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Comparative Performance Analysis of Coarse Solvers for Algebraic Multigrid on Multicore and Manycore architectures 11th

Comparative Performance Analysis of Coarse Solvers for Algeb...

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11th international conference on parallel processing and Applied Mathematics (PPAM)

作者： Druinsky, Alex Ghysels, Pieter Li, Xiaoye S. Marques, Osni Williams, Samuel Barker, Andrew Kalchev, Delyan Vassilevski, Panayot Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9783319321493;9783319321486

We study the performance of a two-level algebraic-multigrid algorithm, with a focus on the impact of the coarse-grid solver on performance. We consider two algorithms for solving the coarse-space systems: the preconditioned conjugate gradient method and a new robust HSS-embedded low-rank sparse-factorization algorithm. Our test data comes from the SPE Comparative Solution Project for oil-reservoir simulations. We contrast the performance of our code on one 12-core socket of a Cray XC30 machine with performance on a 60-core Intel Xeon Phi coprocessor. To obtain top performance, we optimized the code to take full advantage of fine-grained parallelism and made it thread-friendly for high thread count. We also developed a bounds-and-bottlenecks performance model of the solver which we used to guide us through the optimization effort, and also carried out performance tuning in the solver's large parameter space. As a result, significant speedups were obtained on both machines.

关键词： Algebraic multigrid HSS matrices Manycore machines

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An Accelerated Iterative Linear Solver with GPUs for CFD Calculations of Unstructured Grids

An Accelerated Iterative Linear Solver with GPUs for CFD Cal...

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16th Annual international conference on Computational Science (ICCS)

作者： Williams, Justin Sarofeen, Christian Shan, Hau Conley, Matthew North Carolina A&T State Univ Greensboro NC 27411 USA Naval Surface Warfare Ctr Carderock Div Bethesda MD USA Case Western Reserve Univ Cleveland OH 44106 USA

Computational Fluid Dynamics (CFD) utilizes numerical solutions of Partial Differential Equations (PDE) on discretized volumes. these sets of discretized volumes, grids, can often contain tens of millions, or billions of volumes. the analysis time of these large unstructured grids can take weeks to months to complete even on large computer clusters. For CFD solvers utilizing the Finite Volume Method (FVM) with implicit time stepping or a segregated pressure solver, a large portion of the computation time is spent solving a large linear system with a sparse coefficient matrix. In an effort to improve the performance of these CFD codes, in effect decreasing the time to solution of engineering problems, a conjugate gradient solver for a Finite Volume Method Solver Graphics processing Units (GPU) was implemented to solve a model Poisson's equation. Utilizing the improved memory throughput of NVIDIA's Tesla K20 GPU a 2.5 times improvement was observed compared to a parallel CPU implementation on all 10 cores of an Intel Xeon E5-2670 v2. the parallel CPU implementation was constructed using the open source CFD toolbox, Open-FOAM.

关键词： Computational Fluid Dynamics Graphics processing Units (GPU) Finite Volume Method Unstructured Grids Computational Flow and Transport Modeling Simulations algorithms

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Floating Car Data processing Model Based on Hadoop-GIS Tools 5

Floating Car Data Processing Model Based on Hadoop-GIS Tools

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5th international conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Deng, Zhu Bai, Yuqi Sun Yat Sen Univ Sch Geog & Planning Guangzhou Guangdong Peoples R China Ctr Earth Syst Sci Minist Educ Key Lab Earth Syst Modeling Beijing Peoples R China

ISBN: (纸本)9781509023509

Urban travel characteristics is an essential apart of urban crowd flow analysis. Floating car data, known as "FCD", have been recorded taxis' routine trajectories in cities, which can draw the characteristics of urban crowd flow. As a large volume of datasets, floating car data need an efficient way to process and analyze. A Hadoop-GIS methodology is used in this paper, consists of two major tools which provided by ESRI's 'the GIS tools for Hadoop' - Spatial Framework for Hadoop and Geoprocessing Tools for Hadoop. Hadoop-GIS uses a spatial query languages HiveQL, an SQL-like language in Hive with schema transparently converting queries to MapReduce. More than 1 billion floating car data in Beijing over 17 days in November, 2014, is adopted in this paper, generated from about 32,000 GPS-equipped taxicabs. An aggregation analysis proves that Hadoop-GIS could process floating car data effectively and efficiently. After data cleansing and pretreating of floating car data, the taxi trajectories, which terminate at Tsinghua University, are first extracted through Hadoop-GIS to discover the crowd flow patterns in Beijing, aiming to provide suggestion for city vehicles management. Proved by experiments, the FCD processing model, which based on Hadoop-GIS, could fulfill the processing requirements of large scale floating car data sets and has the spatial analyzing capability of parallel processing. these features improve the storage and processing abilities of large scale spatial data greatly.

关键词： Hadoop-GIS floating car data Hive

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Porting Matlab applications to high-performance C++ codes: CPU/GPU-accelerated spherical deconvolution of diffusion MRI data 16th

Porting Matlab applications to high-performance C++ codes: C...

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16th international conference on algorithms and architectures for parallel processing, ICA3PP 2016

作者： Blas, Javier Garcia Dolz, Manuel F. Daniel Garcia, J. Carretero, Jesus Daducci, Alessandro Aleman, Yasser Canales-Rodriguez, Erick Jorge Dpto. de Informática Universidad Carlos III de Madrid Leganés Spain Signal Processing Lab École Polytechnique Fédérale de Lausanne Lausanne Switzerland Instituto de Investigación Sanitaria Gregorio Marañón Madrid Spain Oviedo Spain FIDMAG Germanes Hospitalàries Barcelona Spain

ISBN: (纸本)9783319495828

In many scientific research fields, Matlab has been established as de facto tool for application design. this approach offers multiple advantages such as rapid deployment prototyping and the use of high performance linear algebra, among others. However, the applications developed are highly dependent of the Matlab runtime, limiting the deployment in heterogeneous platforms. In this paper we present the migration of a Matlab-implemented application to the C++ programming language, allowing the parallelization in GPUs. In particular, we have chosen RUMBA-SD, a spherical deconvolution algorithm, which estimates the intravoxel white-matter fiber orientations from diffusion MRI data. We describe the methodology used along with the tools and libraries leveraged during the translation task of such application. To demonstrate the benefits of the migration process, we perform a series of experiments using different high performance computing heterogeneous platforms and linear algebra libraries. this work aims to be a guide for future developments that are implemented out of Matlab. the results show that the C++ version attains, on average, a speedup of 8× over the Matlab one. © Springer international Publishing AG 2016.

关键词： Magnetic resonance imaging

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SOA Applied: Engineering Software as processing Unit of a Serious Game 5th

SOA Applied: Engineering Software as Processing Unit of a Se...

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5th international conference on Games and Learning Alliance (GALA)

作者： Schwarz, Daniel Soebke, Heinrich Hofmann, Andreas F. Angermair, Gerald Schnatmann, Lars Londong, Joerg Tandler Com Ges Umweltinformat mbH Buch Germany Takomat GmbH Cologne Germany Bauhaus Univ Weimar Bauhaus Inst Infrastruct Solut Bis Weimar Germany

ISBN: (纸本)9783319501826;9783319501819

A mechanistic model is an essential component of any simulation game. the development of such a model, including its elementary components, their relations and calculation rules, is a demanding, costly and fault-prone task. this task becomes even more complex if very realistic models are required, as it is mostly the case for serious games. Alternatively, proven simulation software can be integrated into the serious game, turning the game into an easy to use and motivating user interface, and the simulation program into a verified processing unit. In this article, we present a serious game dealing with water infrastructure planning, which interfaces to an engineering simulation software for necessary calculations. After motivating the combination of game and simulation software in this special case of water infrastructure planning, we focus on the characteristics of the software architecture and the development process. Data availability and data aggregation are identified as issues central to a successful implementation of the presented architecture. this case study thus reveals advantages and challenges of serious games employing service-oriented architectures (SOA).

关键词： Service oriented architecture Transition path Water infrastructure Rating system Serious game Engineering software

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Demonstrating PlanetSense: Gathering Geo-Spatial Intelligence from Crowd-sourced and Social-media Data 16

Demonstrating PlanetSense: Gathering Geo-Spatial Intelligenc...

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24th ACM SIGSPATIAL international conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： thakur, Gautam S. Sparks, Kevin Li, Roger Stewart, Robert N. Urban, Marie L. Oak Ridge Natl Lab Geog Informat Sci & Technol GIST Grp 1 Bethel Valley Rd Oak Ridge TN 37831 USA

ISBN: (纸本)9781450345897

Crowd-sourced and volunteered information, social media, and participatory sensors are capable of providing real-time activity data. Monitoring these sources in time of relevance and then using them to gather operational knowledge is important during crisis management. Beyond that, it's important to curate this information for geo-spatial research purposes, including land use classification and population occupancy analysis. In this demonstration, we will showcase PlanetSense - a geo-spatial research platform built to harness the existing power of archived data and add to that, the dynamics of heterogeneous real-time streaming data from social media and volunteered sources, seamlessly integrated with sophisticated machine learning algorithms and visualization tools. A demonstration will focus on - 1) Recent initiative emphasizing the need to harness crowdsources, volunteered, and social media data at scale;2) Anatomy and insight into data collection workflow. We will show the ability to harvest and process several terabytes of raw data in real-time;3) A detailed discussion with insight into more than 20 sources of data will be given. these sources include text, sensors, as well as imagery data;4) PlanetSense's end to end distributed architecture will be discussed with focus on collecting and processing high-volumes of streaming data in a Geo-Data Cloud. Data fusion methods and algorithms for integrating disparate data sources with existing legacy products. Data analytics and machine learning methods for generating operational intelligence on the fly;5) In addition, PlanetSense "App" platform will be shown with hands-on application enabling interested audience to quickly develop and deploy solutions. 6) Several case studies will be discussed relevant to, land use classification, monitoring transient population, high-resolution occupancy analysis, mapping special events population, ability to uncover global breaking events and reactions in near-real time, ability to t

关键词： Social media volunteered geographic information big data architectures real-time analytics

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Auto-tuning TRSM with an asynchronous task assignment model on multicore, multi-GPU and coprocessor systems

Auto-tuning TRSM with an asynchronous task assignment model ...

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ACS/IEEE international conference on Computer Systems and Applications

作者： Clicia Pinto Marcos Barreto Murilo Boratto Laboratório de Sistemas Distribuídos Universidade Federal da Bahia (UFBA) Salvador Bahia Brazil Núcleo de Arquitetura de Computadores e Sistemas Operacionais Universidade do Estado da Bahia (UNEB) Salvador Bahia Brazil

the increasing need for computing power today justifies the continuous search for techniques that decrease the time to answer usual computational problems. To take advantage of new hybrid parallel architectures composed by multithreading and multiprocessor hardware, our current efforts involve the design and validation of highly parallel algorithms that efficiently explore the characteristics of such architectures. In this paper, we propose an automatic tuning methodology to easily exploit multicore, multi-GPU and coprocessor systems. We present an optimization of an algorithm for solving triangular systems (TRSM), based on block decomposition and asynchronous task assignment, and discuss some results.

关键词： Matrix decomposition Multicore processing Tuning Coprocessors Partitioning algorithms Computational modeling

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Recommendation Systems in Real Applications: Algorithm and parallel Architecture 1

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9th international conference on Security, Privacy, and Anonymity in Computation, Communication and Storage (SpaCCS)

作者： Li, Mengxian Jiang, Wenjun Li, Kenli Hunan Univ Sch Informat Sci & Engn Changsha 410082 Hunan Peoples R China

ISBN: (数字)9783319491486

ISBN: (纸本)9783319491486;9783319491479

Recommendation systems are popular both in business and in academia. A series of works have been reported. In this paper, we briefly introduce the background and some basic concepts of recommendation systems, especially the applications in mainstream websites, most of them built upon parallel processing systems. However, how the recommendation algorithm works in real applications? We investigate (1) the key ideas of recommendation algorithms that are being used in real applications and (2) the parallel architecture in those real recommendation systems. In addition, the performance of recommendation system for those sites are also being analyzed and compared. We also analyze their features and compare their performances. Finally, we outline the challenges and opportunities that all recommendation systems are facing. It is anticipated that the present review will deepen people's understanding of the field and hence contribute to guide the future research of recommendation systems. Our work can help people to better understand the literature and guide the future directions.

关键词： Recommendation system Real application parallel architecture Google news Netflix Meituan Facebook

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2016 IEEE international conference on Communication, Network, and Satellite, COMNETSAT 2016 - Proceedings

2016 IEEE International Conference on Communication, Network...

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5th IEEE international conference on Communication, Network, and Satellite, COMNETSAT 2016

ISBN: (纸本)9781509054466

the proceedings contain 19 papers. the topics discussed include: Indonesian telecom industry point of view of newly emerging telecommunication technologies;the Indonesian telecommunication industry anticipation of the new generation of satellite communication technology;optimized target packet error rate for a new cross-layer scheme in AF relay selection system;MIMO Y channel with imperfect CSI: impact of training and feedback overhead;enhancement of MC-CDMA performance system using rotated modulation;comparison of selection and maximal ratio combining in cooperative network coding with AF and DF;performance evaluation of the UFMC scheme under various transmission impairments;design of asynchronous registers for multimode baseband signals processing;simulation design of triple band metamaterial absorber for radar cross section reduction;designing SP4T switching circuit for beamforming system application at 2.35 GHz frequency;effect of Vivaldi element pattern on the uniform linear array pattern;on the spatial separation of multi-user channels using 73 GHz statistical channel models;MIMO radar waveform design using interleaved-OFDM technique;and VLC design and experiment using 650 nm LASER transmitter and parallel red diffuse LED as receiver.

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An Acceleration of a Random Forest Classification using Altera SDK for OpenCL 15

An Acceleration of a Random Forest Classification using Alte...

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15th international conference on Field-Programmable Technology (FPT)

作者： Nakahara, Hiroki Jinguji, Akira Fujii, Tomonori Sato, Simpei Tokyo Inst Technol Tokyo Kanagawa Japan

ISBN: (纸本)9781509056026

A random forest (RF) is a kind of ensemble machine learning algorithm used for a classification and a regression. It consists of multiple decision trees that are built from randomly sampled data. the RF has a simple, fast learning, and identification capability compared with other machine learning algorithms. It is widely used for applicable to various recognition systems. Since it is necessary to un-balanced trace for each tree and requires communication for all the ones, the random forest is not suitable in SIMD architectures such as GPUs. Although the accelerators using the FPGA have been proposed, such implementations were based on HDL design. thus, they required longer design time compared with the soft-ware based realizations. In this paper, we show the accelerator for the RF using the Altera SDK for OpenCL, which is a kind of high-level synthesis. To accelerate the RF classification, we propose the fully pipelined architecture to increase the memory bandwidth using on-chip memories on the FPGA. Also, we apply appropriate bit fixed point representation instead of 32 bit floating point one in order to reduce the hardware size, power consumption, and increase the memory bandwidth. We implemented the RF on the Terasic Corp. DE5-NET FPGA board, and compared with the CPU and the GPU implementations, As for the LPS (lookups per second), the FPGA realization was 10.7 times faster than the GPU one, and it was 14.0 times faster than the CPU one. As for the LPS per power consumption, the FPGA realization was 61.3 times better than the GPU one, and it was 12.1 times better than the CPU one.

关键词： Radio frequency Decision trees Field programmable gate arrays Graphics processing units Acceleration Vegetation Training

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