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检索条件"任意字段=10th International Conference Algorithms and Architectures for Parallel Processing, ICA3PP 2010"

共 21 条记录，以下是11-20 订阅

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Distributing Data in Real Time Spatial Data Warehouse 20th

Distributing Data in Real Time Spatial Data Warehouse

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Hamdi, Wael Faiz, Sami Manouba Univ Campus Univ Manouba Manouba 2010 Tunisia LTSIRS Lab BP 37 Le Belvedere Tunis 1002 Tunisia

ISBN: (纸本)9783030602390;9783030602383

Nowadays, there are many real-time spatial applications like location-aware services and traffic monitoring and the need for real time spatial data processing becomes more and more important. As a result, there is a tremendous amount of real-time spatial data in real-time spatial data warehouse. the continuous growth in the amount of data seems to outspeed the advance of the traditional centralized real-time spatial data warehouse. As a solution, many organizations use distributed real-time spatial data warehouse (DRTSDW) as a powerful technique to achieve OLAP (On Line Analytical processing) analysis and business intelligence (BI). Distributing data in real time data warehouse is divided into two steps: partitioning data and their allocation into sites. Several works have proposed many algorithms for partitioning and allocation data. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly, especially in overload situations. In order to deal with this volumetry and to increase query efficiency, we propose a novel approach for partitioning data in real-time spatial data warehouse to find the right number of clusters and to divides the RTSDW into partitions using the horizontal partitioning. Secondly, we suggest our allocation strategy to place the partitions on the sites where they are most used, to minimize data transfers between sites. We have evaluated those proposed approaches using the new TPC-DS (Transaction processing performance council, http://***, 2014) benchmark. the preliminary results show that the approach is quite interesting.

关键词： Distributed real-time spatial data warehouse Data partitioning approach Data allocation Real time transactions

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A massively parallel hardware for modular exponentiations using the m-ary method

A massively parallel hardware for modular exponentiations us...

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10th international conference algorithms and architectures for parallel processing, ica3pp 2010

作者： Farias, Marcos Santana De Souza Raposo, S. Nedjah, Nadia De Macedo Mourelle, L. Department of Electronics Engineering and Telecommunications Engineering Faculty State University of Rio de Janeiro Brazil Department of System Engineering and Computation Engineering Faculty State University of Rio de Janeiro Brazil

ISBN: (纸本)3642131352

Most of cryptographic systems are based on modular exponentiation. It is performed using successive modular multiplications. One way of improving the throughput of a cryptographic system implementation is reducing the number of the required modular multiplications. Existing methods attempt to reduce this number by partitioning the exponent in constant or variable size windows. In this paper, in the purpose of further accelerating the computation of modular exponentiation, a concurrent novel approach is proposed along with hardware implementation of the concurrent m-ary method. We compare the proposed method to the sequential implementation. © Springer-Verlag Berlin Heidelberg 2010.

关键词： Cryptography

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Rise the Momentum: A Method for Reducing the Training Error on Multiple GPUs 19th

Rise the Momentum: A Method for Reducing the Training Error ...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Tang, Yu Yin, Lujia Zhang, Zhaoning Li, Dongsheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Deep neural network training is a common issue that is receiving increasing attention in recent years and basically performed on Stochastic Gradient Descent or its variants. Distributed training increases training speed significantly but causes precision loss at the mean time. Increasing batchsize can improve training parallelism in distributed training. However, if the batchsize is too large, it will bring difficulty to training process and introduce more training error. In this paper, we consider controlling the total batchsize and lowering batchsize on each GPU by increasing the number of GPUs in distributed training. We train Resnet50 [4] on CIFAR-10 dataset by different optimizers, such as SGD, Adam and NAG. the experimental results show that large batchsize speeds up convergence to some degree. However, if the batchsize of per GPU is too small, training process fails to converge. Large number of GPUs, which means a small batchsize on each GPU declines the training performance in distributed training. We tried several ways to reduce the training error on multiple GPUs. According to our results, increasing momentum is a well-behaved method in distributed training to improve training performance on condition of multiple GPUs of constant large batchsize.

关键词： Multiple GPUs Batchsize Distributed training Momentum

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A parallel Fast Fourier Transform Algorithm for Large-Scale Signal Data Using Apache Spark in Cloud 18th

A Parallel Fast Fourier Transform Algorithm for Large-Scale ...

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18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Yang, Cheng Bao, Weidong Zhu, Xiaomin Wang, Ji Xiao, Wenhua Natl Univ Def Technol Changsha Peoples R China State Key Lab High Performance Comp Changsha Peoples R China Acad Mil Med Sci Beijing Peoples R China

ISBN: (纸本)9783030050573;9783030050566

In the field of signal process, Fast Fourier Transform (FFT) is a widely used algorithm to transform signal data from time to frequency. Unfortunately, with the exponential growth of data, traditional methods cannot meet the demand of large-scale computation on these big data because of three main challenges of large-scale FFT, i.e., big data size, real-time data processing and high utilization of compute resources. To satisfy these requirements, an optimized FFT algorithm in Cloud is deadly needed. In this paper, we introduce a new method to conduct FFT in Cloud with the following contributions: first, we design a parallel FFT algorithm for large-scaled signal data in Cloud;second, we propose a MapReduce-based mechanism to distribute data to compute nodes using big data processing framework;third, an optimal method of distributing compute resources is implemented to accelerate the algorithm by avoiding redundant data exchange between compute nodes. the algorithm is designed in MapReduce computation framework which contains three steps: data preprocessing, local data transform and parallel data transform to integrate processing results. the parallel FFT is implemented in a 16-node Cloud to process real signal data the experimental results reveal an obvious improvement in the algorithm speed. Our parallel FFT is approximately five times faster than FFT in Matlab in when the data size reaches 10 GB.

关键词： Fast fourier transform Cloud computing Apache spark parallel algorithm

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parallel Data processing in Dynamic Hybrid Computing Environment Using MapReduce 1

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14th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Tang, Bing He, Haiwu Fedak, Gilles Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Univ Lyon INRIA UCB Lyon 5668 UMR CNRS ENS Lyon LIP Lab Lyon France

ISBN: (数字)9783319111940

ISBN: (纸本)9783319111940;9783319111933

A novel MapReduce computation model in hybrid computing environment called HybridMR is proposed in the paper. Using this model, high performance cluster nodes and heterogeneous desktop PCs in Internet or Intranet can be integrated to form a hybrid computing environment. In this way, the computation and storage capability of large-scale desktop PCs can be fully utilized to process large-scale datasets. HybridMR relies on a hybrid distributed file system called HybridDFS, and a time-out method has been used in HybridDFS to prevent volatility of desktop PCs, and file replication mechanism is used to realize reliable storage. A new node priority-based fair scheduling (NPBFS) algorithm has been developed in HybridMR to achieve both data storage balance and job assignment balance by assigning each node a priority through quantifying CPU speed, memory size and I/O bandwidth. Performance evaluation results show that the proposed hybrid computation model not only achieves reliable MapReduce computation, reduces task response time and improves the performance of MapReduce, but also reduces the computation cost and achieves a greener computing mode.

关键词： Hybrid Computing Environment Distributed File System MapReduce Volunteer Computing Fault-tolerance

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algorithms and architectures for parallel processing 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Rocco Aversa Joanna Kołodziej Jun Zhang Flora Amato Giancarlo Fortino

ISBN: (数字)9783319038896

ISBN: (纸本)9783319038889

this two volume set LNCS 8285 and 8286 constitutes the proceedings of the 13th international conference on algorithms and architectures for parallel processing , ica3pp 2013, held in Vietri sul Mare, Italy in December 2013. the first volume contains 10 distinguished and 31 regular papers selected from 90 submissions and covering topics such as big data, multi-core programming and software tools, distributed scheduling and load balancing, high-performance scientific computing, parallel algorithms, parallel architectures, scalable and distributed databases, dependability in distributed and parallel systems, wireless and mobile computing. the second volume consists of four sections including 35 papers from one symposium and three workshops held in conjunction with ica3pp 2013 main conference. these are 13 papers from the 2013 international Symposium on Advances of Distributed and parallel Computing (ADPC 2013), 5 papers of the international Workshop on Big Data Computing (BDC 2013), 10 papers of the international Workshop on Trusted Information in Big Data (TIBiDa 2013) as well as 7 papers belonging to Workshop on Cloud-assisted Smart Cyber-Physical Systems (C-Smart CPS 2013).

关键词： Algorithm Analysis and Problem Complexity Software Engineering Computer Communication Networks Database Management

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M2A2 2010 foreword

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2010年第PART 2期6082 LNCS卷 xii页

作者： Hassan, Houcine Sahuquillo, Julio

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HPCTA 2010 foreword

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2010年第PART 2期6082 LNCS卷 xi页

作者： Tsai, Whey Fone Chang, Hsi-Ya Hsu, Ching-Hsien Huang, Kuo-Chan

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GridFTP and parallel TCP support in NaradaBrokering 1

引用

6th international conference on algorithms and architectures for parallel processing, ica3pp

作者： Lim, Sang Boem Fox, Geoffrey Kaplan, Ali Pallickara, Shrideep Pierce, Marion Daejeon Korea Republic of Pervasive Technology Labs. Indiana University Bloomington IN 47404-3730 United States

ISBN: (数字)9783540320715

ISBN: (纸本)3540292357

Many of the key features of file transfer mechanisms like reliable file transferring and parallel transferring are developed as part of the service. It makes very hard to re-use the same code for the different systems. We are trying to overcome this disadvantage by decoupling useful features of file transfer mechanisms from the implementation of the service and protocol, and instead placed into the messaging substrate. We may thus treat file transfer operations as a specific usage case for a more general messaging environment. this will allow us to provide file transfer quality of service to other file transfer tools that does not have same features. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Distributed computer systems

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Performance characterization and optimization for intel xeon phi coprocessor 1

引用

15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Zhang, Cheng Liu, Li Li, Ruizhe Yang, Guangwen Department of Computer Science and Technology Tsinghua University Beijing100084 China Center for Earth System Science Tsinghua University Beijing100084 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

the Intel Xeon Phi is a many-core accelerator which focuses on the high performance applications. To characterize the performance of the Intel Xeon Phi, a system of dual 8-core Intel Xeon E5-2670 processors is employed as a control platform, and a subset of the PARSEC benchmark suite is selected as the benchmark applications. the first evaluation in this paper shows that the applications on the Intel Xeon Phi is averagely 2. 06x slower than on the dual Intel Xeon E5-2670. the further detailed performance characterization quantifies the performance impact of various architecture parameters on the Intel Xeon Phi. To set an example for how to improve the architecture of the Intel Xeon Phi for better performance, the hardware optimization with an additional set of vector processing units is discussed and a simple emulator is developed accordingly. the evaluation results show that this optimization can provide an average speedup of 1. 10. © Springer international Publishing Switzerland 2015.

关键词： parallel architectures

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