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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Supercomputing conference

作者： Giuseppe M. J. Barca Calum Snowdon Jorge L. Galvez Vallejo Fazeleh Kazemian Alistair P. Rendell Mark S. Gordon School of Computing Australian National University Canberra Australia Department of Chemistry Iowa State University Ames IA United States College of Science and Engineering Flinders University Adelaide Australia

ISBN: (纸本)9781665454452

Correlated electronic structure calculations enable an accurate prediction of the physicochemical properties of complex molecular systems; however, the scale of these calculations is limited by their extremely high computational cost. The Fragment Molecular Orbital (FMO) method is arguably one of the most effective ways to lower this computational cost while retaining predictive accuracy. In this paper, a novel distributed many-GPU algorithm and implementation of the FMO method are presented. When applied in tandem with the Hartree-Fock and RI-MP2 methods, the new implementation enables correlated calculations on 623,016 electrons and 146,592 atoms in less than 45 minutes using 99.8% of the Summit supercomputer (27,600 GPUs). The implementation demonstrates remarkable speedups with respect to other current GPU and CPU codes, and excellent strong scalability on Summit achieving 94.6 % parallel efficiency on 4600 nodes. This work makes feasible correlated quantum chemistry calculations on significantly larger molecular systems than before and with higher accuracy.

关键词： Technological innovation Orbital calculations Codes Scalability Quantum chemistry Graphics processing units Prediction algorithms

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image Debanding Using Iterative Adaptive Sparse Filtering 18

Image Debanding Using Iterative Adaptive Sparse Filtering

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18th image processing: Algorithms and Systems conference, IPAS 2020

作者： Gadgil, Neeraj J. Song, Qing Su, Guan-Ming Dolby Laboratories Inc. SunnyvaleCA United States

Due to a limited bit-depth representation of pixel values and lossy compression methods, an image may exhibit annoying banding or false-contouring artifact. Existing banding-removal approaches mainly include low pass filtering, dithering and re-quantization. In this paper, we propose an image debanding method that iteratively improves the image using a content-adaptive spatial filter. The filter uses adaptive span based on local width of the band and operates on 1D sparse pixel-window. The input image is filtered in each direction i.e. vertical, horizontal and two diagonals sequentially while offering parallel processing in each direction. The iterative framework gradually improves the quality of filtered image until it converges to the final output. Experimental results show that our proposed method is effective in debanding images. © 2020, Society for Imaging Science and Technology

关键词： Pixels

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An Interval-centric Model for distributed Computing over Temporal Graphs 36

An Interval-centric Model for Distributed Computing over Tem...

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IEEE 36th International conference on Data Engineering (ICDE)

作者： Gandhi, Swapnil Simmhan, Yogesh Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781728129037

Algorithms for temporal property graphs may be time-dependent (TD), navigating the structure and time concurrently, or time-independent (TI), operating separately on different snapshots. Currently, there is no unified and scalable programming abstraction to design TI and TD algorithms over large temporal graphs. We propose an interval-centric computing model (ICM) for distributed and iterative processing of temporal graphs, where a vertex's time-interval is a unit of data-parallel computation. It introduces a unique time-warp operator for temporal partitioning and grouping of messages that hides the complexity of designing temporal algorithms, while avoiding redundancy in user logic calls and messages sent. GRAPHITE is our implementation of ICM over Apache Giraph, and we use it to design 12 TI and TD algorithms from literature. We rigorously evaluate its performance for diverse real-world temporal graphs as large as 131M vertices and 5.5B edges, and as long as 219 snapshots. Our comparison with 4 baseline platforms on a 10-node commodity cluster shows that ICM shares compute and messaging across intervals to out-perform them by up to 25x, and matches them even in worst-case scenarios. GRAPHITE also exhibits weak-scaling with near-perfect efficiency.

关键词： Iterative methods

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Coherence parameters estimation algorithm for distributed coherent aperture radar in low SNR environment

Coherence parameters estimation algorithm for distributed co...

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2020 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2020

作者： Wang, Xueqi Tu, Gangyi Wu, Shaopeng No.724 Research Institute of China Shipbuilding Industry Corporation Human Resources Department Nanjing China No.724 Research Institute of China Shipbuilding Industry Corporation General Department of Detection System Nanjing China No.724 Research Institute of China Shipbuilding Industry Corporation Development Demonstration Center Nanjing China

ISBN: (纸本)9781728160665

A method of reducing parameters estimation error by multi-pulse accumulation is proposed to deal with the problem of large error and low coherence performance of distributed coherent aperture radar in low SNR(signal-to-noise ratio) environment. This method uses different coherent accumulation methods for low-speed and high-speed moving targets respectively. For low-speed targets without range migration, discrete Fourier transform is used to achieve multi-pulse coherent accumulation. For high-speed targets, the keystone transform is used to correct the range migration firstly, and then the coherent accumulation is realized by discrete Fourier transform. Then, the peak picking method is used to estimate the coherence parameters. Finally, the coherence parameters estimation process in low SNR environment is given in combination with two cases. The simulation results show that, compared with the method of monopulse signal, this method can obtain higher estimation performance of coherence parameters in low SNR environment. And with the increase of accumulated pulses, the estimation performance of coherence parameters also improves. © 2020 IEEE.

关键词： Parameter estimation

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Improving the performance of processing special computing tasks using an asynchronous actor model 10

Improving the performance of processing special computing ta...

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10th International conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2020

作者： Andrianova, E.G. Sachkov, V.E. Zhukov, D.O. MIREA Russian Technological University 78 Vernadsky Avenue Moscow119454 Russia

The use of third-party ready-made solutions to perform special computing tasks (SCT): processing text arrays in a natural language, the use of cryptography methods in data transmission networks, machine learning in solving applied problems, scientific computations and many other ones is a common practice in modern development of software systems. However, the use of third-party ready-made software solutions often leads to their poor optimization for parallel and distributed operation and to the difficulty in vertical and horizontal scaling, impossibility of modifying a solution, e.g., due to its legal protection, etc. In order to solve these problems, an asynchronous actor model can be used that facilitates developing software for processing special computing tasks. In addition, an asynchronous actor model allows parallel processing of input tasks, provides asynchronous operation, and extends its functionality to third-party ready-made solutions, that it is impossible or forbidden to modify to meet user's or optimization requirements. methods, algorithms, and the entire logic of asynchronous actor operation involve transitions of actors from one state to another under the effect of input messages and returning the results of processed tasks to the frame system. This allows setting an automatic grammar and for using methods and approaches for developing software systems based on finite-state automata. The asynchronous actor operation scheme is represented by a directional state graph, which allows using the mathematical apparatus of the finite-state automaton theory (the Mealy model) to describe the same. The developed model is convenient for designing and organizing the SCT processing using computer systems. The use of the asynchronous actor model makes it possible, in special operation modes, to increase the performance of poorly optimized SCT without modifying the same, as compared to task processing without using the asynchronous actor model. © Published under li

关键词： Computer software

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Research on FPGA High-Performance Implementation Method of CNN

Research on FPGA High-Performance Implementation Method of C...

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

作者： XiangChe Zhen Bin He School of information science and technology Beijing University of chemical technology

Convolutional Neural Networks (CNN) are widely used in such fields as image recognition, object detection and image segmentation. These application scenarios have high requirements on real-time data processing capabilities. With the advantages of good flexibility, strong parallel processing ability and low power consumption, Field Programmable Gate Array (FPGA) is very suitable as a hardware platform for processing CNN. Based on the CNN model Lenet-5, this study will explore the high-performance implementation methods for using CNN in image recognition, and further design and implement a CNN hardware accelerator IP core based on FPGA. First, it uses Pytorch to train the network model and extract the weight parameters; then, it uses C language in the Xilinx High-level Synthesis (HLS) to model the forward inference structure of the CNN. The model designed is then optimized with different optimization strategies, including storage structure optimization, pipeline optimization and fixed-point quantization, so as to improve the performance of the hardware accelerator. Finally, the optimized design model is converted into a hardware accelerator through HLS. The experimental results demonstrate that the implementation performance of the CNN accelerator with different optimization strategies can be greatly improved, and it can meet the requirements of high-real-time application scenarios of CNN.

关键词： image recognition Quantization (signal) Power demand Tools Hardware Real-time systems Numerical models

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Accelerating Message Passing Operation of GDL-Based Constraint Optimization Algorithms Using Multiprocessing

Accelerating Message Passing Operation of GDL-Based Constrai...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Syeed Abrar Zaoad Tauhid Tanjim Mir Hasan Md. Mamun-Or-Rashid Ibrahem Abdullah Almansour Md. Mosaddek Khan University of Dhaka Dhaka Bangladesh Austin Peay State University Clarksville TN USA King Abdulaziz City for Science and Technology Riyadh SA

This paper develops a new message passing protocol that can be used to speed up the inference process of Generalized Distributive Law (GDL) based constraint optimization algorithms. In particular, we parallelize the inference process of the GDL-based constraint optimization algorithms which have been widely used to solve Constraint Optimization Problems (COPs) in different real world applications, such as wire routing, image analysis, computer-aided gas pipeline operation, and numerical optimization. It is worth noting that the proposed new parallel approach can be applied to accelerate any GDL-based message passing algorithms, such as Max-Sum, Max-Product or Sum-Product. In this work, we make use of the available Central processing Unit (CPU) cores of a system to minimize the time it requires to execute a given algorithm. Consequently, this reduced computation time will accelerate the GDL-based COP algorithms which can be used in larger systems. However, the main challenge is to maintain the quality of the solution while minimizing the completion time. Our proposed protocol specifically takes this trade-off into account, and our empirical results depict that it is able to produce the same solution quality while accelerating the inference process 2–10 times faster compared to the state-of-the-art.

关键词： Constraint optimization Protocols image analysis Message passing Wires Pipelines Routing

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CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on image Deduplication

CE-Dedup: Cost-Effective Convolutional Neural Nets Training ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Xuan Li Liqiong Chang Xue Liu School of Computer Science McGill University Montreal Quebec Canada

Attributed to the ever-increasing large image datasets, Convolutional Neural Networks (CNNs) have become popular for vision-based tasks. It is generally admirable to have larger-sized datasets for higher network training accuracies. However, the impact of dataset quality has not to be involved. It is reasonable to assume the near-duplicate images exist in the datasets. For instance, the Street View House Numbers (SVHN) dataset having cropped house plate digits from 0 to 9 are likely to have repetitive digits from the same/similar house plates. Redundant images may take up a certain portion of the dataset without consciousness. While contributing little to no accuracy improvement for the CNNs training, these duplicated images unnecessarily pose extra resource and computation consumption. To this end, this paper proposes a framework to assess the impact of the near-duplicate images on CNN training performance, called CE-Dedup. Specifically, CE-Dedup associates a hashing-based image deduplication approach with downstream CNNs-based image classification tasks. CE-Dedup balances the tradeoff between a large deduplication ratio and a stable accuracy by adjusting the deduplication threshold. The effectiveness of CE-Dedup is validated through extensive experiments on well-known CNN benchmarks. On one hand, while maintaining the same validation accuracy, CE-Dedup can reduce the dataset size by 23%. On the other hand, when allowing a small validation accuracy drop (by 5%), CE-Dedup can trim the dataset size by 75%.

关键词： Training Benchmark testing Convolutional neural networks Task analysis image classification

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Determination of Shelled Corn Damages using Colored image Edge Detection with Convolutional Neural Network 12

Determination of Shelled Corn Damages using Colored Image Ed...

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IEEE 12th International conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)

作者： Yumang, Analyn Magwili, Glenn Montoya, Sev Kyle C. Zaldarriaga, Corleone Jorel G. Mapua Univ Sch EECE Manila Philippines

ISBN: (纸本)9781665419710

In the Philippines corn is one of the top agricultural products produced in the country, specifically yellow corn. It is distributed in various cities and provinces to consumers. It is important that the corn kernels to undergo quality assurance before releasing them to the consumers. The methods for evaluating and qualifying corn kernels that are employed by most farms in the country are only done by manual human inspection and these methods are inconsistent which results to inaccurate findings. This is more prevalent when dealing with large amounts of kernels that need to be qualified. This study offers to reduce those inconsistencies by implementing a neural network-assisted method of inspection. The damages to corn kernels can be determined by its physical attributes and as such, the neural network will easily detect the type of damage within a given sample. Aside from the healthy kernels, the types of damage that was included in this study are the following: drier damage, heat damage, heat damage (drier phase), OCOL (Other Color) Type A and OCOL Type B .The neural network that will be used will be a Convolutional Neural Network wherein the images of the samples are subjected to layers of processing. This study also uses Colored image Edge Detection. The detection method used in this study has obtained an accuracy rating of 96.66%.

关键词： Convolutional Neural Network Corn kernels Edge Detection Colored image Edge Detection

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TOWARDS MAKING UNSUPERVISED GRAPH HASHING ROBUST

TOWARDS MAKING UNSUPERVISED GRAPH HASHING ROBUST

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IEEE International conference on Multimedia and Expo (ICME)

作者： Gu, Xuesong Dong, Guohua Zhang, Xiang Lan, Long Luo, Zhigang NUDT Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China NUDT Inst Quantum Informat & HPCL Changsha 410073 Hunan Peoples R China NUDT Coll Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781728113319

Unsupervised hashing without supervision easily deteriorates in the case of grossly corrupted data. Motivated by robust optimization, this paper proposes a dual-graph regularized robust hashing (DGRH) based on both manifold smoothness and robust estimators in a more intuitive manner. Orthogonal to existing robust hashing methods, DGRH directly removes the outliers of datasets with M-estimator to exert robustness. In specific, it intends to recover low-rank representation from corrupted data via l(1) loss while preserving neighborhood relationships among samples with dual-graph regularization. Although DGRH seems a simple extension of robust PCA on graphs with hashing trick, it is easy to implement yet effective. Theory analysis is provided to support our claim. Experiments of image retrieval on three popular benchmark datasets show the efficacy of DGRH as compared to several well-behaved representative counterparts.

关键词： Discrete graph hashing image retrieval unsupervised learning robust hashing

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