

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10th IEEE international conference on Collaborative Computing Networking applications and Worksharing

作者： Guo, Qiulei Palanisamy, Balaji Karimi, Hassan A. Univ Pittsburgh Sch Informat Sci Geoinformat Lab Pittsburgh PA 15260 USA

ISBN: (纸本)9781631900433

The proliferation of data acquisition devices like 3D laser scanners had led to the burst of large-scale spatial terrain data which imposes many challenges to spatial data analysis and computation. With the advent of several emerging collaborative cloud technologies, a natural and cost-effective approach to managing such large-scale data is to store and share such datasets in a publicly hosted cloud service and process the data within the cloud itself using modern distributed computing paradigms such as MapReduce. For several key spatial data analysis and computation problems, polygon retrieval is a fundamental operation which is often computed under real-time constraints. However, existing sequential algorithms fail to meet this demand effectively given that terrain data in recent years have witnessed an unprecedented growth in both volume and rate. In this work, we develop a MapReduce-based parallel polygon retrieval algorithm which aims at minimizing the 10 and CPU loads of the map and reduce tasks during spatial data processing. The results of the preliminary experiments on a Hadoop cluster demonstrate that the proposed techniques are scalable and lead to more than 35% reduction in execution time of the polygon retrieval operation over existing distributed algorithms.

关键词： MapReduce

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distributed Pattern Matching and Document Analysis in Big Data using Hadoop MapReduce Model 3

Distributed Pattern Matching and Document Analysis in Big Da...

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international conference parallel distributed Grid Computing

作者： Ramya, A., V Sivasankar, E. Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781479976836

Sequential pattern mining and Document analysis is an important data mining problem in Big Data with broad applications. This paper investigates a specific framework for managing distributed processing in dataset pattern match and document analysis context. MapReduce programming model on a Hadoop cluster is highly scalable and works with commodity machines with integrated mechanisms for fault tolerance. In this paper, we propose a Knuth Morris Pratt based sequential pattern matching in distributed environment with the help of Hadoop distributed File System as efficient mining of sequential patterns. It also investigates the feasibility of partitioning and clustering of text document datasets for document comparisons. It simplifies the search space and acquires a higher mining efficiency. Data mining tasks has been decomposed to many Map tasks and distributed to many Task trackers. The map tasks find the intermediate results and send to reduce task which consolidates the final result. Both theoretical analysis and experimental result with data as well as cluster of varying size shows the effectiveness of MapReduce model primarily based on time requirements.

关键词： distributed processing MapReduce Programming Model Hadoop Cluster

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parallel Infrastructures and Systems for Near-Field Tsunami Detection and Impact Assessment

Parallel Infrastructures and Systems for Near-Field Tsunami ...

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28th IEEE international conference on Advanced Information Networking and applications Workshops (IEEE WAINA)

作者： Yazir, Yagiz Onat Erickson, Josh Coady, Yvonne Univ Victoria Dept Comp Sci Victoria BC V8W 2Y2 Canada

ISBN: (纸本)9781479926527

This paper describes our ongoing effort to develop an efficient and scalable infrastructure to model and simulate near-field tsunamis in order to develop site-specific impact scenarios (e.g. the expected effects of the tsunami on land). Our goal is to be able to leverage this infrastructure in order to provide the necessary information to assist emergency planning, notification and response programs. Our primary focus is to produce the scenario information quickly by efficiently utilizing the available hardware, providing as much lead-time as possible for the response systems. We have been able to show that a distributed computing approach allows for parallel computation of simulations, resulting in shorter times between the occurrence of an event and the simulated output. Included in this paper is our proposed computational model, a summary of our infrastructure and an evaluation of our proposed distributed framework.

关键词： INFRASTRUCTURE tsunamis impact assessment near-field system response parallel processing (COMPUTERS) simulating

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Architectural investigation of matrix data layout on multicore processors

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2014年 37卷 64-75页

作者： Kim, Minwoo Ro, Won Woo Yonsei Univ Sch Elect & Elect Engn Seoul South Korea

Many practical applications include matrix operations as essential procedures. In addition, recent studies of matrix operations rely on parallel processing to reduce any calculation delays. Because these operations are highly data intensive, many studies have investigated work distribution techniques and data access latency to accelerate algorithms. However, previous studies have not considered hardware architectural features adequately, although they greatly affect the performance of matrix operations. Thus, the present study considers the architectural characteristics that affect the performance of matrix operations on real multicore processors. We use matrix multiplication, LU decomposition, and Cholesky factorization as the test applications, which are well-known data-intensive mathematical algorithms in various fields. We argue that applications only access matrices in a particular direction, and we propose that the canonical data layout is the optimal matrix data layout compared with the block data layout. In addition, the tiling algorithm is utilized to increase the temporal data locality in multilevel caches and to balance the workload as evenly as possible in multicore environments. Our experimental results show that applications using the canonical data layout with tiling have an 8.23% faster execution time and 3.91% of last level cache miss rate compared with applications executed with the block data layout. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Matrix data layout Multicore architecture Multilevel cache structure parallel algorithm Tiling algorithm

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Codon Based Protein Synthesis for Breast Cancer Risk Prediction through parallel Computing

Codon Based Protein Synthesis for Breast Cancer Risk Predict...

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international conference on Information Communication and Embedded Systems (ICICES)

作者： Vinu, S. Murugesan, G. St Josephs Coll Engn Dept Comp Sci & Engg Chennai Tamil Nadu India

ISBN: (纸本)9781479938346

distributed parallel computing have more advantages in large data processing. By the help of normally available single CPU, single-core computers, it is possible to perform distributed parallel processing by connecting the computers in a network. A computer which receives a job called as Coordinator, acts as the job manager that distributes jobs amongst the other computers. All the pre-processing and post-processing is done on the coordinator processor. Subordinates refer to any one of the computers on the network that is not a coordinator. Protein and its DNA sequence analysis is one of the large data processing applications. DNA is the chemical in each of our cells that makes up the instructions for how our cells function. DNA translated as protein and get stability. Certain protein mutations can dramatically increase the risk for developing certain cancers. Here analyzing protein and DNA sequence refers analysis the sequence of amino acids and nucleic acid to check certain mutation in it. By analyzing protein and DNA sequence, can predict the current as well as future possibility for cancer. To predict accurate risk for breast cancer number of proteins and their DNAs need to be analysis. This is a time consuming process. In order to speed-up this process distributed parallel analysis of protein and DNA sequences to be performed. Here coordinator of the system distributes each protein and its DNA sequences to different subordinates in the distributed system. Subordinates calculate current and future local risk with the help of mutation table and codon translation rules then send it back to coordinator. Finally Coordinator calculates the overall risk and reports it. This system improves the speed and accuracy of protein and DNA sequence analysis process. But to predict the risk for breast cancer accurately, required number of breast cancer related proteins and their DNA sequence, it requires more cost.

关键词： parallel computing large data processing DNA sequence protein sequence mutation codon

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A distributed Approach for Top-k Star Queries on Massive Information Networks 20

A Distributed Approach for Top-k Star Queries on Massive Inf...

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20th IEEE international conference on parallel and distributed Systems (ICPADS)

作者： Jin, Jiahui Khemmarat, Samamon Gao, Lixin Luo, Junzhou Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Univ Massachusetts Amherst Dept Elect & Comp Engn Amherst MA USA

ISBN: (纸本)9781479976157

Massive information networks, such as the knowledge graph by Google, contain billions of labeled entities. Star queries, which aim to identify an entity, given a set of related entities, are common on such networks. Answering star queries can be modeled as a graph pattern matching problem. Traditional approaches apply graph indices to accelerate the query processing. Unfortunately, it is so costly that it is nearly infeasible to build indices on billion node graphs since the time or storage complexity of most indexing techniques is super-linear to the graph size. In this paper, we propose an algorithm to identify the top-k best answers for a star query. Instead of using expensive indices, our algorithm utilizes novel bounding techniques to derive the top-k best answers efficiently. Further, the algorithm can be implemented in a distributed manner scaling to billions of entities and hundreds of machines. We demonstrate the effectiveness and the efficiency of our approach through a series of experiments on real-world information networks.

关键词： Top-k Query distributed System Star Query Information Network Billion-Node Graph Big Data

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TOUGH2-PETSc: A parallel Solver for TOUGH2 15

TOUGH2-PETSc: A Parallel Solver for TOUGH2

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15th international conference on parallel and distributed Computing, applications and Technologies (PDCAT)

作者： Hathorn, Daniel Wu, Yushu Chen, Zizhong Colorado Sch Mines Golden CO 80401 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781479983346

TOUGH2 is a general-purpose numerical simulation program for multi-dimensional, multiphase, multicomponent fluid flows, heat transfer and contaminant transport in porous and fractured media. It has been used worldwide for geothermal reservoir engineering, nuclear waste isolation, environmental assessment and remediation, and modeling flow and transport in variably saturated media. TOUGH2 is very computationally intense, and the accuracy and scope of the simulation is limited by the amount of processing power available on a single computer. This makes it an ideal canadate for parallel computing, as more CPU power and memory is available. Furthermore, TOUGH2' s main computational unit is a linear equation solver. In parallel computing, a lot of effort has been spent to develop highly efficient parallel linear equation solvers. In this paper, we present TOUGH2-PETSc, a parallel implementation of TOUGH2 that uses PETSc to solve the linear systems in TOUGH2. PETSc is a library of high-performance linear and non-linear equation solvers that has been throughly tested at scale. Based on TOUGH2 and PETSc, TOUGH2-PETSc gives TOUGH2 users the potential to perform larger scale and higher resolution simulations. Experimental results demonstrate that the parallel TOUGH2-PETSc shows improved performance over the sequential version.

关键词： Solvers Linear equation parallel processing (COMPUTERS) parallel Lines nonlinear equations contaminant transport Linear system

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parallel QR algorithm for the C-method: application to the diffraction by gratings and rough surfaces 15

Parallel QR algorithm for the C-method: application to the d...

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Simulation Multiconference

作者： Cihui Pan Nahid Emad Richard Dusseaux Laboratoire PRiSM Universite de Versailles Saint-Quentin en Yvelines laboratoire PRiSM Universite de Versailles Saint-Quentin en Yvelines laboratoire LATMOS

ISBN: (纸本)9781510801011

The curvilinear coordinate method (C-method) is an exact method for analysing of electromagnetic waves scattering from rough surfaces. It is based on Maxwell's equations under covariant form written in a non-orthogonal coordinate system. This method leads to an eigenvalue system. All the eigenvalues and eigenvectors of the scattering matrix are required. The QR algorithm seems to be a suitable solution for this high dimension, dense, non-symmetric and complex scattering matrix. In this paper, we present the parallel QR algorithm that is specifically designed for the C-method. We define the "early shift" for the scattering matrix according to the property that we have observed. We mixed the "early shift", Wilkinson's shift and exceptional shift together to accelerate the convergence. Especially, we use the "early shift" first in order to have quick deflation of the real eigenvalues of the scattering matrix. The multi-window bulge chain chasing and parallel aggressive early deflation are used. The multi-window bulge chain chasing approach ensures that most computations are performed in 3 BLAS operations. The aggressive early deflation approach can detect deflation much quicker and thus accelerate convergence. Mixed MPI-OpenMP techniques are utilized for performing the codes to distributed memory platforms. Numerical experiments are performed and applications of this parallel QR algorithm are applied to a real physical problem of diffraction.

关键词： Electromagnetism parallel QR Shift algorithm Multishift Aggressive early deflation S-matrix theory QR algorithms Electromagnetic fields Electromagnetism Rough surfaces quick release deflation deflation diffraction Gratings parallel Lines

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A Performance Analysis of MapReduce applications on Big Data in Cloud based Hadoop

A Performance Analysis of MapReduce Applications on Big Data...

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international conference on Information Communication and Embedded Systems (ICICES)

作者： Gohil, Parth Garg, Dweepna Panchal, Bakul Govt Engn Coll Dept Comp Sci & Engn Modasa Gujarat India Parul Inst Engn & Technol Dept Comp Sci & Engn Vadodara Gujarat India LD Coll Engn Dept Informat Technol Ahmadabad Gujarat India

ISBN: (纸本)9781479938346

MapReduce is one of the most popular programming model for big data analysis in distributed and parallel Computing Environment. It is used for implementing parallel applications. With the growing development of mobile Internet and cloud computing, the issues related to big data have been a matter of concern in both industry and academy. There are several platforms for users to develop their applications based on MapReduce framework such as Hadoop. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. This paper discusses various MapReduce applications like Wordcount, Pi, TeraSort, Grep in Cloud based Hadoop. We have shown experimental results of these applications on Amazon EC2 using two types of Ubuntu instances. In this paper, performance of above application has been shown with respect to execution time and number of nodes. We find in our research study that as the number of nodes increases the execution time decreases and performance increases.

关键词： Big Data MapReduce Hadoop HDFS Amazon EC2 Cloud Computing

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