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检索条件"任意字段=6th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP"

共 292 条记录，以下是141-150 订阅

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Generic parallel Genetic Algorithm Framework for Protein Optimisation

Generic Parallel Genetic Algorithm Framework for Protein Opt...

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11th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Folkman, Lukas Pullan, Wayne Stantic, Bela Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia

ISBN: (纸本)9783642246685

Proteins are one of the most vital macromolecules on the cellular level. In order to understand the function of a protein, its structure needs to be determined. For this purpose, different computational approaches have been introduced. Genetic algorithms can be used to search the vast space of all possible conformations of a protein in order to find its native structure. A framework for design of such algorithms that is both generic, easy to use and performs fast on distributed systems may help further development of genetic algorithm based approaches. We propose such a framework based on a parallel master-slave model which is implemented in C++ and Message Passing interface. We evaluated its performance on distributed systems with a different number of processors and achieved a linear acceleration in proportion to the number of processing units.

关键词： parallel Genetic Algorithm Protein Optimisation Protein Structure Prediction

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GPU-Based Heterogeneous Coding Architecture for HEVC 16th

GPU-Based Heterogeneous Coding Architecture for HEVC

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16th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Cebrian-Marquez, Gabriel Migallon, Hector Luis Martinez, Jose Lopez-Granado, Otoniel Pinol, Pablo Cuenca, Pedro Univ Castilla La Mancha Albacete Res Inst Informat I3A Plz Univ 2 Albacete 02071 Spain Miguel Hernandez Univ Dept Phys & Comp Architecture Elche 03202 Spain

ISBN: (纸本)9783319495835;9783319495828

the High Efficiency Video Coding (HEVC) standard has nearly doubled the compression efficiency of prior standards. Nonetheless, this increase in coding efficiency involves a notably higher computing complexity that should be overcome in order to achieve real-time encoding. For this reason, this paper focuses on applying parallel processing techniques to the HEVC encoder with the aim of reducing significantly its computational cost without affecting the compression performance. Firstly, we propose a coarse-grained slice-based parallelization technique that is executed in a multi-core CPU, and then, with finer level of parallelism, a GPU-based motion estimation algorithm. Both techniques define a heterogeneous parallel coding architecture for HEVC. Results show that speed-ups of up to 4.06x can be obtained on a quad-core platform with low impact in coding performance.

关键词： H.265 HEVC Heterogeneous parallel encoding GPU

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A Platform for Stock Market Simulation with Distributed Agent-Based Modeling 1

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

作者： Wang, Chunyu Yu, Ce Wu, Hutong Chen, Xiang Li, Yuelei Zhang, Xiaotao Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Tianjin Univ Sch Econ & Management Tianjin Peoples R China

ISBN: (数字)9783319111940

ISBN: (纸本)9783319111940;9783319111933

Agent-based modeling (ABM) has been widely used in stock market simulation. However, traditional simulations of stock markets with ABM on single computers are limited by the computing capability as breakthroughs in financial research need much larger amount of agents. this paper introduces a platform for stock market simulation with ABM focusing on large scale parallel agents in a distributed computing environment such as Cluster and Mpp. With the customized trade strategies inside the agents, the runtime system of the platform can distribute the massive amount of agents to multiple computing nodes automatically during the execution of the simulation. And agents exchange information with each other and the market through a uniform communication system. With this platform financial researchers can design their own financial model without caring about the complexity of parallelization and related problems. the sample simulation on the platform is verified to be compatible with the data from Euronext-NYSE and the platform shows fair scalability and performance under different parallelism configurations.

关键词： stock market simulation agent-based modeling parallel distributed environment

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CC-MOEA: A parallel Multi-objective Evolutionary Algorithm for Recommendation Systems 20th

CC-MOEA: A Parallel Multi-objective Evolutionary Algorithm f...

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

作者： Wei, Guoshuai Wu, Quanwang Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Recommender systems have nowadays been widely used in a variety of applications such as Amazon and Ebay. Traditional recommendation techniques mainly focus on recommendation accuracy only. In reality, other metrics such as diversity and novelty also play a key role for modern recommendation systems. Although some works based on multi-objective evolutionary algorithm have been proposed for multi-objective recommendation, they are usually very time-consuming because of the large data size of the RSs and the long-term evolution iterations and hence it greatly limits their application in practice. To address these shortcomings, this paper first designs a multi-objective recommendation system, taking into account diversity and novelty as well as accuracy. then, a novel parallel multi-objective evolutionary algorithm called CC-MOEA is proposed to optimize these conflicting metrics. CC-MOEA is devised grounded on NSGA-II and a cooperative coevolutionary island model, and a parallel global non-dominated selection method is introduced to reduce the runtime of finding the global optimal individuals. Furthermore, a new initialization method and a crossover operator are specifically designed. the experimental results reveal that CC-MOEA outperforms some state-of-the-art algorithms in terms of hypervolume and runtime.

关键词： Accuracy Cooperative coevolutionary Multi-objective evolutionary algorithm parallel Recommendation system

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Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU 23rd

Bayesian Optimization for Auto-tuning Convolution Neural Net...

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Zhu, Huming Liu, Chendi Zhang, Lingyun Dong, Ximiao Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

ISBN: (纸本)9789819708109;9789819708116

GPU as a hardware processor plays an important role in the training of deep neural networks. However, when using GPUs for computation on convolutional neural network models, different combinations of GPU kernel configuration parameters have different performance. therefore, this paper proposes BAGF, a bayesian auto-tuning framework for GPU kernels, which parameterizes the factors affecting the performance of GPU programs and uses bayesian optimization methods to search for the best parameters in the search space consisting of the parameters. Compared with other optimization algorithms, BAGF obtains excellent configuration parameters with fewer iterations. this paper analyzes the performance of BAGF on four benchmarks and compares with other common optimization algorithms. In addition, the performance improvement of each parameter configuration is analyzed. Finally, the BAGF was tested with the convolution layer of Alexnet, and the results of the Roofline model were analyzed. Compared with the original parameter configuration, the speed of BAGF was increased by 50.09%.

关键词： CNN parallel computing GPU Auto-tuning

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An Advanced Data Redistribution Approach to Accelerate the Scale-Down Process of RAID-6

An Advanced Data Redistribution Approach to Accelerate the S...

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

作者： Du, Congjin Wu, Chentao Li, Jie Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Key Lab Scalable Comp & Syst Shanghai 200240 Peoples R China

ISBN: (纸本)9783319111940;9783319111933

Nowadays RAID is widely used with the increasing requirements of the reliability in storage systems and the fast development of cloud computing. Among various levels and implementations of RAID systems, RAID-6 is one of the most significant category with the ability to tolerate concurrent failures of any two disks. However, the scalability of RAID-6 is a big challenge. Although many approaches are proposed to accelerate the scaling process and reduce the overhead, how to efficiently remove disks (refers to scale-down process) from existing array is still an open problem. To address the scalability problem, we propose an Advanced Data Redistribution (ADR) approach. the basic idea of ADR is to reorganize previous stripes in RAID-6 systems to achieve higher scalability. ADR is a stripe-level scheme and can be combined with other approaches as SDM and MDS-Frame. It can minimize the overhead of data migration and parity modification. We have conducted mathematical analysis by comparing ADR to various popular RAID-6 codes. the results show that, compared to typical approach (Round-Robin), ADR decreases more than 52.1% migration I/O operations, saves the migration time by up to 63.5%, and speeds up of the scaling process by up to 1.91.

关键词： RAID-6 MDS Code Performance Evaluation Scalability Scale-Down Reliability

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ParaMoC: A parallel Model Checker for Pushdown Systems 19th

ParaMoC: A Parallel Model Checker for Pushdown Systems

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

作者： Wei, Hansheng Ye, Xin Shi, Jianqi Huang, Yanhong East China Normal Univ Natl Trusted Embedded Software Engn Technol Res C Shanghai Peoples R China Res Ctr Hardware Software Codesign Technol & Applicat Eng Shanghai Peoples R China Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China LIPN Paris France Paris Univ 7 Paris France

ISBN: (纸本)9783030389611;9783030389604

Model checking on Pushdown Systems (PDSs) has been extensively used to deal with numerous practical problems. However, the existing model checkers for pushdown systems are executed on the central processing unit (CPU), the performance is hampered by the computing power of the CPU. Compared with the CPU, the graphics processing unit (GPU) has more processing cores, which are suitable and efficient for parallel computing. therefore, it is very attractive to accelerate model checking of PDSs on the GPU. In this paper, we present a new parallel model checker, named ParaMoC, to speed up the performance of model checking problems for pushdown systems (PDSs). Moreover, we focus on how to use Graphics processing Units (GPUs) to accelerate the reachability verification and the LTL model checking of PDSs. the ParaMoC running on a state-of-the-art GPU can be 100 times faster than the traditional PDS model checker.

关键词： Graphics processing unit

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DOS-GAN: A Distributed Over-Sampling Method Based on Generative Adversarial Networks for Distributed Class-Imbalance Learning 20th

DOS-GAN: A Distributed Over-Sampling Method Based on Generat...

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

作者： Guan, Hongtao Ma, Xingkong Shen, Siqi Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9783030602482;9783030602475

Class-imbalance Learning is one of the hot research issues in machine learning. In the practical application of distributed class-imbalance learning, data continues to arrive, which often leads to classimbalance situations. the imbalance problem in the distributed scenario is particular: the imbalanced state of different nodes may be complementary. the imbalanced states of different nodes may be complementary. Using this complementary relationship to do oversampling to change the imbalanced state is a valuable method. However, the data island limits data sharing in this case between the nodes. To this end, we propose DOS-GAN, which can take turns to use the data of one same class data on multiple nodes to train the global GAN model, and then use this GAN generator to oversampling the class without the original data being exchanged. Extensive experiments confirm that DOS-GAN outperforms the combination of traditional methods and achieves classification accuracy closes to the method of data aggregating.

关键词： Class-imbalance learning Distributed learning Generative adversarial networks Data isolated islands

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parallel Communication Mechanisms in Solving Integral Equations for Electromagnetic Scattering Based on the Method of Moments 18th

Parallel Communication Mechanisms in Solving Integral Equati...

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

作者： Song, Lan Peters, Dennis K. Huang, Weimin Liu, Zhiwei Lei, Lixia Wen, Tangliu Wuhan Univ Sch Comp State Key Lab Software Engn Wuhan 430072 Hubei Peoples R China East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Mem Univ Fac Engn & Appl Sci Elect & Comp Engn St John NF Canada

ISBN: (纸本)9783030050511;9783030050504

In this paper, a parallel solution of impedance filling was studied to improve the efficiency for the method of moments (MOM) in solving the integral equation for electromagnetic scattering. Based on the formalization method, the correctness verification method of the parallel communication protocol was proposed to avoid the abnormal situation such as deadlock caused by unsynchronized MPI message delivery. Finally, a numerical example is given to verify the accuracy of the parallel algorithm in a multi-core cluster environment, and the parallel efficiency and computational capability are also tested. From the experiment data, it can be seen that the results of the parallel-based integral equation match well with the results of the Mie analytical solution. the parallel algorithm proposed in this paper is evaluated in terms of the acceleration ratio and parallel efficiency. the experimental results demonstrate the reliability and effectiveness of the method.

关键词： Formal verification parallel communication Electromagnetic scattering Petri net

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Asynchronous parallel Dijkstra's Algorithm on Intel Xeon Phi Processor How to Accelerate Irregular Memory Access Algorithm 18th

Asynchronous Parallel Dijkstra's Algorithm on Intel Xeon Phi...

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

作者： Zhang, Weidong Zhang, Lei Chen, Yifeng Peking Univ EECS Beijing 100871 Peoples R China HuaWei Inc Shenzhen Peoples R China

ISBN: (纸本)9783030050511;9783030050504

As the instruction-level parallelism (ILP) on CPU develops to a rather advanced level, the exploration that whether many-core architecture is applicable for graph algorithms is generating more interests in researchers. However, due to the irregular memory access and the low ratio of computation to memory access, the performance of graph algorithms on many-core architectures has never worked good enough. To obtain outstanding speedup on many-core architecture, first of all, we need to figure out three questions: (i) how to optimize the memory access, (ii) how to minimize the overhead of synchronization, (iii) how to exploit the parallelism in algorithm. Prior works hardly reach the goal if such questions are treated in separated way. throughout this paper, we aim to settle these questions systematically, and try to provide a set of methods of optimizing graph algorithms on many-core architecture. this paper mainly discusses how to accelerate the Single Source Shortest Path (SSSP) problem on Intel Many Integrated Core (MIC) architecture, on which we propose an asynchronous parallel Dijkstra's algorithm. It aims at maximizing parallelism and minimizing overhead of synchronization. Experimental result shows that the MIC architecture could efficiently solve the SSSP problem, and its performance could be sped up by 9.2x compared to the benchmark of DIMACS.

关键词： Single source shortest path parallel algorithm Intel MIC Road network

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