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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241-250 订阅

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A Data Cleaning Algorithm Based on Two-Layer Index for Satellite Big Data

A Data Cleaning Algorithm Based on Two-Layer Index for Satel...

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IEEE international conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Li Bing Yang Xiaoyan Shi Tao Ma Fuli Yang Guangjian Yu Qinsi Meng Lingtong Key Laboratory of Cyberspace Security Defense Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China National Space Science Center Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

With the increasing information capacity and high data transmission rate from satellite to ground, the volume of satellite data has grown rapidly. the high proportion of duplicate part in re-transmission data and inter-orbit data seriously reduce the data processing effectiveness. In this paper, A data cleaning algorithm to eliminate duplicate data based on two-layer index from the transmission frame and source packet is proposed to achieve this goal. Typical experiments show that the processing time of the algorithm is closely related to the total amount of data and the proportion of duplicate data. Compared with traditional algorithm, the algorithm has significant advantage in terms of processing timeliness.

关键词： Cloud computing Satellites Big Data parallel processing Data processing Market research Cleaning

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A Configurable Detector Based on Layered Belief Propagation for Massive MIMO Detection

A Configurable Detector Based on Layered Belief Propagation ...

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international conference on Integrated Circuits and Microsystems (ICICM)

作者： Rui Gao Houren Ji Wenyue Zhou Xiaohu You Chuan Zhang Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS) National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Massive multiple-input multiple-output (MIMO) detection plays a prominent role in the field of wireless communication. And with this trend, message passing detection (MPD) attracts a great attention since its advantages of high throughput and low complexity between distinct detection algorithms. Among various MPD algorithms, Gaussian approximate interference belief propagation (GAI-BP) detection can exhibit excellent convergence and performance in the different scenarios. However, its hardware design should be more efficient and flexible further. this paper proposes a novel semi-parallel hardware architecture for GAI-BP detection, aimed at improving its configurability using a layer scheduling scheme. through this scheme, the proposed detector achieves a gain of 0.65 dB when compared to the original algorithm, while simultaneously accommodating multi-antenna radios through the proposed hardware design.

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Research on sound source location technology of microphone array

Research on sound source location technology of microphone a...

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IEEE international conference on Signal and Image processing (ICSIP)

作者： Guangyan Wang Huarui Cai Junnian Li Tianle Zang Mengyin Dong Yuguo Wang dept. College of Information Engineering Tianjin University of Commerce Tianjin China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In this paper, the research progress based on high-resolution spectral estimation in microphone array sound source localization is discussed. the application of Multiple Signal Classification (MUSIC) algorithm in sound source location is studied, and the effect of the algorithm in one and two dimensions is compared. through the simulation experiments of one-dimension and two-dimensional MUSIC algorithm, the paper verifies the advantages of two-dimensional MUSIC algorithm in positioning accuracy compared with one-dimensional algorithm. In addition, the processing idea and flow of MUSIC algorithm are introduced in detail, including signal model, signal and noise subspace, sensor covariance matrix estimation, and spatial translation processing of related sources. the results show that MUSIC algorithm has high accuracy and reliability in sound source location, which provides an important reference for further application and research of sound source location technology.

关键词： Accuracy Music Estimation Pattern classification Position measurement parallel processing Microphone arrays

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A Joint Distributed Computation Offloading and Resource Allocation Scheme for MEC Networks

A Joint Distributed Computation Offloading and Resource Allo...

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international conference on Computer and Communications (ICCC)

作者： Enchang Sun Mengsi Li Xiaoxuan Dong Dongying Zhang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Dublin International College Beijing University of Technology Beijing China Information Technology Support Center Beijing University of Technology Beijing China

Multi-access edge computing (MEC) technology is a potential solution to the conflict between resource-constrained unmanned aerial vehicles (UAVs) and various emerging computationally intensive, delay-sensitive applications of UAVs. In this paper, we study the joint optimization problem of distributed computation offloading and resource allocation in MEC-supported UAV application scenarios, where the task of each UAV can be divided and offloaded to multiple servers deployed in ground wireless access points (WAPs) for parallel processing. To enhance the quality of user experience (QoE) while avoiding excessive optimization, we propose a new user satisfaction function to evaluate the task processing level of MEC network, which respectively scores the actual task processing delay and energy consumption based on different criteria determined by task requirements. We aim to trade off delay and energy consumption by jointly optimizing server selection, computing resource allocation, transmission power control, and task allocation to achieve maximum user satisfaction. the joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. To reduce the complexity of the joint optimization problem, it is decomposed into three sub-problems based on the divide-and-conquer approach: server computing resource allocation, transmission power control, and task offloading decision. We propose a three-layer intelligent optimization (TIO) algorithm, which employs the Lagrange multiplier method, the AdaDelta gradient descent method, and our proposed multivariate decoupling-particle swarm optimization (MD-PSO) algorithm to solve the above three sub-problems alternately for gradually approaching the maximum user satisfaction. the numerical results show that our proposed algorithm is significantly superior to the benchmark algorithms in terms of solving speed, accuracy, and stability.

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A neuro-genetic approach for inferring gene regulatory networks from gene expression data 22

A neuro-genetic approach for inferring gene regulatory netwo...

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Proceedings of the 9th international conference on Bioinformatics Research and Applications

作者： Guo Mao Zhengbin Pang Jie Liu Ke Zuo Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China

ISBN: (纸本)9781450396868

Accurate prediction of gene regulation rules is important for understanding complex life processes. Existing computational algorithms designed for bulk transcriptome datasets typically require a large number of time points to infer gene regulatory networks (GRNs), are suitable for a small number of genes, and cannot efficiently detect potential regulatory relationships. We propose an approach based on a deep learning framework to reconstruct GRNs from bulk transcriptome datasets, assuming that the expression levels of transcription factors involved in gene regulation are strong predictors of the expression of their target genes. the algorithm uses multilayer perceptrons to infer the regulatory relationship between multiple transcription factors and a gene, and uses genetic algorithms to search for the best regulatory gene combination. the results show that our approach is more accurate than other methods for reconstructing gene regulatory networks on real-world and simulated bulk transcriptome gene expression datasets.

关键词： gene regulatory networks datasets multilayer perceptrons genetic algorithms

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A Heterogeneous KBA parallel Algorithm for the Cartesian Discrete Ordinates for Multizone Heterogeneous System

A Heterogeneous KBA Parallel Algorithm for the Cartesian Dis...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Runhua Li Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Innovations in powerful high-performance computing (HPC) architecture are enabling high-fidelity whole-core neutron transport simulations at reasonable time. Especially, the currently fashionable heterogeneous architectures make the cost of such simulations at very low level. Neutron distribution of a reactor core is governed by the Boltzmann neutron transport equation (BTE), first viable solutions of which need tremendous computer resources. Among of the high-fidelity numerical methods, the discrete ordinates method (SN) is becoming popular in the reaction design community by taking a good balance between computational cost and accuracy. Recently, MT-3000, which is a multizone heterogeneous architecture with a peak double precision performance of 11.6 TFLOPS, is proposed. In this work, the BTE is solved by the SN with heterogenous Koch-Baker-Alcouffe (KBA) parallel algorithms based on the MT-3000 architecture. A communication mechanism has been established to efficiently transmit data among the acceleration cores and the CPU cores. the kernel computation procedure is largely accelerated by the vectorization and instruction pipelining techniques. Numerical experiments show that our formulation could achieve 1.37 TFLOPs with single MT-3000, that is 11.8% of its peak performance.

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Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies

Development and Application of a Monte Carlo Tree Search Alg...

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Intelligent Computing and Human-Computer Interaction (ICHCI), international conference on

作者： Ye Zhang Mengran Zhu Kailin Gui Jiayue Yu Yong Hao Haozhan Sun University of Pittsburgh Pittsburgh PA USA Miami University Oxford OH USA University of Washington Seattle WA USA Warner Bro. Discovery Culver City CA USA Columbia University New York NY USA Duke University Durham NC USA

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, contingent upon the volume of simulations conducted. Notwithstanding its broad applicability, the algorithm's performance can be adversely impacted in certain scenarios, particularly within the domain of game strategy development. this research posits that the inherent branch divergence within the Da Vinci Code board game significantly impedes parallelism when executed on Graphics processing Units (GPUs). To investigate this hypothesis, we implemented and meticulously evaluated two variants of the MCTS algorithm, specifically designed to assess the impact of branch divergence on computational performance. Our comparative analysis reveals a linear improvement in performance with the CPU-based implementation, in stark contrast to the GPU implementation, which exhibits a non-linear enhancement pattern and discernible performance troughs. these findings contribute to a deeper understanding of the MCTS algorithm's behavior in divergent branch scenarios, highlighting critical considerations for optimizing game strategy algorithms on parallel computing architectures.

关键词： Solid modeling Monte Carlo methods Codes Computational modeling Instruction sets Graphics processing units Games parallel processing Time measurement Robots

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IOT: INSTANCE-WISE LAYER REORDERING FOR TRANSFORMER STRUCTURES 9

IOT: INSTANCE-WISE LAYER REORDERING FOR TRANSFORMER STRUCTUR...

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9th international conference on Learning Representations, ICLR 2021

作者： Zhu, Jinhua Wu, Lijun Xia, Yingce Xie, Shufang Qin, Tao Zhou, Wengang Li, Houqiang Liu, Tie-Yan University of Science and Technology of China China Microsoft Research

With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed. Currently, almost all these models assume that the layer order is fixed and kept the same across data samples. We observe that different data samples actually favor different orders of the layers. Based on this observation, in this work, we break the assumption of the fixed layer order in Transformer and introduce instance-wise layer reordering into model structure. Our Instance-wise Ordered Transformer (IOT) can model variant functions by reordered layers, which enables each sample to select the better one to improve the model performance under the constraint of almost same number of parameters. To achieve this, we introduce a light predictor with negligible parameter and inference cost to decide the most capable and favorable layer order for any input sequence. Experiments on 3 tasks (neural machine translation, abstractive summarization, and code generation) and 9 datasets demonstrate consistent improvements of our method. We further show that our method can also be applied to other architectures beyond Transformer. Our code is released at Github. © 2021 ICLR 2021 - 9th international conference on Learning Representations. All rights reserved.

关键词： Natural language processing systems

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SVM parallel Concept Test with SMO Decomposition on Cancer Microarray Dataset 9

SVM Parallel Concept Test with SMO Decomposition on Cancer M...

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9th international conference on Information and Communication Technology, ICoICT 2021

作者： Prasojoe, Rahmat Ramadan Setyorini, Setyorini Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781665404471

Support Vector Machine (SVM) is a reliable method for performing classification and regression especially in supervised machine learning. However, SVM has scalability issues in compute time and memory usage. therefore, there are many proposals for parallel Support Vector Machine (PSVM) for mining large-scale data. In this study, we conducted a PSVM concept test with SMO decomposition that could be handled and classified cancer using microarray data. We apply the Sequential Minimal Optimization (SMO) technique which uses LaGrange multipliers to solve quadratic programming (QP) problems that arise during training. To test the concept of SMO decomposition, the data set will be broken down into several subsets and then independently conduct SMO training for each subset and combine each training result into one SMO classification model. Evaluation is done by comparing the accuracy and performance of SMO decomposition and non-decomposition SMO. Evaluation result are accuracy of SMO decomposition 75% and non-decomposition SMO 63%, and as well as SMO decomposition training time 5.7 times faster than to non-decomposition SMO. © 2021 IEEE.

关键词： Support vector machines

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Efficient Route Selection for Drone-based Delivery Under Time-varying Dynamics 18

Efficient Route Selection for Drone-based Delivery Under Tim...

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18th IEEE international conference on Mobile Ad hoc and Smart Systems (IEEE MASS)

作者： Khanda, Arindam Coro, Federico Sorbelli, Francesco Betti Pinotti, Cristina M. Das, Sajal K. Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Univ Perugia Dept Comp Sci & Math Perugia Italy

ISBN: (纸本)9781665449359

the use of drones can be a valuable solution for the problem of delivering goods for many reasons. In fact, they can be efficiently employed in time-critical situations when there is a traffic jam on the roads, to serve customers in hard-toreach places, or simply to expand the business. However, due to limited battery capacities and the fact that drones can serve a single customer at a time, a drone-based delivery system (DBDS) aims to minimize the drones' energy usage for completing a route from the depot to the customer and go back to the depot for new deliveries. In general, the shortest delivery route could not be the optimal choice since external factors like the wind (which varies with time) can affect energy consumption. Previous work has mainly considered simplified DBDSs assuming architectures with a single drone and with static costs on paths. Moreover, in these non-centralized architectures, the drones themselves compute the routes on the fly employing their onboard processing resources, making this choice costly. In this paper we develop a centralized system for computing energy-efficient time-varying routes for drones in a multi-depot multi-drone delivery system. Specifically, we propose a novel centralized parallel algorithm called parallel Shortest Route Update (PSRU) that, over time, updates the drones' delivery routes avoiding the whole recomputation from scratch. A comprehensive evaluation proves that PSRU is up to 4.5x faster than the state-of-the-art algorithms.

关键词： Drone Dynamic graph parallel algorithm GPU

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