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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing"

共 2826 条记录，以下是461-470 订阅

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Algorithmic Analysis of Priority-Based Bin Packing 7th

Algorithmic Analysis of Priority-Based Bin Packing

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7th international conference on algorithms and Discrete Applied Mathematics, CALDAM 2021

作者： Wojciechowski, Piotr Subramani, K. Velasquez, Alvaro Caskurlu, Bugra LDCSEE West Virginia University MorgantownWV United States Information Directorate AFRL RomeNY United States TOBB University of Economics and Technology Ankara Turkey

ISBN: (纸本)9783030678982

this paper is concerned with a new variant of Traditional Bin Packing (TBP) called Priority-Based Bin Packing with Subset Constraints (PBBP-SC). In a TBP instance, we are given a collection of items { a1, a2, … an}, with ai∈ (0, 1 ) and a collection of unit-size bins { B1, B2, …, Bm}. One problem associated with TBP is the bin minimization problem. the goal of this problem is to pack the items in as few bins as possible. In a PBBP-SC instance, we are given a collection of unit-size items and a collection of bins of varying capacities. Associated with each item is a positive integer which is called its priority. the priority of an item indicates its importance in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. Checking if there is a feasible assignment to a given instance is one problem. Finding a maximum priority assignment in case of the instance being infeasible is another. Finding an assignment with the fewest number of bins to pack a feasible instance is a third. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. © 2021, Springer Nature Switzerland AG.

关键词： parallel processing systems

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parallel Computation for Sparse Network Component Analysis 4th

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4th international conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Elsayad, Dina Hamad, Safwat Shedeed, Howida A. Tolba, M.F. Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783030141172

the Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network goal is determining the topological order of genes interactions. Moreover, the regulatory network is a vital for understanding genes influence on each other. However, the main challenge confronting gene regulatory network algorithms is the massive data size. Where, the algorithm runtime is relative to the data size. this paper presents a parallel computation for Sparse Network Component Analysis (PSparseNCA) with application on gene regulatory network. PSparseNCA is a parallel version of SparseNCA. PSparseNCA enhanced the computation of SparseNCA using a distributed computing model. Where, the workload is distributed among P processing nodes, PSparseNCA is more efficient than SparseNCA. It achieved a better performance and its speedup reached 12.33. In addition, PsparseNCA complexity is O(NM2/P) instead of O(NM2) for SparseNCA. © 2020, Springer Nature Switzerland AG.

关键词： Gene expression

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parallel Graph Clustering Based on Minhash 10th

Parallel Graph Clustering Based on Minhash

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10th international conference on Computer Science and its Applications, CSA 2018 and the 13th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2018

作者： Kim, Byoungwook Chung, Jaehwa Gil, Joon-Min Shon, JinGon Department of Computer Engineering Dongguk University Gyeongju Korea Republic of Department of Computer Science Korea National Open University Seoul Korea Republic of School of Information Technology Engineering Catholic University of Daegu Daegu Korea Republic of

ISBN: (纸本)9789811393402

Graph clustering is a technique for grouping vertices having similar characteristics into the same cluster. It is widely used to analyze graph data and identify its characteristics. Recently, a large-capacity large-scale graph data is being generated in a variety of applications such as a social network service, a world wide web, and a telephone network. therefore, the importance of clustering technique for efficiently processing large capacity graph data is increasing. In this paper, we propose a clustering algorithm that efficiently generates clusters of large capacity graph data. Our proposed method efficiently estimates the similarity between clusters in the graph using Min-Hash and generates clusters according to the calculated similarity. In the experiment using real world data, we show the efficiency of the proposed method compared with the proposed method and existing graph clustering methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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A Synchronization-Avoiding Distance-1 Grundy Coloring Algorithm for Power-Law Graphs 28

A Synchronization-Avoiding Distance-1 Grundy Coloring Algori...

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28th international conference on parallel architectures and Compilation Techniques (PACT)

作者： Firoz, Jesun Sahariar Zalewski, Marcin Lumsdaine, Andrew Pacific Northwest Natl Lab Seattle WA 98109 USA

ISBN: (纸本)9781728136134

In this paper, we propose a distributed, unordered, label-correcting distance-1 Grundy (vertex) coloring algorithm, namely, Distributed Control (DC) coloring algorithm. Our algorithm eliminates the need for vertex-centric barriers and global synchronization for color refinement, relying only on atomic operations and local termination detection to update vertex color. DC proceeds optimistically, correcting the colors asynchronously as the algorithm progresses and depends on local ordering of tasks to minimize the execution of sub-optimal work. We implement our DC coloring algorithm and the well-known Jones-Plassmann algorithm and compare their performance with 4 different types of standard RMAT graphs and real-world graphs. We show that the elimination of waiting time of global and vertex-centric barriers and investing this time for local ordering leads to improved scaling for graphs with prominent power-law characteristics and densely interconnected local subgraphs.

关键词： Color Decentralized control Message systems Synchronization Heuristic algorithms Task analysis parallel processing

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High throughput Hardware for Hoeffding Tree Algorithm with Adaptive Naive Bayes Predictor

High Throughput Hardware for Hoeffding Tree Algorithm with A...

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international conference for Convergence of Technology (I2CT)

作者： Ashin Antony Devi A Kuruvilla Varghese Indian Institute Of Science Bangalore India

Hoeffding tree algorithm is a popular online decision tree algorithm capable of learning from huge data streams. the algorithm involves complex time consuming computations in the leaves of the tree for each data instance. these computations involve a lot of parallelisms which can be exploited and implemented in a field programmable gate array to achieve speedup. this paper presents a hardware accelerator for Hoeffding tree algorithm with adaptive naive bayes predictor in the leaves. the proposed system is capable of accelerating data streams with both nominal and numeric attributes using minimum hardware resources for huge datasets. It is implemented on a Xilinx VC707 board based on Virtex-7 XC7VX485T field programmable gate array. the implemented system is about 9x faster than StreamDm(C++), a well known reference software implementation for the standard forest cover type dataset.

关键词： Vegetation parallel processing Logic gates Prediction algorithms throughput Hardware Table lookup

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parallelization of Data Buffering and processing Mechanism in Mesh Wireless Sensor Network for IoT Applications 6th

Parallelization of Data Buffering and Processing Mechanism i...

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6th international conference on Advanced Computing, Networking, and Informatics, ICACNI 2018

作者： Jain, Monika Saxena, Rahul Jaidka, Siddharth Jhamb, Mayank Kumar School of Computing and Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811396793

IoT, being a field of great interest and importance for the coming generations, involves certain challenging and improving aspects for the IoT application developers and researchers to work upon. A wireless sensor mesh networking has emerged as an attractive option for wide range of low-power IoT applications. this paper shows that how the data can be stored, read and processed parallelly by the parent node in the cluster from multiple sensor nodes, thus reducing the response time drastically. the use of parallelized algorithm for the communication protocol optimized using OpenMP standards for multi-core architecture between the sensors and parent node enables multiple radio technologies to be used for an application which could not be more than one in case of serial processing. the proposed algorithm has been tested for a wireless network application measuring temperature and moisture concentrations using numerous sensors for which the response time is recorded to be less than 10 ms. the paper also discusses in detail the hardware configurations for the application tested along with the results throwing light on the parallel mechanism for buffering and processing the messages. Finally, the paper is concluded by claiming the edge of parallel algorithm-based routing protocol over the serial in the light of graphical results and analysis. © 2020, Springer Nature Singapore Pte Ltd.

关键词： parallel algorithms

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Stream parallelism Annotations for Multi-Core Frameworks 20

Stream Parallelism Annotations for Multi-Core Frameworks

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24th Brazilian Symposium on Programming Languages, SBLP 2020, co-located with the Brazilian conference on Software: theory and Practice, CBSoft 2020

作者： Hoffmann, Renato B. Griebler, Dalvan Danelutto, Marco Fernandes, Luiz G. School of Technology Pucrs Porto Alegre Brazil University of Pisa Pisa Italy

ISBN: (纸本)9781450389433

Data generation, collection, and processing is an important workload of modern computer architectures. Stream or high-intensity data flow applications are commonly employed in extracting and interpreting the information contained in this data. Due to the computational complexity of these applications, high-performance ought to be achieved using parallel computing. Indeed, the efficient exploitation of available parallel resources from the architecture remains a challenging task for the programmers. Techniques and methodologies are required to help shift the efforts from the complexity of parallelism exploitation to specific algorithmic solutions. To tackle this problem, we propose a methodology that provides the developer with a suitable abstraction layer between a clean and effective parallel programming interface targeting different multi-core parallel programming frameworks. We used standard C++ code annotations that may be inserted in the source code by the programmer. then, a compiler parses C++ code with the annotations and generates calls to the desired parallel runtime API. Our experiments demonstrate the feasibility of our methodology and the performance of the abstraction layer, where the difference is negligible in four applications with respect to the state-of-the-art C++ parallel programming frameworks. Additionally, our methodology allows improving the application performance since the developers can choose the runtime that best performs in their system. © 2020 ACM.

关键词： parallel programming

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17th international conference on algorithms and architectures for parallel processing, ICA3PP 2017

17th International Conference on Algorithms and Architecture...

引用

17th international conference on algorithms and architectures for parallel processing, ICA3PP 2017

ISBN: (纸本)9783319654812

the proceedings contain 66papers. the special focus in this conference is on algorithms and architectures for parallel processing. the topics include: Pipelining computation and optimization strategies for scaling GROMACS on the sunway many-core processor;exploring FPGA-GPU heterogeneous architecture for ADAS;new huge page allocator with main memory compression;an fpga-based real-time moving object tracking approach;automatic acceleration of stencil codes in android devices;optimizing concurrent evacuation transfers for geo-distributed datacenters in SDN;energy-balanced and depth-controlled routing protocol for underwater wireless sensor networks;on the energy efficiency of sleeping and rate adaptation for network devices;private and efficient set intersection protocol for big data analytics;a topology-aware framework for graph traversals;adaptive traffic signal control with network-wide coordination;a novel parallel dual-character string matching algorithm on graphical processing units;distributed nonnegative matrix factorization with HALS algorithm on mapreduce;GPU-accelerated block-max query processing;KD-tree and healpix-based distributed cone search indexing system for multi-band astronomical catalogs;an out-of-core branch and bound method for solving the 0-1 knapsack problem on a GPU;the curve boundary design and performance analysis for DGM based on openFOAM;leakage-resilient password-based authenticated key exchange;secure encrypted data deduplication with ownership proof and user revocation;optimally selecting the timing of zero-day attack via spatial evolutionary game and performance analysis of a ternary optical computer based on M/M/1 queueing system.

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Adversarial BiLSTM-CRF architectures for Extra-Propositional Scope Resolution 9th

Adversarial BiLSTM-CRF Architectures for Extra-Propositional...

引用

9th CCF international conference on Natural Language processing and Chinese Computing, NLPCC 2020

作者： Huang, Rongtao Ye, Jing Zou, Bowei Hong, Yu Zhou, Guodong Natural Language Processing Lab Soochow University Suzhou China Institute for Infocomm Research Singapore Singapore

ISBN: (纸本)9783030604561

Due to the ability of expressively representing narrative structures, proposition-aware learning models in text have been drawing more and more attentions in information extraction. Following this trend, recent studies go deeper into learning fine-grained extra-propositional structures, such as negation and speculation. However, most of elaborately-designed experiments reveal that existing extra-proposition models either fail to learn from the context or neglect to address cross-domain adaptation. In this paper, we attempt to systematically address the above challenges via an adversarial BiLSTM-CRF model, to jointly model the potential extra-propositions and their contexts. this is motivated by the superiority of sequential architecture in effectively encoding order information and long-range context dependency. On the basis, we come up with an adversarial neural architecture to learn the invariant and discriminative latent features across domains. Experimental results on the standard BioScope corpus show the superiority of the proposed neural architecture, which significantly outperforms the state-of-the-art on scope resolution in both in-domain and cross-domain scenarios. © 2020, Springer Nature Switzerland AG.

关键词： Architecture

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A 2D parallel Triangle Counting Algorithm for Distributed-Memory architectures 19

A 2D Parallel Triangle Counting Algorithm for Distributed-Me...

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48th international conference on parallel processing (ICPP)

作者： Tom, Ancy Sarah Karypis, George Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781450362955

Triangle counting is a fundamental graph analytic operation that is used extensively in network science and graph mining. As the size of the graphs that needs to be analyzed continues to grow, there is a requirement in developing scalable algorithms for distributed-memory parallel systems. To this end, we present a distributedmemory triangle counting algorithm, which uses a 2D cyclic decomposition to balance the computations and reduce the communication overheads. the algorithm structures its communication and computational steps such that it reduces its memory overhead and includes key optimizations that leverage the sparsity of the graph and the way the computations are structured. Experiments on synthetic and real-world graphs show that our algorithm obtains an average relative speedup range between 3.24 to 7.22 out of 10.56 across the datasets using 169 MPI ranks over the performance achieved by 16 MPI ranks. Moreover, we obtain an average speedup of 10.2 times on comparison with previously developed distributed-memory parallel algorithms.

关键词： triangle counting graph analytics distributed-memory

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