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共 68 条记录，以下是11-20 订阅

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More Effective Distributed Deep Learning Using Staleness Based Parameter Updating 18th

More Effective Distributed Deep Learning Using Staleness Bas...

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

作者： Ye, Yan Chen, Mengqiang Yan, Zijie Wu, Weigang Xiao, Nong Sun Yet Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou Peoples R China MoE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030050542;9783030050535

Deep learning technology has been widely applied for various purposes, especially big data analysis. However, computation required for deep learning is getting more complex and larger. In order to accelerate the training of large-scale deep networks, various distributed parallel training protocols have been proposed. In this paper, we design a novel asynchronous training protocol, Weighted Asynchronous parallel (WASP), to update neural network parameters in a more effective way. the core of WASP is "gradient staleness", a parameter version number based metric to weight gradients and reduce the influence of the stale parameters. Moreover, by periodic forced synchronization of parameters, WASP combines the advantages of synchronous and asynchronous training models and can speed up training with a rapid convergence rate. We conduct experiments using two classical convolutional neural networks, LeNet-5 and ResNet-101, at the Tianhe-2 supercomputing system, and the results show that, WASP can achieve much higher acceleration than existing asynchronous parallel training protocols.

关键词： Distributed deep learning parallel computing Parameter server Asynchronous parallel Supercomputing system

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Power-Performance Evaluation of parallel Multi-objective EEG Feature Selection on CPU-GPU Platforms 17th

Power-Performance Evaluation of Parallel Multi-objective EEG...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Jose Escobar, Juan Ortega, Julio Francisco Diaz, Antonio Gonzalez, Jesus Damas, Miguel Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain

ISBN: (纸本)9783319654829;9783319654812

Heterogeneous CPU-GPU platforms include resources to benefit from different kinds of parallelism present in many data mining applications based on evolutionary algorithms that evolve solutions with time-demanding fitness evaluation. this paper describes an evolutionary parallel multi-objective feature selection procedure with subpopulations using two scheduling alternatives for evaluation of individuals according to the number of subpopulations. Evolving subpopulations usually provides good diversity properties and avoids premature convergence in evolutionary algorithms. the proposed procedure has been implemented in OpenMP to distribute dynamically either subpopulations or individuals among devices and OpenCL to evaluate the individuals taking into account the devices characteristics, providing two parallelism levels in CPU and up to three levels in GPUs. Different configurations of the proposed procedure have been evaluated and compared with a master-worker approach considering not only the runtime and achieved speedups but also the energy consumption between both scheduling models.

关键词： Heterogeneous subpopulations scheduling Energy-aware computing EEG classification Multi-objective feature selection GPU

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A Hybrid parallel Search Algorithm for Solving Combinatorial Optimization Problems on Multicore Clusters 17th

A Hybrid Parallel Search Algorithm for Solving Combinatorial...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata III LIDI Sch Comp Sci La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783319654829;9783319654812

Multicore clusters are widely used to solve combinatorial optimization problems, which require high computing power and a large amount of memory. In this sense, Hash Distributed A* (HDA*) parallelizes A*, a combinatorial optimization algorithm, using the MPI library. HDA* scales well on multicore clusters and on multicore machines. Additionally, there exist several versions of HDA* that were adapted for multicore machines, using the Pthreads library. In this paper, we present Hybrid HDA* (HHDA*), a hybrid parallel search algorithm based on HDA* that combines message-passing (MPI) with shared-memory programming (Pthreads) to better exploit the computing power and memory of multicore clusters. We evaluate the performance and memory consumption of HHDA* on a multicore cluster, using the 15-puzzle as a case study. the results reveal that HHDA* achieves a slightly higher average performance and uses considerably less memory than HDA*. these improvements allowed HHDA* to solve one of the hardest 15-Puzzle instances.

关键词： parallel search algorithms Hybrid programming Multicore cluster Combinatorial optimization problems Hash Distributed A

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On the Use of Binary Trees for DNA Hydroxymethylation Analysis 17th

On the Use of Binary Trees for DNA Hydroxymethylation Analys...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Gonzalez, Cesar Perez, Mariano Orduna, Juan M. Chaves, Javier Garcia, Ana-Barbara Univ Valencia Dept Informat Avda Univ S-N Valencia 46100 Spain CIBERDEM Carlos III Hlth Inst INCLIVA Hlth Res Inst Avda Menendez Pelayo 4 Accesorio Valencia 46010 Spain

ISBN: (纸本)9783319654829;9783319654812

DNA methylation (mC) and hydroxymethylation (hmC) can have a significant effect on normal human development, health and disease status. Hydroxymethylation studies require specific treatment of DNA, as well as software tools for their analysis. In this paper, we propose a parallel software tool for analyzing the DNA hydroxymethylation data obtained by TAB-seq. the software is based on the use of binary trees for searching the different occurrences of methylation and hydroxymethylation in DNA samples. the binary trees allow to efficiently store and access the information about the methylation of each methylated/hydroxymethylated cytosines in the samples. Evaluation results shows that the performance of the application is only limited by the computer input/output bandwidth, even for the case of very long samples.

关键词： High performance computing DNA hydroxymethylation parallel pipeline

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Improving Hash Distributed A* for Shared Memory architectures Using Abstraction 16th

Improving Hash Distributed A* for Shared Memory Architecture...

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

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata Sch Comp Sci III LIDI La Plata Argentina Minist Sci Technol & Product Innovat CONICET Buenos Aires DF Argentina

ISBN: (纸本)9783319495835;9783319495828

the A* algorithm is generally used to solve combinatorial optimization problems, but it requires high computing power and a large amount of memory, hence, efficient parallel A* algorithms are needed. In this sense, Hash Distributed A* (HDA*) parallelizes A* by applying a decentralized strategy and a hash-based node distribution scheme. However, this distribution scheme results in frequent node transfers among processors. In this paper, we present Optimized AHDA*, a version of HDA* for shared memory architectures, that uses an abstraction-based node distribution scheme and a technique to group several nodes before transferring them to the corresponding thread. Both methods reduce the amount of node transfers and mitigate communication and contention. We assess the effect of each technique on algorithm performance. Finally, we evaluate the scalability of the proposed algorithm, when it is run on a multicore machine, using the 15-puzzle as a case study.

关键词： HDA* for shared memory architectures Abstraction-based node distribution scheme Hash-based node distribution scheme Scalability Combinatorial optimization problems

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Statistical Analysis of CCM.M-K1 international Comparison Based on Monte Carlo Method 16th

Statistical Analysis of CCM.M-K1 International Comparison Ba...

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

作者： Cai, Chang-qing Ren, Xiao-ping Hao, Guo-dong Wang, Jian Huang, Tao Natl Inst Metrol Beijing 100029 Peoples R China Cent S Univ Dept Intelligence Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9783319499567;9783319499550

the application of the Monte Carlo method is used in the processing of the measurement result of CCM.M-K1. this method can get over the limitations that apply in certain cases to the method described in GUM. Introduction and analysis of CCM.M-K1 measurement result was given out and commercial software named @RISK was used to purse numerical simulation and the result was compared with the final report of CCM.M-K1, which showed that differences between results of these two were negligible.

关键词： Key comparison Reference value Degree of equivalence Monte carlo method

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Shared Memory Tile-Based vs Hybrid Memory GOP-Based parallel algorithms for HEVC Encoder 16th

Shared Memory Tile-Based vs Hybrid Memory GOP-Based Parallel...

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

作者： Migallon, Hector Lopez-Granado, Otoniel Galiano, Vicente Pinol, Pablo Malumbres, Manuel P. Miguel Hernandez Univ Dept Phys & Comp Architecture Avda Univ S-N Alicante 03202 Spain

ISBN: (纸本)9783319495835;9783319495828

After the emergence of the new High Efficiency Video Coding standard, several strategies have been followed in order to take advantage of the parallel features available in it. Many of the parallelization approaches in the literature have been performed in the decoder side, aiming at achieving real-time decoding. However, the most complex part of the HEVC codec is the encoding side. In this paper, we perform a comparative analysis of two parallelization proposals. One of them is based on tiles, employing shared memory architectures and the other one is based on Groups Of Pictures, employing distributed shared memory architectures. the results show that good speed-ups are obtained for the tile-based proposal, especially for high resolution video sequences, but the scalability decreases for low resolution video sequences. the GOP-based proposal outperforms the tile-based proposal when the number of processes increases. this benefit grows up when low resolution video sequences are compressed.

关键词： HEVC Video coding parallel encoding Shared memory Distributed shared memory

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Automated parallel Simulation of Heart Electrical Activity Using Finite Element Method 16th

Automated Parallel Simulation of Heart Electrical Activity U...

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

作者： Sozykin, Andrey Epanchintsev, Timofei Zverev, Vladimir Khamzin, Svyatoslav Bersenev, Aleksandr Krasovskii Inst Math & Mech Ekaterinburg Russia Inst Immunol & Physiol UrB RAS Ekaterinburg Russia Ural Fed Univ Ekaterinburg Russia

ISBN: (纸本)9783319499567;9783319499550

In this paper we present an approach to the parallel simulation of the heart electrical activity using the finite element method with the help of the FEniCS automated scientific computing framework. FEniCS allows scientific software development using the near-mathematical notation and provides automatic parallelization on MPI clusters. We implemented the ten Tusscher-Panfilov (TP06) cell model of cardiac electrical activity. the scalability testing of the implementation was performed using up to 240 CPU cores and the 95 times speedup was achieved. We evaluated various combinations of the Krylov parallel linear solvers and the preconditioners available in FEniCS. the best performance was provided by the conjugate gradient method and the biconjugate gradient stabilized method solvers with the successive over-relaxation preconditioner. Since the FEniCS-based implementation of TP06 model uses notation close to the mathematical one, it can be utilized by computational mathematicians, biophysicists, and other researchers without extensive parallel computing skills.

关键词： Heart simulation Finite element method Scalability Krylov subspace methods FEniCS parallel computing

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Efficient parallel Algorithm for Optimal DAG Structure Search on parallel Computer with Torus Network 16th

Efficient Parallel Algorithm for Optimal DAG Structure Searc...

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

作者： Honda, Hirokazu Tamada, Yoshinori Suda, Reiji Univ Tokyo Grad Sch Informat Sci & Technol Tokyo 1138656 Japan

ISBN: (纸本)9783319495835;9783319495828

the optimal directed acyclic graph search problem constitutes searching for a DAG with a minimum score, where the score of a DAG is defined on its structure. this problem is known to be NP-hard, and the state-of-the-art algorithm requires exponential time and space. It is thus not feasible to solve large instances using a single processor. Some parallel algorithms have therefore been developed to solve larger instances. A recently proposed parallel algorithm can solve an instance of 33 vertices, and this is the largest solved size reported thus far. In the study presented in this paper, we developed a novel parallel algorithm designed specifically to operate on a parallel computer with a torus network. Our algorithm crucially exploits the torus network structure, thereby obtaining good scalability. through computational experiments, we confirmed that a run of our proposed method using up to 20,736 cores showed a parallelization efficiency of 0.94 as compared to a 1296-core run. Finally, we successfully computed an optimal DAG structure for an instance of 36 vertices, which is the largest solved size reported in the literature.

关键词： Optimal DAG structure Optimal bayesian network structure parallel algorithm Distributed algorithm Torus network

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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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