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检索条件"任意字段=7th International Symposium on Parallel Architectures, Algorithms, and Programming, PAAP 2015"

共 29 条记录，以下是1-10 订阅

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Proceedings - international symposium on parallel architectures, algorithms and programming, paap

Proceedings - International Symposium on Parallel Architectu...

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7th international symposium on parallel architectures, algorithms, and programming, paap 2015

ISBN: (纸本)9781467391177

the proceedings contain 40 papers. the topics discussed include: efficient heuristic for placing monitors on flow networks;efficient implementation of genetic algorithms on GP-GPU with scheduled persistent CUDA threads;exploiting pure superword level parallelism for array indirections;modeling binary oriented software buffer overflow vulnerability in process algebra;OpenISMA: an approach of achieving a scalable OpenFlow network by identifiers separating and mapping;an efficient tolerant-anisotropic localization for large-scale wireless sensor network;distributed processing of approximate range queries in wireless sensor networks;vector localization algorithm based on signal strength in wireless sensor network;and parallel and improvement of pre-computation technique for approximation shortest distance query.

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parallel Architecture, Algorithm and programming 1st ed. 2017

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丛书名： Communications in Computer and Information Science

2017年

作者： Guoliang Chen Hong Shen Mingrui Chen

ISBN: (数字)9789811064425

ISBN: (纸本)9789811064418

this book constitutes the refereed proceedings of the 8th international symposium on parallel Architecture, Algorithm and programming, paap 2017, held in Haikou, China, in June 2017. the 50 revised full papers and 7 revised short papers presented were carefully reviewed and selected from 192 submissions. the papers deal with research results and development activities in all aspects of parallel architectures, algorithms and programming techniques.

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parallel and Improvement of Pre-computation Technique for Approximation Shortest Distance Query 7

Parallel and Improvement of Pre-computation Technique for Ap...

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7th international symposium on parallel architectures, algorithms, and programming, paap 2015

作者： Liu, Huimin Fu, Qiang Zhou, Yinghua School of Computer Science University of Science and Technology of China National High Performance Computing Center at Hefei Hefei China

ISBN: (纸本)9781467391177

With the development of mobile Internet, more and more applications are associated with location. there are many applications just care about shortest distance of two points and do not care about the paths, such as take-away applications. the problem of shortest distance query of objects has caught many researchers' attention in recent years. One of solutions is pre-computation. We can select representative node for each node in graph, and approximation shortest distance of two nodes can be obtained by computing the distance between their representative nodes. the distance between any two representative nodes is pre-computed. In this paper, we propose a more effective algorithm to select representative such that the number of representatives is less than random algorithm, and we use parallel approach to compute the shortest distance between representatives and optimize the data struct to reduce the memory cost of distance result. We evaluate our approach on a real data set. the evaluation result shows the size of representative nodes are less than random algorithm, and the computing time and memory cost reduce effectively. © 2015 IEEE.

关键词： Cost reduction

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Next Generation Cloud Computing Reliability:Issues and Challenges

Next Generation Cloud Computing Reliability:Issues and Chall...

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the 7th international symposium on parallel architectures, algorithms and programming, paap2015(2015年第七届国际并行体系结构,算法和编程研讨会)

作者： Jemal Abawajy Parallel and Distributed Computing Lab School of Information Technology Deakin University Australia

Introduction Cloud computing is widely recognized for its cost savings,scaling and for accelerating business innovation.

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CUDA Grid-Level Task Progression algorithms 17

CUDA Grid-Level Task Progression Algorithms

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Kartsaklis, Christos Joubert, Wayne Hernandez, Oscar R. Eisenbach, Markus Elwasif, Wael R. Bernholdt, David E. Oak Ridge Natl Lab Oak Ridge TN 37831 USA

ISBN: (纸本)9781479989379

Tasking is a prominent parallel programming model. In this paper we conduct a first study into the feasibility of task-parallel execution at the CUDA grid, rather than the stream/kernel level, for regular, fixed in-out dependency task graphs, similar to those found in wavefront computational patterns, making the findings broadly applicable. We propose and evaluate three CUDA task progression algorithms, where threadblocks cooperatively process the task graph, and argue about their performance in terms of tasking throughput, atomics and memory IO overheads. Our initial results demonstrate a throughput of 38 million tasks/second on a Kepler K20X architecture.

关键词： parallel architectures parallel programming CUDA grid-level task progression algorithm CUDA task progression algorithm Kepler K20X architecture parallel programming model wavefront computational pattern Computational modeling Graphics processing units Instruction sets Kernel parallel processing Radiation detectors Runtime

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A parallel Clustering Algorithm for Power Big Data Analysis 8th

A Parallel Clustering Algorithm for Power Big Data Analysis

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8th international symposium on parallel architectures, algorithms, and programming (paap)

作者： Meng, Xiangjun Chen, Liang Li, Yidong State Grid Shandong Power Co Jinan Shandong Peoples R China Shandong Luneng Software Technol Jinan Shandong Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China

ISBN: (纸本)9789811064425;9789811064418

With the fast development of information technology, the power data is growing at an exponentially speed. In the face of multi-dimensional and complicated power network data, the performance of the traditional clustering algorithms are not satisfied. How to effectively cope with the power network data is becoming a hot topic. this paper proposes a parallel implement of K-means clustering algorithm based on Hadoop distributed file system and Mapreduce distributed computing framework to deal this problem. the experimental results show that the performance of our proposed algorithm significantly outperforms the traditional clustering algorithm and the parallel clustering algorithm can significantly reduce the time complexity and can be applied in analyzing and mining of the power network data.

关键词： parallel algorithm K-means clustering Power data

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A parallel BSP algorithm for irregular dynamic programming

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7th international symposium on Advanced parallel Processing Technologies

作者： Low, Malcolm Yoke Hean Liu, Weiguo Schmidt, Bertil Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ New South Wales Asia Singapore Singapore

ISBN: (纸本)9783540768364

Dynamic programming is a widely applied algorithm design technique in many areas such as computational biology and scientific computing. Typical applications using this technique are compute-intensive and suffer from long runtimes on sequential architectures. therefore, several parallel algorithms for both fine-grained and coarse-grained architectures have been introduced. However, the commonly used data partitioning scheme can not be efficiently applied to irregular dynamic programming algorithms, i.e. dynamic programming algorithms with an uneven load density pattern. In this paper we present a tunable parallel Bulk Synchronous parallel (BSP) algorithm for such kind of applications. this new algorithm can balance the workload among processors using a tunable block-cyclic data partitioning method and thus is capable of getting almost linear performance gains. We present a theoretical analysis and experimentally show that it leads to significant runtime savings for pairwise sequence alignment with general gap penalties using BSPonMPI on a PC cluster.

关键词： BSP irregular dynamic programming partitioning load balancing scientific computing

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Application Modeling for Scalable Simulation of Massively parallel Systems 17

Application Modeling for Scalable Simulation of Massively Pa...

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Anger, Eric Yalamanchili, Sudhakar Dechev, Damian Hendry, Gilbert Wilke, Jeremiah Georgia Inst Technol Atlanta GA 30332 USA Univ Cent Florida Orlando FL USA Sandia Natl Labs Livermore CA USA

ISBN: (纸本)9781479989379

Macro-scale simulation has been advanced as one tool for application-architecture co-design to express operation of exascale systems. these simulations approximate the behavior of system components, trading off accuracy for increased evaluation speed. Application skeletons serve as the vehicle for these simulations, but they require accurately capturing the execution behavior of computation. the complexity of application codes, the heterogeneity of the platforms, and the increasing importance of simulating multiple performance metrics (e. g., execution time, energy) require new modeling techniques. We propose flexible statistical models to increase the fidelity of application simulation at scale. We present performance model validation for several exascale mini-applications that leverage a variety of parallel programming frameworks targeting heterogeneous architectures for both time and energy performance metrics. When paired with these statistical models, application skeletons were simulated on average 12.5 times faster than the original application incurring only 6.08% error, which is 12.5% faster and 33.7% more accurate than baseline models.

关键词： parallel architectures parallel programming power aware computing principal component analysis application-architecture codesign energy performance metrics exascale systems flexible statistical model heterogeneous architectures massively parallel systems parallel programming frameworks performance metrics performance model scalable simulation modeling statistical model time performance metrics Analytical models Computational modeling Data models Hardware Load modeling Predictive models Skeleton Performance metrics statistical models (nuclear) power aware computing parallel programming Skeletons data models Load modeling parallel architectures parallel system ANALYSIS MODELS Computational modeling Prediction models bone structure Computer hardware simulating

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Optimal Performance Prediction of ADAS algorithms on Embedded parallel architectures 17

Optimal Performance Prediction of ADAS Algorithms on Embedde...

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Saussard, Romain Bouzid, Boubker Vasiliu, Marius Reynaud, Roger Renault SAS Guyancourt France Univ Paris 11 Inst Elect Fondamentale Orsay France

ISBN: (纸本)9781479989379

ADAS (Advanced Driver Assistance Systems) algorithms increasingly use heavy image processing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. these SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of image processing algorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.

关键词： Heterogeneous architectures Embedded Systems Performance Prediction Image Processing

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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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