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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251-260 订阅

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parallel Exploitation of 2D LiDAR Simultaneous Localization and Mapping 5

Parallel Exploitation of 2D LiDAR Simultaneous Localization ...

引用

5th international conference on Computer, Communication, and Signal processing, ICCCSP 2021

作者： Shreyas Madhav, A V Kanna, B Rajesh Pavithra, L K Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665432771

Simultaneous Localization and Mapping plays an integral role in the field of autonomous robotics for ensuring proper navigation and exploration in unknown environments. Applications of SLAM range from surveillance and monitoring to self driving cars or UAVs. the high computational complexity involved in simultaneously tracking the trajectory of the robot while constantly updating the surrounding map based on every scan observation is disadvantageous to the mainstream adoption of SLAM. With the advent of parallel computing and multicore architectures, serial programs and algorithms can be accelerated in terms of execution time and efficiency. this paper explores the effects of CPU thread parallelism on 2D LiDAR SLAM in an indoor environment. the recorded scans are matched and loop closure detection is performed. the final trajectory and mapping is finetuned with the help of pose graph optimization. the number of threads and CPU cores are varied to draw analysis of their effects on the serial algorithm. the simulated results show a performance increase and a decrease of 19.1% in execution time for the parallelized algorithm utilizing 4 CPUs, compared to its serial version. © 2021 IEEE.

关键词： Program processors

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Translation of Array-Based Graph Programs to Spark SQL on Block Arrays

Translation of Array-Based Graph Programs to Spark SQL on Bl...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Noor, Md Hasanuzzaman Fegaras, Leonidas Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781665439022

Many graph algorithms can be expressed as repetitive computations that resemble matrix multiplication in which the addition and multiplication operations have been replaced with generalized operations that form an algebraic structure known as a semiring. Similar to matrix multiplication, these graph algorithms can be implemented in a distributed system using block arrays, which are distributed collections of non-overlapping dense arrays. In a distributed system, computations on block matrices are significantly faster than the same computations in the coordinate format in terms of computation and communication cost. Given the rise of Big Data and data-driven decision-making, our goal is to rewrite sequential programs on large graphs that are initially written to run on a single computer using loops and standard array operations to data-parallel programs on array blocks that can run on a distributed system. Our framework, called OSQLgen, automatically parallelizes graph programs with loops and arrays to distributed data-parallel programs. OSQLgen recognizes programs that are equivalent to a semiring structure and translates them to fast Spark SQL programs on block arrays that can run on a computer cluster in a distributed mode. We compare the performance of OSQLgen with GraphX, GraphFrames, and hand-written Spark SQL programs on coordinate and block arrays on various graph problems. On certain graph problems, OSQLgen is up to 36x faster than GraphX, 25x faster than GraphFrames, and up to 99x faster than hand-written Spark SQL programs on coordinate arrays, giving performance close to that of hand-written Spark SQL programs on block arrays.

关键词： Big Data arrays SQL Spark Graph processing

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ANALYSIS of the EFFECTIVENESS of VARIOUS MEthODS for parallelIZING DATA processing IMPLEMENTED in the ROOT PACKAGE 9

ANALYSIS of the EFFECTIVENESS of VARIOUS METHODS for PARALLE...

引用

9th international conference "Distributed Computing and Grid Technologies in Science and Education", GRID 2021

作者： Solovjeva, Tatiana Joint Institute for Nuclear Research 6 Joliot-Curie St. Moscow Region Dubna 141980 Russia

the ROOT software package is currently being upgraded in several ways to improve data processing performance. this paper will consider several tools implemented in this framework for calculations on modern heterogeneous computing architectures. PROOF (parallel ROOT Package Extension) divides common work into small chunks, i.e. packets. the size of the first packet used for calibration, the minimum and maximum set size of the packet, the degree of data structuring affect the speed of their processing. When processing large amounts of data, the read and write speed can be crucial. the new asynchronous file merge feature in the TBufferMerger class allows writing data in parallel from multiple streams to a single output file. Our calculations show a good scalability of the macro execution time depending on the number of processor cores used. © 2021 CEUR-WS. All rights reserved.

关键词： Data handling

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Scalable algorithms Using Sparse Storage for parallel Spectral Clustering on GPU 18th

Scalable Algorithms Using Sparse Storage for Parallel Spect...

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18th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2021

作者： He, Guanlin Vialle, Stephane Sylvestre, Nicolas Baboulin, Marc Université Paris-Saclay CNRS LISN Orsay91405 France CentraleSupélec Gif-sur-Yvette91192 France Université Paris-Saclay CNRS CEA Maison de la Simulation Gif-sur-Yvette91191 France

ISBN: (纸本)9783030935702

Spectral clustering has many fundamental advantages over k-means, but has high computational complexity (O(n3) ) and memory requirement (O(n2) ), making it prohibitively expensive for large datasets. In this paper we present our solution on GPU to address the scalability challenge of spectral clustering. First, we propose optimized algorithms for constructing similarity matrix directly in CSR sparse format on the GPU. Next, we leverage the spectral graph partitioning API of the GPU-accelerated nvGRAPH library for remaining computations especially for eigenvector extraction. Finally, experiments on synthetic and real-world large datasets demonstrate the high performance and scalability of our GPU implementation for spectral clustering. © 2022, IFIP international Federation for Information processing.

关键词： Scalability

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Small-Step Real-Time Simulation for VSC Based on FPGA

Small-Step Real-Time Simulation for VSC Based on FPGA

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IEEE international conference on Power and Power and Renewable Energy (ICPRE)

作者： Yanqi Hou Kuan Li Deling Fan Kongming Sun Qiang Huang Yudun Li Yuntao Sun Guohui Zhang State Grid Shandong Electricm Shandong Smart Grid Technology Innovation Center Power Research Institute Ji'nan China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

the voltage source converter (VSC) has been widely used in the new power system. Its electromagnetic transient simulation has the characteristics of low simulation speed and high resource consumption. In this paper, the small-step real-time simulator for VSC based on field programmable gate array (FPGA) is established. Firstly, the fixed-admittance model for VSC is established to improve the calculation speed of the primary system. At the same time, the parallel computing process of the primary system is designed to achieve small-step real-time computing of the primary system for VSC based on the parallel computing architecture of FPGA. Secondly, according to the logic of the control system for VSC, the serial-parallel computing process of the control system is designed, and the controller for VSC is implemented based on FPGA. Finally, the performance of the established FPGA simulator is verified based on the control experiments on PSCAD/EMTDC.

关键词： Computational modeling Process control parallel processing Logic gates Control systems Transformers Real-time systems Topology Field programmable gate arrays Voltage-source converters

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Multi-Objective Optimization Design of Cable Driven parallel Robot Based on Aircraft Spraying

Multi-Objective Optimization Design of Cable Driven Parallel...

引用

Automation, Control and Robotics Engineering (CACRE), international conference on

作者： Zonghui Yang Ting Liu Sen Jiao Xudong Chang College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China College of Aerospace Engineering Jilin Institute of Chemical Technology Jilin China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

this article focuses on the cable driven parallel robot for aircraft spraying. Based on a multi-objective optimization model, the performance of the cable driven parallel robot (CDPR) is optimized using Prairie Dogs optimization algorithm. Firstly, a static model of 8-cable 6-degree-of-freedom CDPR suitable for aircraft spraying was established, and corresponding evaluation indicators were designed for four performance indicators: workspace, average stiffness, stiffness fluctuation, and flexibility. Establish a multi-objective optimization model by processing performance indicators and solve it using the Prairie Dogs optimization algorithm. the final results indicate that the groundhog optimization algorithm based on multi-objective optimization design has a good effect on the performance optimization of CDPR, providing important reference for the design and optimization of aircraft spraying robots. this study has important theoretical and practical significance in the field of aircraft spraying robots, providing new ideas and methods for optimizing robot performance, and has certain guiding significance for engineering practice.

关键词： parallel robots Grasslands Fluctuations Optimization models Atmospheric modeling Spraying Dogs

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EVALUATION OF NEURAL architectures TRAINED WIth SQUARE LOSS VS CROSS-ENTROPY IN CLASSIFICATION TASKS 9

EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS ...

引用

9th international conference on Learning Representations, ICLR 2021

作者： Hui, Like Belkin, Mikhail Computer Science and Engineering University of California San Diego San DiegoCA92093 United States Halıcıoğlu Data Science Institute University of California San Diego San DiegoCA92093 United States

Modern neural architectures for classification tasks are trained using the cross-entropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be well-founded. We explore several major neural architectures and a range of standard benchmark datasets for NLP, automatic speech recognition (ASR) and computer vision tasks to show that these architectures, with the same hyper-parameter settings as reported in the literature, perform comparably or better when trained with the square loss, even after equalizing computational resources. Indeed, we observe that the square loss produces better results in the dominant majority of NLP and ASR experiments. Cross-entropy appears to have a slight edge on computer vision tasks. We argue that there is little compelling empirical or theoretical evidence indicating a clear-cut advantage to the cross-entropy loss. Indeed, in our experiments, performance on nearly all non-vision tasks can be improved, sometimes significantly, by switching to the square loss. Furthermore, training with square loss appears to be less sensitive to the randomness in initialization. We posit that training using the square loss for classification needs to be a part of best practices of modern deep learning on equal footing with cross-entropy. © 2021 ICLR 2021 - 9th international conference on Learning Representations. All rights reserved.

关键词： Natural language processing systems

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Neural Medication Extraction: A Comparison of Recent Models in Supervised and Semi-supervised Learning Settings 9

Neural Medication Extraction: A Comparison of Recent Models ...

引用

9th IEEE international conference on Healthcare Informatics (IEEE ICHI)

作者： Kocabiyikoglu, Ali Can Babouchkine, Jean-Marc Portet, Francois Qader, Raheel Calystene SA F-38320 Eybens France Univ Grenoble Alpes CNRS Grenoble INP LIG F-38000 Grenoble France Lingua Custodia Paris France

ISBN: (纸本)9781665401326

Drug prescriptions are essential information that must be encoded in electronic medical records. However, much of this information is hidden within free-text reports. this is why the medication extraction task has emerged. To date, most of the research effort has focused on small amount of data and has only recently considered deep learning methods. In this paper, we present an independent and comprehensive evaluation of state-of-the-art neural architectures on the I2B2 medical prescription extraction task both in the supervised and semi-supervised settings. the study shows the very competitive performance of simple DNN models on the task as well as the high interest of pre-trained models. Adapting the latter models on the I2B2 dataset enables to push medication extraction performances above the state-of-the-art. Finally, the study also confirms that semi-supervised techniques are promising to leverage large amounts of unlabeled data in particular in low resource setting when labeled data is too costly to acquire.

关键词： Medication information extraction Natural Language processing Natural Language Understanding

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Sentiment Analysis in Food Reviews Based on Graph Convolutional Networks

Sentiment Analysis in Food Reviews Based on Graph Convolutio...

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6th international conference on Intelligent Computing and Signal processing (ICSP)

作者： Luyao Wang Zhifeng Hu Chunlong Yao Yang Li Dalian Polytechnic University School of Information Science and Engineering Dalian Liaoning China Dalian Cloud Force Technologies Co. LTD Dalian Liaoning China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Multitask Bert GCN network model integrating BERT and GCN is proposed to address the issue of aspect level sentiment analysis in food reviews. the model concatenates the sentiment features of aspect words extracted from graph convolutional networks GCN and BERT, respectively. Establish a multi task learning model based on the concatenated emotional feature vectors, and output different levels of emotional attitudes in the output layer. the combination of GCN and multitasking learning in this model can amplify the advantages of parameter sharing, fully utilize the relationships between words, and improve the efficiency of emotional feature extraction. At the same time, the entire model uses parallel processing mode, reducing the training time of the model. through experiments, it has been shown that the Multitask Bert GCN model utilizes the advantage of parameter sharing to better establish the relationship between aspect words and attitude words. Compared with other models, the accuracy and F1 value of the model have been improved by 10%, and the training time has been shortened by 15%.

关键词： Training Sentiment analysis Analytical models Accuracy Reviews Graph convolutional networks Signal processing Feature extraction Multitasking Vectors

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3D MRI Image Segmentation using 3D UNet architectures: Technical Review

3D MRI Image Segmentation using 3D UNet Architectures: Techn...

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15th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 9th international conference on Bioimaging (BIOIMAGING)

作者： Kamble, Vijaya Daruwala, Rohin Veermata Jijabai Technol Inst VJTI Dept Elect Engn Mumbai Maharashtra India

ISBN: (纸本)9789897585524

From last few decades machine learning & deep convolutional neural networks (CNNs) used extensively and have shown remarkable performance in almost all fields including medical diagnostics. It is used in medical domain for automatic tissue, lesion detection, segmentation, anatomical or structure segmentation classification & survival predictions. In this paper we presented an extensive technical literature review on 3D CNN U-Net architectures applied for 3D brain magnetic resonance imaging (MRI) analysis. We mainly focused on the architectures, its modifications, pre-processing techniques, types datasets, data preparation, methodology, GPU, tumor disease types and per architectures evaluation measures in this works. Our primary goal for this extensive technical review is to report how different 3D U-Net architectures or CNN architectures have been used to differentiate between state-of-the-art strategies, compare their results obtained using public/clinical datasets and examine their effectiveness. this paper is intended to present detailed reference for further research activity or plan of strategy to use 3D U-Nets for brain MRI automated tumor diseases detection, segmentation & survival prediction analysis. Finally. we are presenting a novel perspective to assist research directions on the future of CNNs & 3D U-Net architectures to explore in subsequent years to help doctors & radiologist.

关键词： 3D UNet MRI Images Segmentation Brain Tumors

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