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Increasing performance of parallel and distributed systems in high performance computing using weight based approach

Increasing performance of parallel and distributed systems i...

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IEEE international conference on Circuit, Power and Computing Technologies, ICCPCT 2015

作者： Jothi, I. Arul Indumathy, P. Dept of Computer Science and Engg Jerusalem College of Engineering Anna University Chennai Chennai India

ISBN: (纸本)9781479970759

High performance computing (HPC), large scale instruments and continuously increasing simulation tools are generating data at a huge rate that are difficult to be effectively managed and analyzed. Implementation of MapReduce model provides a way for processing huge volumes of data through the use of large number of commodity computers. MapReduce and Hadoop have been initially used for processing web data. But recently they have been used for processing more complex scientific applications. The proposed system helps to understand the impact of file system, network and programming modes on performance. The performance an application can obtain is largely work load dependent. Design of every MapReduce system has to include the Key factors like High speed, Quick Response, Accurate result. The proposed work is to improve the scheduling and management functionality of parallel and distributed Computing. The proposed technique Weight based Approach improves the performance by improving job scheduling strategy. © 2015 IEEE.

关键词： MapReduce

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Formal and Conceptual Definitions of the Hybrid Model of distributed Computings in Networks

Formal and Conceptual Definitions of the Hybrid Model of Dis...

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international Siberian conference on Control and Communications (SIBCON)

作者： Dubravin, Aleksey V. Zinkin, Sergey A. Paschenko, Dmitry V. Penza State Univ Fac Comp Engn Penza Russia

ISBN: (纸本)9781479971039

This work deals with the issues of abstract and structural design of distributed network applications based on logic-algebraic approach and some of the methods of artificial intelligence. Formal methods being used allow you to develop distributed applications for data processing which correspond to a chosen paradigm. The paradigm means the concept being chosen for software solutions, for example, the principle of organization of communication between processes, the method of naming and synchronization of processes, the approach to the distribution of the objects and coordination of their functioning. In order to perform conformity of application with the paradigm selected by developer, at the initial stages of the project it is suggested to use conceptual models and logical models of artificial intelligence.

关键词： sequential operator scheme virtual memory space Linda parallel distributed operator network

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Multi-frame digital interferometry using a single digital record system

Multi-frame digital interferometry using a single digital re...

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IEEE international conference on Automatica (ICA-ACCA)

作者： J. Pedreros C. Pavez M. Castro A. Sepúlveda G. Avaria F. Watkins L. Soto Departamento de Ingeniería Eléctrica Universidad de Santiago de Chile Santiago Chile Center for Research and Applications in Plasma Physics and Pulsed Power (P4) Santiago Chile Universidad Nacional Andrés Bello Santiago Chile Comisión Chilena de Energía Nuclear Santiago Chile Facultad de Ciencias Universidad Mayor Santiago Chile Departamento de Física Universidad de Santiago de Chile Santiago Chile

ISBN: (纸本)9781509011483

In this work a multi-frame interferometric system based in digital image processing and digital interferometry is presented. Here, an experimental optical setup which integrates the virtues of the digital interferometry technique and the capacity to capture two interferograms, to two different times, in a single digital recording system is implemented. The digital reconstruction of the interferograms required the capture of three interferometric patterns of very thin parallel fringes (15-20 lines/mm), one of them with information of the phase object and the other two are interferometric patterns of reference. One of the referential pattern is captured in the same condition as the one with plasma and the other one slightly changing the frequency of the pattern fringes. The interferogram associated with each instant of time is separated by means of spatial filtering techniques. Thus, the technique allows obtain digital interferograms in fringes of infinite and finite width in two different times. The interferometric system was tested in a laser-induced spark plasma in air.

关键词： Plasmas Optical interferometry Physics Holography Optical imaging Optical refraction

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Energy Consumption Reduction with DVFS for Message Passing Iterative applications on Heterogeneous Architectures 29

Energy Consumption Reduction with DVFS for Message Passing I...

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29th IEEE international parallel and distributed processing Symposium (IPDPS)

作者： Charr, Jean-Claude Couturier, Raphael Fanfakh, Ahmed Giersch, Arnaud Univ Franche Comte FEMTO ST Inst IUT Belfort Montbeliard 19 Ave Marechal JuinBP 527 F-90016 Belfort France

ISBN: (纸本)9781467376846

Computing platforms are consuming more and more energy due to the increasing number of nodes composing them. To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency scaling (DVFS) is one of them. It reduces the frequency of a CPU to lower its energy consumption. However, lowering the frequency of a CPU may increase the execution time of an application running on that processor. Therefore, the frequency that gives the best trade-off between the energy consumption and the performance of an application must be selected. In this paper, a new online frequency selecting algorithm for heterogeneous platforms (heterogeneous CPUs) is presented. It selects the frequencies and tries to give the best trade-off between energy saving and performance degradation, for each node computing the message passing iterative application. The algorithm has a small overhead and works without training or profiling. It uses a new energy model for message passing iterative applications running on a heterogeneous platform. The proposed algorithm is evaluated on the SimGrid simulator while running the NAS parallel benchmarks. The experiments show that it reduces the energy consumption by up to 34% while limiting the performance degradation as much as possible. Finally, the algorithm is compared to an existing method, the comparison results show that it outperforms the latter, on average it saves 4% more energy while keeping the same performance.

关键词： Arrays Computational modeling Degradation Energy consumption Mathematical model Message passing Time-frequency analysis

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Heterogeneous resource selection for arbitrary HPC applications in the cloud 15

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15th IFIP WG 6.1 international conference on distributed applications and Interoperable Systems, DAIS 2015 Held as Part of the 10th international Federated conference on distributed Computing techniques, DisCoTec 2015

作者： Iordache, Anca Buyukkaya, Eliya Pierre, Guillaume IRISA University of Rennes 1 Rennes35000 France

ISBN: (纸本)9783319191287

Cloud infrastructures offer a wide variety of resources to choose from. However, most cloud users ignore the potential benefits of dynamically choosing cloud resources among a wide variety of VM instance types with different configuration/cost tradeoffs. We propose to automate the choice of resources that should be assigned to arbitrary non-interactive applications. During the first executions of the application, the system tries various resource configurations and builds a custom performance model for this application. Thereafter, cloud users can specify their execution time or financial cost constraints, and let the system automatically select the resources which best satisfy this constraint. © IFIP international Federation for Information processing 2015.

关键词： Computers

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X-Ray: Monitoring and analysis of distributed database queries 15

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作者： Guimarães, Pedro Pereira, José INESC TEC and U. Minho Guimaraes Portugal

ISBN: (纸本)9783319191287

The integration of multiple database technologies, including both SQL and NoSQL, allows using the best tool for each aspect of a complex problem and is increasingly sought in practice. Unfortunately, this makes it difficult for database developers and administrators to obtain a clear view of the resulting composite data processing paths, as they combine operations chosen by different query optimisers, implemented by different software packages, and partitioned across distributed systems. This work addresses this challenge with the X-Ray framework, that allows monitoring code to be added to a Java-based distributed system by manipulating its bytecode at runtime. The resulting information is collected in a NoSQL database and then processed to visualise data processing paths as required for optimising integrated database systems. This proposal is demonstrated with a distributed query over a federation of Apache Derby database servers and its performance evaluated with the standard TPC-C benchmark workload. © IFIP international Federation for Information processing 2015.

关键词： Java programming language

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distributed Graph Algorithmics: Theory and Practice 15

Distributed Graph Algorithmics: Theory and Practice

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8th ACM international conference on Web Search and Data Mining (WSDM)

作者： Lattanzi, Silvio Mirrokni, Vahab Google Res New York NY 10001 USA

ISBN: (纸本)9781450333177

As a fundamental tool in modeling and analyzing social, and information networks, large-scale graph mining is an important component of any tool set for big data analysis. processing graphs with hundreds of billions of edges is only possible via developing distributed algorithms under distributed graph mining frameworks such as MapReduce, Pregel, Gigraph, and alike. For these distributed algorithms to work well in practice, we need to take into account several metrics such as the number of rounds of computation and the communication complexity of each round. For example, given the popularity and ease-of-use of MapReduce framework, developing practical algorithms with good theoretical guarantees for basic graph algorithms is a problem of great importance. In this tutorial, we first discuss how to design and implement algorithms based on traditional MapReduce architecture. In this regard, we discuss various basic graph theoretic problems such as computing connected components, maximum matching, MST, counting triangle and overlapping or balanced clustering. We discuss a computation model for MapReduce and describe the sampling, filtering, local random walk, and core-set techniques to develop efficient algorithms in this framework. At the end, we explore the possibility of employing other distributed graph processing frameworks. In particular, we study the effect of augmenting MapReduce with a distributed hash table (DHT) service and also discuss the use of a new graph processing framework called ASYMP based on asynchronous message-passing method. In particular, we will show that using ASyMP, one can improve the CPU usage, and achieve significantly improved running time.

关键词： parallel computing MapReduce algorithms Large scale data-mining

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Novel Data-Distribution Technique for Hadoop in Heterogeneous Cloud Environments 9

Novel Data-Distribution Technique for Hadoop in Heterogeneou...

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9th international conference on Complex, Intelligent and Software Intensive Systems (CISIS)

作者： Ubarhande, Vrushali Popescu, Alina-Madalina Gonzalez-Velez, Horacio Natl Coll Ireland Cloud Competency Ctr Dublin 1 Ireland

ISBN: (纸本)9781479988709

The Hadoop framework has been developed to effectively process data-intensive MapReduce applications. Hadoop users specify the application computation logic in terms of a map and a reduce function, which are often termed MapReduce applications. The Hadoop distributed file system is used to store the MapReduce application data on the Hadoop cluster nodes called Datanodes, whereas Namenode is a control point for all Datanodes. While its resilience is increased, its current data-distribution methodologies are not necessarily efficient for heterogeneous distributed environments such as public clouds. This work contends that existing data distribution techniques are not necessarily suitable, since the performance of Hadoop typically degrades in heterogeneous environments whenever data-distribution is not determined as per the computing capability of the nodes. The concept of data-locality and its impact on the performance of Hadoop are key factors, since they affect the performance in the Map phase when scheduling tasks. The task scheduling techniques in Hadoop should arguably consider data locality to enhance performance. Various task scheduling techniques have been analysed to understand their data-locality awareness while scheduling applications. Other system factors also play a major role while achieving high performance in Hadoop data processing. The main contribution of this work is a novel methodology for data placement for Hadoop Datanodes based on their computing ratio. Two standard MapReduce applications, WordCount and Grep, have been executed and a significant performance improvement has been observed based on our proposed data distribution technique.

关键词： MapReduce Data Placement Hadoop Data Locality Cloud Computing distributed Computing

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Architecture of Efficient Word processing using Hadoop MapReduce for Big Data applications

Architecture of Efficient Word Processing using Hadoop MapRe...

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international conference on Man and Machine Interfacing (MAMI)

作者： Mandal, Bichitra Sahoo, Ramesh Kumar Sethi, Srinivas IGIT Dept CSEA Sarang India

ISBN: (纸本)9781509002252

Understanding the characteristics of MapReduce workloads in a Hadoop, is the key in making optimal and efficient configuration decisions and improving the system efficiency. MapReduce is a very popular parallel processing framework for large-scale data analytics which has become an effective method for processing massive data by using cluster of computers. In the last decade, the amount of customers, services and information increasing rapidly, yielding the big data analysis problem for service systems. To keep up with the increasing volume of datasets, it requires efficient analytical capability to process and analyze data in two phases. They are mapping and reducing. Between mapping and reducing phases, MapReduce requires a shuffling to globally exchange the intermediate data generated by the mapping. In this paper, it is proposed a novel shuffling strategy to enable efficient data movement and reduce for MapReduce shuffling with number of consecutive words and their count in the word processor. To improve its scalability and efficiency of word processor in big data environment, repetition of consecutive words count with shuffling is implemented on Hadoop. It can be implemented in a widely-adopted distributed computing platform and also in single word processor big documents using the MapReduce parallel processing paradigm.

关键词： Big Data Hadoop MapReduce Word Processor

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Optimizing Strassen Matrix Multiply on GPUs 16

Optimizing Strassen Matrix Multiply on GPUs

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16th IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Khan, Ayaz ul Hasan Al-Mouhamed, Mayez Fatayer, Allam KFUPM Dept Comp Engn Coll Comp Sci & Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781479986767

Many core systems are basically designed for applications having large data parallelism. Strassen Matrix Multiply (MM) can be formulated as a depth first (DFS) traversal of a recursion tree where all cores work in parallel on computing each of the NxN sub-matrices that reduces storage at the detriment of large data motion to gather and aggregate the results. We propose Strassen and Winograd algorithms (S-MM and W-MM) based on three optimizations: a set of basic algebra functions to reduce overhead, invoking efficient library (CUBLAS 5.5), and parameter-tuning of parametric kernel to improve resource occupancy. On GPUs, W-MM and S-MM with one recursion level outperform CUBLAS 5.5 Library with up to twice as faster for large arrays satisfying N>=2048 and N>=3072, respectively. Compared to NVIDIA SDK library, S-MM and W-MM achieved a speedup between 20x to 80x for the above arrays. The proposed approach can be used to enhance the performance of CUBLAS and MKL libraries.

关键词： Graphics processing Unit (GPU) CUDA Programming Strassen Fast Matrix Multiplication

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