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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

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A parallel input-output system for resolving spatial data challenges: An agent-based model case study

A parallel input-output system for resolving spatial data ch...

引用

ACM SIGSPATIAL 2nd international Workshop on High Performance and distributed Geographic Information Systems, ACM SIGSPATIAL HPDGIS 2011

作者： Shook, Eric Wang, Shaowen Department of Geography University of Illinois at Urbana-Champaign Urbana IL 61801 United States University of Illinois at Urbana-Champaign Urbana IL 61801 United States

ISBN: (纸本)9781450310406

With recent advances in data collection technologies such as remote sensing and global positioning systems, the amount of spatial data being produced has been increasing at a staggering rate. Simultaneously, a shift is being experienced in computing from single-core to multi-core processors. To effectively utilize the computational power afforded by these new generation of processors for serving data-intensive geospatial applications, parallel computing techniques need to be employed. parallel computing, however, raises new challenges associated with handling the input and output of spatial data in parallel. This paper describes a parallel Input/Output System (PIOS) to address challenges associated with handling large amounts of diverse spatial data. The PIOS is based on a hierarchical structure that uses a scalable file partitioning strategy and combines data and metadata to enable efficient handling of terabyte-scale data sets in parallel. A spatially-explicit agent-based model is developed as a case study. Computational experiments were conducted on a supercomputer supported by the National Science Foundation. PIOS achieved ten times speedup in parallel input/output time, and was demonstrated to efficiently scale to over one thousand processing cores and handle multiple terabytes of data. © 2011 ACM.

关键词： Supercomputers

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Lega SCi: Legacy system C model integration into parallel systemc simulators

Lega SCi: Legacy system C model integration into parallel sy...

引用

2013 IEEE 37th Annual Computer Software and applications conference, COMPSAC 2013

作者： Schumacher, Christoph Weinstock, Jan Henrik Leupers, Rainer Ascheid, Gerd Tosoratto, Laura Lonardo, Alessandro Petras, Dietmar Grotker, Thorsten Institute for Communication Technologies and Embedded Systems RWTH Aachen University Germany Istituto Nazionale di Fisica Nucleare Sezione di Roma Rome Italy Synopsys Inc. Aachen Germany

ISBN: (纸本)9780769549798

Virtual prototyping of parallel and embedded systems increases insight into existing computer systems. It further allows to explore properties of new systems already during their specification phase. Virtual prototypes of such systems benefit from parallel simulation techniques due to the increased simulation speed. One common problem full system simulator implementers face is the revision and integration of legacy models coded without thread-safety and deterministic behavior in mind. To lessen this burden, this paper presents a methodology to integrate unmodified SystemC legacy models into parallel SystemC simulators. Using the proposed technique, the embedded platform simulator of the EU FP7 project EURETILE, which inherited a number of legacy models from its predecessor project SHAPES, has been transformed into a parallel simulation platform, demonstrating speed-ups of up to 3.36 on four simulation host cores. © 2013 IEEE.

关键词： Simulators

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parallel Primitives for Vendor-Agnostic Implementation of Big Data Mining Algorithms 32

Parallel Primitives for Vendor-Agnostic Implementation of Bi...

引用

32nd IEEE international conference on Advanced Information Networking and applications (IEEE AINA)

作者： Bandirali, Cesare Lodi, Stefano Moro, Gianluca Pagliarani, Andrea Sartori, Claudio D'Angelo, Salvatore Di Martino, Beniamino Esposito, Antonio Univ Bologna Dipartimento Informat Sci & Ingn DISI Viale Risorgimento 2 I-40136 Bologna Italy Univ Campania Luigi Vanvitelli Dipartimento Ingn Via Roma 29 I-81031 Aversa Italy

ISBN: (纸本)9781538653951

In the age of Big Data, scalable algorithm implementations as well as powerful computational resources are required. For data mining and data analytics the support of big data platforms is becoming increasingly important, since they provide algorithm implementations with all the resources needed for their execution. However, choosing the best platform might depend on several constraints, including but not limited to computational resources, storage resources, target tasks, service costs. Sometimes it may be necessary to switch from one platform to another depending on the constraints. As a consequence, it is desirable to reuse as much algorithm code as possible, so as to simplify the setup in new target platforms. Unfortunately each big data platform has its own peculiarity, especially to deal with parallelism. This impacts on algorithm implementation, which generally needs to be modified before being executed. This work introduces functional parallel primitives to define the parallelizable parts of algorithms in a uniform way, independent of the target platform. Primitives are then transformed by a compiler into skeletons, which are finally deployed on vendor-dependent frameworks. The procedure proposed aids not only in terms of code reuse but also in terms of parallelization, because programmer's expertise is not demanded. Indeed, it is the compiler that entirely manages and optimizes algorithm parallelization. The experiments performed show that the transformation process does not negatively affect algorithm performance.

关键词： Big Data Skeleton Data mining Task analysis Sparks Machine learning algorithms Data analysis

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Efficient algorithms for prefix and general prefix computations on distributed shared memory systems with applications

Efficient algorithms for prefix and general prefix computati...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： V. Kamakoti N. Balakrishnan Supercomputer Education and Research Centre Indian Institute of Science Bangalore India

The paper presents efficient scalable algorithms for performing prefix (PC) and general prefix (GPC) computations on a distributed shared memory, (DSM) system with applications. PC and GPC are generic techniques that can be used to design sequential and parallel algorithms for a number of problems from diverse areas (K. Arvind et al., 1995; V. Kamakoti and C. Pandurangan, 1992).

关键词： distributed computing Algorithm design and analysis Concurrent computing parallel algorithms Programming profession Supercomputers Phase change random access memory parallel processing Software systems Scalability

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A distributed Concurrency Control Mechanism for XML Data

A Distributed Concurrency Control Mechanism for XML Data

引用

international conference on parallel processing Workshops (ICPPW)

作者： Leonardo O. Moreira Flavio R.C. Sousa Javam C. Machado Departament of Computer Science Federal University of Ceará Fortaleza Brazil

XML has become a widely used standard for data exchange among applications. Consequently, a large amount of data is distributed on the Web and stored in different persistence models. DBMSs provide concurrency control techniques to manage such data. However, the structure of XML data makes the application of these techniques difficult. Regarding distributed environments, there are few papers available and they all have limitations. This paper introduces DTX, a mechanism for distributed concurrency control for XML data. In order to evaluate DTX, experiments that measure its performance are presented.

关键词： Concurrency control XML Access protocols Concurrent computing Environmental management Proposals parallel processing Java Computer science Application software

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Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs 26th

Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs

引用

26th international conference on parallel and distributed Computing (Euro-Par)

作者： Wang, Qinglin Li, Dongsheng Huang, Xiandong Shen, Siqi Mei, Songzhu Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

ISBN: (纸本)9783030576752;9783030576745

Convolutional Neural Networks (CNNs) are widely applied in various machine learning applications and very time-consuming. Most of CNNs' execution time is consumed by convolutional layers. A common approach to implementing convolutions is the FFT-based one, which can reduce the arithmetic complexity of convolutions without losing too much precision. As the performance of ARMv8 multi-core CPUs improves, they can also be utilized to perform CNNs like Intel X86 CPUs. In this paper, we present a new parallel FFT-based convolution implementation on ARMv8 multi-core CPUs. The implementation makes efficient use of ARMv8 multi-core CPUs through a series of computation and memory optimizations. The experiment results on two ARMv8 multicore CPUs demonstrate that our new implementation gives much better performance than two existing approaches in most cases.

关键词： CNNs Convolution FFT ARMv8 parallel algorithm

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A Data-parallel ILUPACK for Sparse General and Symmetric Indefinite Linear Systems 22nd

引用

22nd international European conference on parallel and distributed Computing (Euro-Par)

作者： Aliaga, Jose I. Bollhofer, Matthias Dufrechou, Ernesto Ezzatti, Pablo Quintana-Orti, Enrique S. Univ Jaime I Dept Ingn & Ciencia Computac Castellon de La Plana Spain TU Braunschweig Inst Computat Math Braunschweig Germany Univ Republica Inst Computac Montevideo Uruguay

ISBN: (纸本)9783319589435;9783319589428

The solution of sparse linear systems of large dimension is a critical step in problems that span a diverse range of applications. For this reason, a number of iterative solvers have been developed, among which ILUPACK integrates an inverse-based multilevel ILU preconditioner with appealing numerical properties. In this paper, we enhance the computational performance of ILUPACK by off-loading the execution of several key computational kernels to a Graphics processing Unit (GPU). In particular, we target the preconditioned GMRES and BiCG methods for sparse general systems and the preconditioned SQMR method for sparse symmetric indefinite problems in ILUPACK. The evaluation on a NVIDIA Kepler GPU shows a sensible reduction of the execution time, while maintaining the convergence rate and numerical properties of the original ILUPACK solver.

关键词： Iterative solvers Preconditioning Incomplete LU (ILU) factorization Sparse triangular linear systems Graphics processing unit (GPU)

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The Impact of Input Error on the Scheduling of Task Graphs with Imprecise Computations in Heterogeneous distributed Real-Time Systems

引用

18th international conference on Analytical and Stochastic Modelling and applications (ASMTA)

作者： Stavrinides, Georgios L. Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

ISBN: (纸本)9783642217135

In real-time systems, it is often more desirable for a job to produce an approximate, imprecise result by its deadline than to produce a precise result late. In this paper, we evaluate by simulation the performance of a heterogeneous distributed real-time system, where composite jobs with end-to-end deadlines are scheduled dynamically as they arrive in the system, utilizing imprecise computations. Each job is a directed acyclic graph of component tasks, where the output data of a task may be used as input by another task. In case the input data of a component task are imprecise, the processing time of the task is extended, in order to correct the error and produce a result of acceptable quality. The impact of input error on the system performance is investigated under various workloads and input error limits.

关键词： heterogeneous distributed real-time systems task graphs imprecise computations input error simulation

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parallel Computing Technologies 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Victor Malyshkin

ISBN: (数字)9783319219097

ISBN: (纸本)9783319219080

This book constitutes the proceedings of the 13th international conference on parallel Computing Technologies, PaCT 2015, held in Petrozavodsk, Russia, during August / September 2015. The 37 full papers and 14 short papers presented were carefully reviewed and selected from 87 submissions. The papers are organized in topical sections on parallel models, algorithms and programming methods; unconventional computing; cellular automata; distributed computing; special processors programming techniques; applications.

关键词： Software Engineering/Programming and Operating Systems Algorithm Analysis and Problem Complexity Numeric Computing Simulation and Modeling Performance and Reliability Computer Systems Organization and Communication Networks

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Smart Car Parking System Solution for the Internet of Things in Smart Cities 1

Smart Car Parking System Solution for the Internet of Things...

引用

1st international conference on Computer applications and Information Security (ICCAIS)

作者： Alsafery, Wael Alturki, Badraddin Reiff-Marganiec, Stephan Jambi, Kamal Univ Leicester Dept Informat Leicester Leics England King Abdulaziz Univ Comp Sci Dept Jeddah Saudi Arabia

ISBN: (纸本)9781538644270

The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.

关键词： Internet of Things (IoT) smart city smart car parking Disruptive Car Parks embedded computing Cloud data mining Fog Computing distributed Data Analytics

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