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检索条件"任意字段=16th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2016"

共 40 条记录，以下是21-30 订阅

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Improving the Performance of Cardiac Simulations in a Multi-GPU Architecture Using a Coalesced Data and Kernel Scheme 16th

Improving the Performance of Cardiac Simulations in a Multi-...

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

作者： Cordeiro, Raphael Pereira Oliveira, Rafael Sachetto dos Santos, Rodrigo Weber Lobosco, Marcelo Univ Fed Juiz de Fora Programa Modelagem Computac Juiz de Fora Brazil Univ Fed Sao Joao del Rei Dept Ciencia Computacao Sao Joao del Rei Brazil

ISBN: (纸本)9783319495835;9783319495828

In this paper we evaluate a new coalesced data and kernel scheme used to reduce the execution costs of cardiac simulations that run on multi-GPU environments. the new scheme was tested for an important part of the simulator, the solution of the systems of Ordinary Differential Equations (ODEs). the results have shown that the proposed scheme is very effective. the execution time to solve the systems of ODEs on the multi-GPU environment was reduced by half, when compared to a scheme that does not implemented the proposed data and kernel coalescing. As a result, the total execution time of cardiac simulations was 25% faster.

关键词： High performance computing parallel computing Multi-GPU Cardiac electrophysiology Computational modeling

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the Impact of Panel Factorization on the Gauss-Huard Algorithm for the Solution of Linear Systems on Modern architectures 16th

The Impact of Panel Factorization on the Gauss-Huard Algorit...

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

作者： Catalan, Sandra Ezzatti, Pablo Quintana-Orti, Enrique S. Remon, Alfredo Univ Jaime I Dep Ingn & Ciencia Compuatc Castellon de La Plana 12701 Spain Univ Republica Inst Computat Montevideo 11300 Uruguay Max Planck Inst Dynam Complex Tech Syst D-30106 Magdeburg Germany

ISBN: (纸本)9783319495835;9783319495828

the Gauss-Huard algorithm (the GHA) is a specialized version of Gauss-Jordan elimination for the solution of linear systems that, enhanced with column pivoting, exhibits numerical stability and computational cost close to those of the conventional solver based on the LU factorization with row pivoting. Furthermore, the GHA can be formulated as a procedure rich in matrix multiplications, so that high performance can be expected on current architectures with multi-layered memories. Unfortunately, in principle the GHA does not admit the introduction of look-ahead, a technique that has been demonstrated to be rather useful to improve the performance of the LU factorization on multi-threaded platforms with high levels of hardware concurrency. In this paper we analyze the effect of this drawback on the implementation of the GHA on systems accelerated with graphics processing units (GPUs), exposing the roles of the CPU-to-GPU and single precision-to-double precision performance ratios, as well as the contribution from the operations in the algorithm's critical path.

关键词： Linear systems of equations Gauss-Huard algorithm LU factorization Multicore processors Graphics processing units (GPUs) Mixed precision High performance

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the Co-design of Astrophysical Code for Massively parallel Supercomputers 16th

The Co-design of Astrophysical Code for Massively Parallel S...

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

作者： Glinsky, Boris Kulikov, Igor Chernykh, Igor Weins, Dmitry Snytnikov, Alexey Nenashev, Vladislav Andreev, Andrey Egunov, Vitaly Kharkov, Egor Inst Computat Math & Math Geophys SB RAS Lavrentjeva Ave 6 Novosibirsk 630090 Russia Novosibirsk State Tech Univ Novosibirsk 630073 Russia Volgograd State Tech Univ Volgograd 400005 Russia

ISBN: (纸本)9783319499567;9783319499550

the rapid growth of supercomputer technologies became a driver for the development of natural sciences. Most of the discoveries in astronomy, in physics of elementary particles, in the design of new materials in the DNA research are connected with numerical simulation and with supercomputers. Supercomputer simulation became an important tool for the processing of the great volume of the observation and experimental data accumulated by the mankind. Modern scientific challenges put the actuality of the works in computer systems and in the scientific software design to the highest level. the architecture of the future exascale systems is still being discussed. Nevertheless, it is necessary to develop the algorithms and software for such systems right now. It is necessary to develop software that is capable of using tens and hundreds of thousands of processors and of transmitting and storing of large volumes of data. In the present work the technology for the development of such algorithms and software is proposed. As an example of the use of the technology, the process of the software development is considered for some problems of astrophysics.

关键词： Exascale systems Co-design High performance computing Computational astrophysics Physics of plasmas

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A parallel Algorithm of Kirchhoff Pre-stack Depth Migration Based on GPU 1

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

作者： Wang, Yida Li, Chao Tian, Yang Yan, Haihua Zhao, Changhai Zhang, Jianlei Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (数字)9783319111940

ISBN: (纸本)9783319111940;9783319111933

Kirchhoff pre-stack depth migration (KPSDM) algorithm, as one of the most widely used migration algorithms, plays an important part in getting the real image of the earth. However, this program takes considerable time due to its high computational cost;hence the working efficiency of the oil industry is affected. the general purpose Graphic processing Unit (GPU) and the Compute Unified Device Architecture (CUDA) developed by NVIDIA have provided a new solution to this problem. In this study, we have proposed a parallel algorithm of the Kirchhoff pre-stack depth migration and an optimization strategy based on the CUDA technology. Our experiments indicate that for large data computations, the accelerated algorithm achieves a speedup of 8 similar to 15 times compared with NVIDIA GPU.

关键词： Kirchhoff pre-stack depth migration GPU CUDA parallel algorithm optimization

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Abstract parallel Array Types and Ghost Cell Update Implementation 18th

Abstract Parallel Array Types and Ghost Cell Update Implemen...

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

作者： Zhang, Shuang Wang, Bei Chen, Yifeng Peking Univ EECS Beijing 100871 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Stencil patterns are widely used in scientific simulations and image processing. To parallelize these problems, we have to divide data into chunks that are processed by different processors. One challenge with this approach is the update of ghost cells, which are the neighbor values that calculated on remote processes. this paper focus on the update communication. We provide an abstract array types to describe distribution patterns, such as ghost cells, from global and intuitive view. Based on this description, a general copyto function is provided to perform communication automatically. Furthermore, our work makes it possible to design a distribution-independent algorithm. this results in better productivity on tuning performance.

关键词： MPI Stencil Ghost cell

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Scaling DBSCAN-like algorithms for Event Detection Systems in Twitter 16th

Scaling DBSCAN-like Algorithms for Event Detection Systems i...

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

作者： Capdevila, Joan Pericacho, Gonzalo Torres, Jordi Cerquides, Jesus Polytech Univ Catalonia UPC Dept Comp Architec Barcelona Spain Barcelona Supercomp Ctr BSC CNS Dept Comp Sci Barcelona Spain Artificial Intelligence Res Inst IIIA CSIC Barcelona Spain

ISBN: (纸本)9783319495835;9783319495828

the increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. the proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. the results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.

关键词： Event detection parallel algorithm Data clustering DBSCAN MapReduce Apache Spark Twitter

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A parallel Approach to Advantage Actor Critic in Deep Reinforcement Learning 19th

A Parallel Approach to Advantage Actor Critic in Deep Reinfo...

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

作者： Zhu, Xing Du, Yunfei Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Deep Reinforcement learning (DRL) algorithms recently still take a long time to train models in many applications. parallelization has the potential to improve the efficiency of DRL algorithms. In this paper, we propose an parallel approach (ParaA2C) for the popular Actor-Critic (AC) algorithms in DRL, to accelerate the training process. Our work considers the parallelization of the basic advantage actor critic (Serial-A2C) in AC algorithms. Specifically, we use multiple actor-learners to mitigate the strong correlation of data and the instability of updating, and finally reduce the training time. Note that we assign each actor-learner MPI process to a CPU core, in order to prevent resource contention between MPI processes, and make our ParaA2C approach more scalable. We demonstrate the effectiveness of ParaA2C by performing on Arcade Learning Environment (ALE) platform. Notably, our ParaA2C approach takes less than 10 min to train in some commonly used Atari games when using 512 CPU cores.

关键词： Deep reinforcement learning Advantage actor critic parallelization MPI Scalable

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Configuring Concurrent Computation of Phylogenetic Partial Likelihoods: Accelerating Analyses Using the BEAGLE Library 17th

Configuring Concurrent Computation of Phylogenetic Partial L...

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

作者： Ayres, Daniel L. Cummings, Michael P. Univ Maryland Ctr Bioinformat & Computat Biol College Pk MD 20742 USA

ISBN: (纸本)9783319654829;9783319654812

We describe our approach in augmenting the BEAGLE library for high-performance statistical phylogenetic inference to support concurrent computation of independent partial likelihoods arrays. Our solution involves identifying independent likelihood estimates in analyses of partitioned datasets and in proposed tree topologies, and configuring concurrent computation of these likelihoods via CUDA and opencL frameworks. We evaluate the effect of each increase in concurrency on throughput performance for our partial likelihoods kernel for a four-state nucleotide substitution model on a variety of parallel computing hardware, such as NVIDIA and AMD GPU5, and Intel multicore cPus, observing up to 16-fold speedups over our previous implementation. Finally, we evaluate the effect of these gains on an domain application program, MrBayes. For a partitioned nucleotide-model analysis we observe an average speedup for the overall run time of 2.1-fold over our previous parallel implementation, and 10-fold over the native MrBayes with SSE.

关键词： Bayes methods Biology computing Evolution (biology) Phylogeny Maximum likelihood estimation Multicore processing parallel programming High performance computing

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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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algorithms and architectures for parallel processing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Yang Xiang Wanlei Zhou Alfredo Cuzzocrea Michael Hobbs

ISBN: (数字)9783642246692

ISBN: (纸本)9783642246685

this two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th international conference on algorithms and architectures for parallel processing, ica3pp 2011, held in Melbourne, Australia, in October 2011. the second volume includes 37 papers from one symposium and three workshops held together with ica3pp 2011 main conference. these are 16 papers from the 2011 international Symposium on Advances of Distributed Computing and Networking (ADCN 2011), 10 papers of the 4th IEEE international Workshop on Internet and Distributed Computing Systems (IDCS 2011), 7 papers belonging to the III international Workshop on Multicore and Multithreaded architectures and algorithms (M2A2 2011), as well as 4 papers of the 1st IEEE international Workshop on parallel architectures for Bioinformatics Systems (HardBio 2011).

关键词： Algorithm Analysis and Problem Complexity Artificial Intelligence Software Engineering Information Systems Applications (incl. Internet) Computer Communication Networks Management of Computing and Information Systems

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