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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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Simonstrator: Simulation and prototyping platform for distributed mobile applications 8

Simonstrator: Simulation and prototyping platform for distri...

引用

8th EAI international conference on Simulation Tools and Techniques, SIMUTOOLS 2015

作者： Richerzhagen, Björn Stingl, Dominik Rückert, Julius Steinmetz, Ralf Technische Universität Darmstadt Germany Technische Universität Darmstadt Germany

ISBN: (纸本)9781631900792

the increasing market penetration of mobile devices, such as smartphones and tablets, poses additional challenges on the design of distributed systems. Due to the heterogeneous environment consisting of both, mobile and fixed devices, a multitude of effects on different scales need to be considered. Microscopic effects, such as an individual user's interaction with the device, as well as macroscopic effects, such as scalability with the number of users have an impact on the system's performance. the combined evaluation of micro- and macroscopic effects requires both, simulations and prototypical deployments. Furthermore, insights obtained through prototypes during user studies can lead to refined protocols and algorithms, thereby contributing to the overall design process. To enable parallel assessment of micro- and macroscopic effects, we propose the Simonstrator platform, consisting of a lightweight framework for the development and instrumentation of distributed systems as well as runtime environments for (i) the interaction with common simulators, (ii) the deployment on testbeds, and (iii) Android devices. the platform is specifically targeted towards distributed systems for heterogeneous scenarios, considering mobile and fixed networks. We show sample simulations and prototypical deployments of two exemplary use cases: a live video streaming system and a middleware for augmented reality games, highlighting different evaluation goals and environments supported by the proposed Simonstrator platform. Copyright © 2015 ICST.

关键词： Augmented reality

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A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems 17

A Data-oriented Method for Scheduling Dependent Tasks on Hig...

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2015 IEEE 17th international conference on High Performance computing and Communications (HPCC)

作者： Zhang, Peng Gao, Yuxiang Qiu, Meikang SUNY Stony Brook Dept Biomed Engn Stony Brook NY 11794 USA Cray Inc Cluster Solut Dept San Jose CA USA Pace Univ Dept Comp Sci New York NY 10038 USA

ISBN: (纸本)9781479989379

the rapidly-changing computer architectures, though improving the performance of computers, have been challenging the programming environments for efficiently harnessing the potential of novel architectures. In this area, though the high-density multi-GPU architecture enabled unparalleled performance advantage of dense GPUs in a single server, it has increased the difficulty for scheduling diversified and dependent tasks. We therefore propose a data-oriented method for scheduling dependent tasks for this architecture while providing its implementation. In our method, we model a parallel program as a collection of data-dependent tasks for which data dependencies are managed by an expressive matrix. Accordingly, we develop a hierarchical scheduler infrastructure for our model. In this, a top scheduler is built for querying the data-dependency matrix;three downstream schedulers for queuing computation tasks that are exclusively assigned to processor, accelerator or either;and a multitude of bottom schedulers each for providing a processing element with assigned tasks. We experiment our scheduler for examples of Strassen matrix multiplication and Cholesky matrix inversion algorithms on a computer that has 8 Tesla K40 GPUs. the results show that our method is capable of offering the efficient task parallelism while fulfilling the complex task dependencies. When advanced task-oriented schedulers have been widely designed for distributed systems, a lightweight data-driven scheduler could be an alternative and handy approach that can handle the dependent yet diversified tasks of data-intensive applications for the novel high-density multi-accelerator system.

关键词： data scheduling task scheduling parallel computing heterogeneous multi-GPU systems

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Exploring molecular distributed detection 9

Exploring molecular distributed detection

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9th IEEE international conference on Nano/Molecular Medicine and Engineering, NANOMED 2015

作者： Rogers, Uri Koh, Min-Sung School of Computing and Engineering Sciences Eastern Washington University CheneyWA99004 United States

ISBN: (纸本)9781467396714

Chemical-exchange provides a fundamental mechanism by which microorganisms and biological cells communicate. Using this concept and the idea of biological sized nanomachines, this paper explores the detection of a undesired biological agent in a distributed setting. distributed in the sense that the system information is dispersed across the nanomachines, each possessing limited communication capabilities. To study this problem, the molecular distributed detection system is divided into four major components. the first is the biological agent itself. the second is a collection of autonomous nano-sized sensors, capable of observing the environment, and releasing a certain type of communication molecule directly related to that observation. the third is an aqueous molecular transmission channel under drift, modeled using Brownian motion. the fourth is a fusion stage that collects the communication molecules and fuses this information to determine if a biological agent is present or not. Using this framework, we explore biological agent detection performance using optimal and suboptimal fusion rules in a parallel topology for diverse potential applications. © 2015 IEEE.

关键词： Brownian movement

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Simulation Runner: A Cloud-Based parallel and distributed HPC Platform

Simulation Runner: A Cloud-Based Parallel and Distributed HP...

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IEEE international conference on Cloud computing, CLOUD

作者： Zhenbang Liu Hengming Zou Wenming Ye School of Software Shanghai Jiao Tong University Shanghai China

Scientific workloads often require scalable computing resources. the traditional HPC paradigm provides large scale computing resources but requires IT specialists to buy and maintain large computer infrastructure in their organization. With the emergence of cloud computing, more and more scientists and engineers are moving their computing workloads from local clusters to public cloud. However, such a migration often requires experience both in understanding cloud architecture and cloud development skills, which can impede the efficiency and success of migration. this paper presents a cloud-based parallel and distributed computing platform to support scientific computing. With the Simulation Runner, scientists only need to submit their embarrassingly parallel scientific applications with appropriate arguments or parameters. there is no need to rebuild migrate cloud applications or purchase large computer infrastructures by themselves. the system also uses a file caching mechanism to improve performance and reduce the costs of data-intensive computing tasks. A 44X speedup was resulted from using 128 computing instances compared with premise high performance desktop computers. A comparison study on the performance of file cache is given at the end of the paper.

关键词： Cloud computing Portals Computational modeling Runtime environment Databases Electronic mail

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15th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2015

15th International Conference on Algorithms and Architecture...

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15th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2015

ISBN: (纸本)9783319271361

the proceedings contain 59 papers. the special focus in this conference is on applications of parallel and distributed computing. the topics include: On exploring a virtual agent negotiation inspired approach for route guidance in urban traffic networks;optimization of binomial option pricing on intel MIC heterogeneous system;stencil computations on HPC-oriented ARMv8 64-bit multi-core processor;a particle swarm optimization algorithm for controller placement problem in software defined network;a streaming execution method for multi-services in mobile cloud computing;economy-oriented deadline scheduling policy for render system using IaaS cloud;towards detailed tissue-scale 3D simulations of electrical activity and calcium handling in the human cardiac ventricle;task parallel implementation of matrix multiplication on multi-socket multi-core architectures;refactoring for separation of concurrent concerns;exploiting scalable parallelism for remote sensing analysis models by data transformation graph;resource-efficient vibration data collection in cyber-physical systems;a new approach for vehicle recognition and tracking in multi-camera traffic system;a scalable distributed fingerprint identification system;energy saving and load balancing for SDN based on multi-objective particle swarm optimization;pre-stack kirchhoff time migration on hadoop and spark;a cyber physical system with GPU for CNC applications;a solution of the controller placement problem in software defined networks;parallel column subset selection of kernel matrix for scaling up support vector machines;real-time deconvolution with GPU and spark for big imaging data analysis and parallel kirchhoff pre-stack depth migration on large high performance clusters.

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Metallurgical processing of stainless and special steels in the 21st century 8

Metallurgical processing of stainless and special steels in ...

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8th European Stainless Steel conference Science and Market and Duplex Stainless Steel conference and Exhibition, ESSC 2015

作者： Pierer, Robert Jendryssek, Udo De Fries, Uwe INTECO special melting technologies Bruck an der Mur Austria INTECO vacuum technologies GmbH Tönisvorst Germany UdF Stahl-Innovation Herborn Germany

In the last century the stainless steel market changed from special application to mass production. Increasing production went parallel with the development of continuously improved materials. New steel grades as well as further developments of existing alloys such as Cr-Ni austenitic steels, Cr-Ni-Mo austenitic steels, ferritic Cr-steels and nowadays more and more Cr-Mn-Ni austenitic steels have been accompanied by advancements in metallurgical processing. the Vacuum Oxygen Decarburization (VOD) and Argon Oxygen Decarburization (AOD) process were developed in the 60`s, where operation was done manually and calculation using a slide rule. Research and modelling of the process started in the 70's. Accordingly, in the 80`s computing-capacity became high enough for the development of on-line process models. Already 20 years ago, AOD as well as VOD steel plants were operated near thermo-dynamic limits, resulting in the development of more and more sophisticated mathematical models to improve the process. On the one hand, the present study compares the advantages and disadvantages of the different steel production routes, whereby their limitations in the production of special and stainless steel grades is highlighted. On the other hand, the possibilities of powerful AOD and VOD process models, and the resulting optimization potentials such as energy, alloy and refractory material consumption will be illustrated. the proper selection of the process route as well as the application of state-of-the-art AOD and VOD process models are the key to minimize production costs. © 2015 international Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.

关键词： Chromium steel

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Exploiting parallelism for bioinformatics data analysis applications by data transformation graph

Exploiting parallelism for bioinformatics data analysis appl...

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international conference on BioMedical Engineering and Informatics (BMEI)

作者： ZhenChun Huang Yang Gu XiaoXuan Bai Department of Computer Science and Technology Tsinghua University Beijing China School of Computer and Information Technology Liaoning Normal University Dalian China

Bioinformatics applications which are both data-intensive and computation-intensive bring great challenges to their development and optimization. In order to study and accelerate bioinformatics data analysis models, a method named data transformation graph (DTG) is introduced first. It describes scientific data analysis models by dependencies and transformations among their data items. then, taking BLAST as an example, DTG is used to study the data dependency in this popular bioinformatics data analysis model and parallel it by both query splitting and database partition. At last, parallel versions of BLAST proposed by DTG are implemented based on a distributed data-intensive computing middleware called Robinia. the result of performance test shows that parallel BLAST can achieve near-linear speedup with good scalability, and data transformation graph can be used to study, parallelize and optimize bioinformatics analysis applications for higher performance.

关键词： Bioinformatics parallel processing Data analysis Biological system modeling Computational modeling Data models Analytical models

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Designing a distributed Design Exploration Framework in the Inter-cloud Environment

Designing a Distributed Design Exploration Framework in the ...

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IEEE international conference on Cloud computing, CLOUD

作者： Masaharu Muneotmo Tomoya Abe Hokkaido Daigaku Sapporo Hokkaido JP Graduate School of Information Science and Technology Hokkaido University Sapporo Japan

We propose an extreme-scale distributed design exploration framework in the inter-cloud infrastructure consisting of supercomputers and cloud computing systems. In design explorations, we need to search optimal configurations of input parameters that maximize or minimize objective function(s). In our framework, simulations are performed in parallel employing multiple supercomputers of the Japanese nation-wide high-performance computing infrastructure, and virtual or real machines in academic cloud systems are employed for parameter surveys and optimizations with machine learning. To enable nation-wide scale collaborations of supercomputers and cloud systems, we employ scalable distributed databases and objective storages to store input design parameters and resulting output information generated by simulations.

关键词： Optimization distributed databases Computational modeling Supercomputers Cloud computing Genetic algorithms

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A-DRM: Architecture-aware distributed Resource Management of Virtualized Clusters 15

A-DRM: Architecture-aware Distributed Resource Management of...

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11th ACM SIGPLAN/SIGOPS international conference on Virtual Execution Environments

作者： Wang, Hui Isci, Canturk Subramanian, Lavanya Choi, Jongmoo Qian, Depei Mutlu, Onur Beihang Univ Beijing 100191 Peoples R China IBM Thomas J Watson Res Ctr Cambridge MA USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Dankook Univ Yongin Gyeonggi Do South Korea

ISBN: (纸本)9781450334501

Virtualization technologies has been widely adopted by large-scale cloud computing platforms. these virtualized systems employ distributed resource management (DRM) to achieve high resource utilization and energy savings by dynamically migrating and consolidating virtual machines. DRM schemes usually use operating-system-level metrics, such as CPU utilization, memory capacity demand and I/O utilization, to detect and balance resource contention. However, they are oblivious to microarchitecture-level resource interference (e.g., memory bandwidth contention between different VMs running on a host), which is currently not exposed to the operating system. We observe that the lack of visibility into microarchitecture-level resource interference significantly impacts the performance of virtualized systems. Motivated by this observation, we propose a novel architecture-aware DRM scheme (A-DRM), that takes into account microarchitecture-level resource interference when making migration decisions in a virtualized cluster. A-DRM makes use of three core techniques: 1) a profiler to monitor the microarchitecture-level resource usage behavior online for each physical host, 2) a memory bandwidth interference model to assess the interference degree among virtual machines on a host, and 3) a cost-benefit analysis to determine a candidate virtual machine and a host for migration. Real system experiments on thirty randomly selected combinations of applications from the CPU2006, PARSEC, STREAM, NAS parallel Benchmark suites in a four-host virtualized cluster show that A-DRM can improve performance by up to 26.55%, with an average of 9.67%, compared to traditional DRM schemes that lack visibility into microarchitecture-level resource utilization and contention.

关键词： virtualization microarchitecture live migration performance counters resource management

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