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检索条件"任意字段=8th International Conference on Parallel Processing and Applied Mathematics"

共 2796 条记录，以下是571-580 订阅

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Digital signal processing in FPGA for particle track reconstruction at the HL-LHC ATLAS 8

Digital signal processing in FPGA for particle track reconst...

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8th international conference on Modern Circuit and System Technologies (MOCAST)

作者： Poggi, Riccardo Univ Geneva Dept Phys Nucl & Corpusculaire Geneva Switzerland

ISBN: (纸本)9781728111841

In the context of the high-luminosity large hadron collider (HL-LHC) upgrade, this work presents the latest update on the design of the FPGA firmware responsible of particle track reconstruction in the pattern recognition mezzanine (PRM) of the hardware-based tracking for the trigger (HTT) system, a subsystem of the ATLAS experiment trigger and data acquisition system. this computationally demanding task relies heavily on two FPGA features: the embedded in silicon digital signal processing (DSP) components and the performance of an available high bandwidth memory (HBM). the document reports the mathematical algorithm used for track reconstruction and analyses a preliminary performance test. these considerations are then used to provide estimates on the DSP and HBM resource usage in order to prove the feasibility of the firmware design. Finally, key factors for a parallel design are identified and outlook presented.

关键词： Track reconstruction DSP HBM

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Single Bounded parallel-Batch Machine Scheduling with an Unavailability Constraint and Job Delivery 14th

Single Bounded Parallel-Batch Machine Scheduling with an Una...

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14th international conference on Algorithmic Aspects in Information and Management, AAIM 2020

作者： Fan, Jing Ng, C.T. Cheng, T.C.E. Shi, Hui Shanghai Polytechnic University Shanghai201209 China Logistics Research Centre Department of Logistics and Maritime Studies The Hong Kong Polytechnic University Hong Kong Shanghai General Hospital School of Medicine Shanghai Jiaotong University Shanghai200080 China

ISBN: (纸本)9783030576011

We consider a scheduling problem where a manufacturer processes a set of jobs for a customer and delivers the completed jobs to the customer. the job sizes and processing times are given. the objective is to minimize the maximum delivery time to the customer. In the production stage, one machine with an unavailability period is used to process the jobs. the machine has a fixed capacity and the jobs are processed in batches under the condition that the total size of the jobs in a batch cannot exceed the machine capacity. the processing time of a batch is the maximum processing time of the jobs contained in the batch. In addition, each batch is non-resumable, i.e., if the processing of a batch cannot be completed before the unavailability period, the batch needs to be processed anew after the unavailability interval. In the distribution stage, the manufacturer assigns a vehicle with a fixed capacity to deliver the completed jobs. the total size of the completed jobs in one delivery cannot exceed the vehicle capacity. We first consider the case where the jobs have the same size and arbitrary processing times, for which we provide a 3/2-approximation algorithm and show that the worst-case ratio is tight. We then consider the case where the jobs have the same processing time and arbitrary sizes, for which we provide a 5/3-approximation algorithm and show that the worst-case ratio is tight. © 2020, Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Accelerating GPU-based evolutionary induction of decision trees - fitness evaluation reuse 13th

Accelerating GPU-based evolutionary induction of decision tr...

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13th international conference on parallel processing and applied mathematics, PPAM 2019

作者： Jurczuk, Krzysztof Czajkowski, Marcin Kretowski, Marek Faculty of Computer Science Bialystok University of Technology Wiejska 45a Bialystok15-351 Poland

ISBN: (纸本)9783030432287

the rapid development of new technologies and parallel frameworks is a chance to overcome barriers of slow evolutionary induction of decision trees (DTs). this global approach, that searches for the tree structure and tests simultaneously, is an emerging alternative to greedy top-down solutions. However, in order to be efficiently applied to big data mining, both technological and algorithmic possibilities need to be fully exploited. this paper shows how by reusing information from previously evaluated individuals, we can accelerate GPU-based evolutionary induction of DTs on large-scale datasets even further. Noting that some of the trees or their parts may reappear during the evolutionary search, we have created a so-called repository of trees (split between GPU and CPU). Experimental evaluation is carried out on the existing Global Decision Tree system where the fitness calculations are delegated to the GPU, while the core evolution is run sequentially on the CPU. Results demonstrate that reusing information about trees from the repository (classification errors, objects’ locations, etc.) can accelerate the original GPU-based solution. It is especially visible on large-scale data where the cost of the trees evaluation exceeds the cost of storing and exploring the repository. © Springer Nature Switzerland AG 2020.

关键词： Graphics processing unit

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parallel graph partitioning optimization under PEGASUS DA application global state monitoring 13th

Parallel graph partitioning optimization under PEGASUS DA ap...

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13th international conference on parallel processing and applied mathematics, PPAM 2019

作者： Smyk, Adam Tudruj, Marek Grochal, Lukasz Polish-Japanese Academy of Information Technology 86 Koszykowa Street Warsaw02-008 Poland Institute of Computer Science Polish Academy of Sciences 5 Jana Kazimierza Street Warsaw01-248 Poland

ISBN: (纸本)9783030432287

the paper concerns the use of global application states monitoring in distributed programs for advanced graph partitioning optimization. Two strategies for the control design of advanced parallel/distributed graph partitioning algorithms are presented and discussed. In the first one, the parallel algorithm control runs on top of the ready to use basic graph partitioning functions available inside an existing graph partitioning METIS tool. the second control strategy is based on a genetic programing algorithm in which the applied basic graph partitioning primitives and the overall algorithmic parallel/distributed control can be freely designed by the user. In these strategies, the graph partitioning control is executed by processes/threads supervised by the application global states monitoring facilities provided inside a novel distributed program design framework PEGASUS DA. this framework provides system support to construct user-defined strongly consistent global application states and an API to define corresponding execution control. In particular, it concerns computing global control predicates on the constructed global states, the predicates evaluation and asynchronous execution control handling to obtain application global state-driven reactions. Based on such implementation, different features of the graph partitioning optimization strategies have been designed and tested. the experimental results have shown benefits of the new graph partitioning control methods designed with the use of the application global states monitoring. © Springer Nature Switzerland AG 2020.

关键词： Application programs

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A Memetic Algorithm with parallel Local Search for Flowshop Scheduling Problems 9th

A Memetic Algorithm with Parallel Local Search for Flowshop ...

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9th international conference on Bioinspired Optimization Methods and their Applications, BIOMA 2020

作者： Borisovsky, Pavel Kovalenko, Yulia Omsk Department Sobolev Institute of Mathematics SB RAS 13 Pevtsov Street Omsk644043 Russia Institute of Scientific Information for Social Sciences RAS 51/21 Nakhimov Avenue Moscow117997 Russia

ISBN: (纸本)9783030637095

We propose a new memetic algorithm to minimize the makespan for the flowshop scheduling problem in two variants: the classic permutation setting and the no-wait statement. Our algorithm hybridizes the local search technique into the framework of a steady-state genetic algorithm. A local search heuristic is applied on each iteration and explores the insertion neighborhood. the execution of the local search is parallelized using the CUDA framework for Graphics processing Units. the initial population is constructed by means of greedy constructive heuristics. A computational experiment on the benchmark instances shows that the proposed algorithm yields results competitive to those of well-known algorithms for the flowshop problem. © 2020, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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parallel performance of an iterative solver based on the golub-kahan bidiagonalization 13th

Parallel performance of an iterative solver based on the gol...

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13th international conference on parallel processing and applied mathematics, PPAM 2019

作者： Kruse, Carola Sosonkina, Masha Arioli, Mario Tardieu, Nicolas Rüde, Ulrich Cerfacs 42 Avenue Gaspard Coriolis Toulouse31100 France Department of Computational Modeling and Simulation Engineering Old Dominion University NorfolkVA23529 United States Libera Universita Mediterranea Strada Statale CasamassimaBA73125 Italy IMSIA UMR 9219 EDF-CNRS-CEA-ENSTA Université Paris Saclay 828 Boulevard des Maréchaux Palaiseau Cedex91762 France Friedrich-Alexander-Universität Erlangen-Nürnberg Cauerstr. 11 Erlangen91058 Germany

ISBN: (纸本)9783030432287

We present an iterative method based on a generalization of the Golub-Kahan bidiagonalization for solving indefinite matrices with a 2×2 block structure. We focus in particular on our recent implementation of the algorithm using the parallel numerical library PETSc. Since the algorithm is a nested solver, we investigate different choices for parallel inner solvers and show its strong scalability for two Stokes test problems. the algorithm is found to be scalable for large sparse problems. © Springer Nature Switzerland AG 2020.

关键词： Iterative methods

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Analysis of the Efficiency of Speculative parallel Computation 8

Analysis of the Efficiency of Speculative Parallel Computati...

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8th IIAI international Congress on Advanced applied Informatics, IIAI-AAI 2019

作者： Iizuka, Yasuki Hamada, Akira Suzuki, Yosuke Graduate School of Science Tokai University Hiratsuka Japan

ISBN: (纸本)9781728126272

parallel computing is a method of increasing the efficiency of computation in order to solve combinatorial optimization problems, which involve enormous computational complexity. However, conventional parallelization cannot fully utilize computational resources. Speculative computation is another method of parallel computation. We have analyzed theoretically that speculative computation is more effective than conventional parallel processing in some cases. However, analysis of the condition is insufficient. In this paper, we confirm one of the conditions experimentally. © 2019 IEEE.

关键词： Combinatorial optimization

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Plant Disease Diagnosis with Color Normalization 8

Plant Disease Diagnosis with Color Normalization

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8th international conference on Modern Circuit and System Technologies (MOCAST)

作者： Petrellis, Nikos TEI Thessaly Comp Sci & Engn Dept Larisa Greece

ISBN: (纸本)9781728111841

A plant disease diagnosis method based on color histogram invariant features, is evaluated on pear diseases. the employed fuzzy-like classification method is tested with five different normalization methods applied on Red-Green-Blue (RGB), Hue-Saturation-Lightness (HSL), Hue-Saturation Value (HSV) and L*a*b format. three metrics (sensitivity, specificity, accuracy) are used to assess the success of each normalization method. the experimental results show that the success in the disease recognition can be improved by up to 4% if appropriate color normalization is employed.

关键词： plant disease diagnosis normalization image processing smart phone application

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Single-Channel Grayscale processing Algorithm for Transmission Tissue Images Based on Heterogeneity Detection 8th

Single-Channel Grayscale Processing Algorithm for Transmissi...

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8th international conference on Communications, Signal processing, and Systems, CSPS 2019

作者： Zhang, Baoju Zhang, Chengcheng Li, Gang Lin, Ling Zhang, Cuiping Wang, Fengjuan Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin300387 China School of Electronics and Communication Engineering Tianjin Normal University Tianjin30087 China State Key Laboratory of Precision Measurement Technology and Instruments Tianjin University Tianjin300072 China Tianjin Key Laboratory of Biomedical Detecting Techniques & Instruments Tianjin University Tianjin300072 China

ISBN: (纸本)9789811394089

Aiming at the problem of low contrast and unclear edge of gray image in hyperspectral transmission imaging, the single-channel grayscale processing algorithm was applied to the simulated image based on the simulation experiment. the experiment shows that this algorithm improves the contrast to a certain extent and enhances the image edge and grayscale image quality. While improving the quality of grayscale images, this algorithm also triples the number of original images, providing a data enhancement method for heterogeneity detection using deep learning. therefore, this experiment verifies the feasibility of the single-channel processing algorithm and may provide a method for multispectral transmission biological tissue images, it may be a data enhancement method that can be applied to deep learning for tissue image detection. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Breast Cancer Diagnosis Using Image processing and Machine Learning for Elastography Images 8

Breast Cancer Diagnosis Using Image Processing and Machine L...

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8th international conference on Modern Circuit and System Technologies (MOCAST)

作者： Adel, Mohamed Kotb, Ahmed Farag, Omar Darweesh, M. Saeed Mostafa, Hassan Inst Aviat Engn & Technol Giza Egypt Cairo Univ NILES Cairo Egypt GUC Cairo Egypt Univ Sci & Technol Nanotechnol Program Zewail City Sci & Technol Giza 12578 Egypt Cairo Univ Fac Engn Giza Egypt

ISBN: (纸本)9781728111841

As a trending medical imaging technique, Elastography and B-mode (ultrasound) are combined as a diagnostic tool to differentiate between benign and malignant breast lesions based on their stiffness and geometric properties. Image processing techniques are applied to the resulting images for feature extraction. Data preprocessing methods and principal component analysis (PCA) as a dimensionality reduction technique are applied to the dataset. In this paper, supervised learning algorithm "support vector machine (SVM)" is used for the classification of combined elastogram and B-mode images. Model validation is performed with K-fold cross-validation to ensure the generalization of the algorithm. Accuracy, confusion matrix, and logistic loss are then evaluated for the used algorithm. the maximum classification accuracy is 94.12% when using SVM with radial basis function (RBF) kernel.

关键词： Breast Cancer Elastography Image processing Principle component analysis Support Vector Machine (SVM)

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