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On GPU optimizations of stencil codes for highly parallel simulations 29

On GPU optimizations of stencil codes for highly parallel si...

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29th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Pfisterer, Nikolai Berghoff, Marco Streit, Achim Karlsruhe Inst Technol Steinbuch Ctr Comp Karlsruhe Germany

ISBN: (纸本)9781665414555

Stencil codes are valuable methods to solve partial differential equations of models in a wide range of applications in science and engineering. Graphics processing units (GPUs) provide a highly parallel architecture with fast directly accessible memory that is desirable to run stencil codes. They enable larger and more complex simulations that are solved faster compared to simulating on CPUs. We provide a solution for users to run highly parallel stencil codes on GPUs, that can also be efficiently used on a distributed GPU cluster. In this work, we present an extension to the multi-disciplinary framework NAStJA originally designed to efficiently run stencil codes on the CPUs in current high-performance computing (HPC) systems, to also be able to run on GPUs. We describe different methods to increase the performance of stencil codes on GPUs like a border exchange method which is transparent to the user, as well as a buffer for gradient values that are needed multiple times. We show their performance using the phase-field method as a stencil code example. With this GPU extension and optimizations implemented into the NAStJA framework, we can show highly improved performance compared to the CPU implementation, and an efficiency of 92% (weak scaling) for a large-scale example simulation on 64 GPUs on the ForHLR II HPC system.

关键词： GPU optimizations stencil code heterogeneous parallelization scalability analysis scalability analysis

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Industrial CT Truncated Projection Data Reconstruction System Based on Artificial Intelligence

Industrial CT Truncated Projection Data Reconstruction Syste...

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Signal processing and Intelligent Computing (SPIC), International conference on

作者： Yang Yang Songjie Wu Liaoning Institute of Science and Technology Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In industry, Computer Tomography (CT) is a key technology used for non-destructive testing and evaluation of internal structures, but significant challenges remain in the processing of truncated projection data. The aim of this study is to investigate an artificial intelligence-based reconstruction system for industrial CT truncated projection data. We investigate the common data truncation problem in industrial CT technology and propose a new data reconstruction method. The method uses the ideas of full variance minimization and segmentation constants to achieve accurate reconstruction of truncated projection data by means of parallel computation. Our study validates the effectiveness and superiority of the method through comparative experiments. Compared with conventional methods, our system excels in both accuracy and stability of reconstruction results, and is better able to handle the challenges posed by truncated data. In addition, we explore the practical application value of the system, demonstrating its potential use in industrial CT. From a single-core to a dual-core processor, the processing time is reduced from 120 to 60 seconds, showing a doubling of the processing speed, which indicates that tasks are efficiently distributed and executed among multiple cores. The research results in this paper will have a positive impact on the development and application of industrial CT technology and promote its wide application in industrial production and quality control.

关键词： Technological innovation image segmentation Accuracy TV Computed tomography parallel processing Minimization Stability analysis image reconstruction Testing

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DL Based System for On-Board image Classification in Real Time, Applied to Disaster Mitigation 7

DL Based System for On-Board Image Classification in Real Ti...

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7th International conference on parallel, distributed and Grid Computing, PDGC 2022

作者： Shukla, Amogh Almal, Shubh Gupta, Ankur Jain, Rinisha Mishra, Rishabh Dhabliya, Dharmesh Vellore Institute of Technology School of Cse TL Vellore India Vaish College of Engineering Department of Cse Haryana Rohtak India Vishwakarma Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9781665454018

Natural phenomena such as earthquakes that cause the destruction of bridges and can cause tsunamis, among other phenomena and disasters, are on the rise as a result of climate change, which is affecting our planet. This is because of the rise in average global temperature and the development of extreme rains, which causes rivers to overflow. The use of satellites for terrestrial observation has made it possible to keep tabs on events throughout the world in recent years. However, these satellite systems aren't without their drawbacks;one is that cloud cover may make it difficult to accurately record photographs. It is done based on the clouds present, and another drawback is the visiting time that the satellites have, which means that the area that you want to record the image is not always available, so you have to wait for the day and the time in which the satellite orbit can pass through the area of interest that the image is to be recorded. Drones, which fly at low altitudes and do not have cloud problems, are one such alternative, solving a problem of satellites, on cloud cover. In light of this, in the present work, a methodology based on low-cost systems is proposed for on-board processing in drones of such data in the event of an emergency, such as after the effects of rain or an earthquake. Methodology involves processing the image in an embedded device, which is coupled to the drone and connected to the camera, in such a way that the information that is sent to the ground station, concentrates on the affected area;this allows for faster decision-making and lessens the impact of the natural disaster. Therefore, it is shown by testing of the embedded computer based on a Raspberry pi 3 for detecting changes in land cover and transmitting this information to an earth station. © 2022 IEEE.

关键词： Earthquakes

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Exploration on Facial image Recognition and processing Algorithms under Hadoop

Exploration on Facial Image Recognition and Processing Algor...

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Integrated Intelligence and Communication Systems (ICIICS), International conference on

作者： Meifang Gu Hailin Tang Lili Li Institutional Research Center Guangdong Baiyun University Guangzhou Guangdong China Faculty of Intelligent Manufacturing Engineering Guangdong Baiyun University Guangzhou Guangdong China Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China

Face recognition is an important technology in the field of digital image processing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of Hadoop algorithm and facial feature extraction. The experimental results were validated in the MATLAB environment and trained using neural networks to improve system performance and adaptability. The test results indicated that the processing speed was between 0.84 and 0.97; the highest resource utilization rate was 0.06, and the lowest was 0.02; the throughput range of facial data was between 13598 and 15479, and the throughput range of user data was between 5037 and 5879. This indicated that the system had fast recognition speed and high-precision image output, and exhibited good fault tolerance and robustness compared to other methods. Exploring facial image recognition and processing algorithms in the Hadoop environment can utilize Hadoop’s distributed computing capabilities to accelerate the processing of large-scale facial image data, achieve fast and efficient processing of large-scale facial image data, accelerate algorithm operation, and possess distributed computing capabilities. Multiple computing nodes can be used for parallel processing to improve algorithm performance.

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Automatic Segmentation of Pneumothorax in Chest Radiographs Based on Dual-Task Interactive Learning Method 23

Automatic Segmentation of Pneumothorax in Chest Radiographs ...

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5th International conference on image, Video and Signal processing, IVSP 2023

作者： Wang, Yaping Lu, Zhenkun School of Artifcial Intelligence Guangxi Minzu University Key Laboratory of Intelligent Unmanned System and Intelligent Equipment Education Department of Guangxi Zhuang Autonomous Region China

ISBN: (纸本)9781450398381

Pneumothorax is one of the common diseases of the lungs and can be fatal in severe cases. With the development of deep learning, the convolutional neural network-based methods have been well used in the field of medical images. However, the chest X-ray images themselves are very complex, it is difficult for current algorithms to achieve pneumothorax segmentation well. Therefore, to address the problem that current deep learning-based segmentation methods cannot meet the demand of existing pneumothorax image segmentation tasks, this paper presents a novel spatially adaptive weighted fusion Signed Distance Map (SAWFSDM) network segmentation model. The proposed model utilizes the rich boundary information in the Signed Distance Map (SDM) to improve the segmentation results by learning the binary mask and SDM symbolic distance map in parallel through a multi-task learning strategy, which provides more supervised information to the training of the model through multiple labels. In addition, the boundary information learned from the SDM branch is fused into the binary mask branch during the decoding process, which strengthens the ability of model feature extraction and makes the model learn more accurate contour shapes. The segmentation model proposed in this paper has been verified by a large number of experiments on the SIIM-ACR pneumothorax segmentation dataset, and the results show that the SAWFSDM model can effectively sense the boundaries of the pneumothorax and improve the accuracy of pneumothorax segmentation, which has significant advantages compared with other advanced pneumothorax segmentation methods. © 2023 ACM.

关键词： image segmentation

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SR2C: A Structurally Redundant Short Reads Collapser for Optimizing DNA Data Compression 29

SR2C: A Structurally Redundant Short Reads Collapser for Opt...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Sun, Hui Ma, Huidong Zheng, Yingfeng Xie, Haonan Wang, Xiaofei Liu, Xiaoguang Wang, Gang Nankai University College of C.S. Nankai-Orange D.T. Joint Lab Nankai-Baidu Joint Lab ICIC TMCC SysNet DISSec GTIISC Tianjin300350 China Guangxi University School of Electrical Engineering Nanning530004 China

ISBN: (纸本)9798350330717

The current redundant sequence deduplication algorithms cannot remove structural repetitive DNA short reads such as mirror, reverse, paired, and complementary palindromes in high-throughput genomics sequencing data. Moreover, these methods also cannot construct indexes to recover the original sequences, thus failing to meet the requirements of lossless compression for downstream applications. To address these problems, we propose a data structure called Cycle-Hash-Linkage (CHL) and present a CPU parallelism optimization algorithm named SR2C (Structurally Redundant Short Reads Collapser) based on CHL to improve the compression ratio of DNA sequencing data. Experimental results on actual data from the NCBI database demonstrate that SR2C achieves an average residual sequence percentage improvement of 2.556% compared to the state-of-the-art redundant sequence deduplication algorithm, Minirmd. Furthermore, SR2C cascaded optimization improves the average compression ratios of compression algorithms Pigz, PBzip2, XZ, and 7Z by 92.345%, 78.999%, 10.132%, and 7.434%, respectively. By leveraging multi-core CPU parallel computation, SR2C effectively reduces time consumption, which achieves 2-5X deduplication and recovers *** same name Linux toolkit is freely available at https://***/fahaihi/SR2C. © 2023 IEEE.

关键词： data compression DNA sequencing data compression parallel algorithm redundancy deduplication rolling-hash algorithm structurally redundant reads

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High Performance Stingray: fast spectral timing for all

High Performance Stingray: fast spectral timing for all

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Matteo Bachetti Eleonora Veronica Lai Daniela Huppenkothen Guglielmo Mastroserio Matteo Lucchini Osservatorio Astronomico di Cagliari Istituto Nazionale di Astrofisica Cagliari Italy Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam the Netherlands Dipartimento di Fisica Università degli Studi di Milano Milano Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Celestial objects are known to be change in brightness over time, driven by a diverse combination of physical processes, whose time scales range from sub-milliseconds to billions of years. Stingray is an open-source Python package that brings advanced time series analysis techniques to the astronomical community, with a focus on high-energy astrophysics, but built on top of general-purpose classes and methods that are designed to be easily adapted and extended to other use cases. We describe the work being done to adapt Stingray to the analysis of large data archives. In particular, we measure the performance and scalability of Stingray and use parallel computing to speed up selected parts of the code.

关键词： Codes Atmospheric measurements Astrophysics Scalability Time series analysis Brightness parallel processing Particle measurements Extraterrestrial measurements Timing

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High-speed and Low-latency 3D Sensing with a parallel-bus Pattern 10

High-speed and Low-latency 3D Sensing with a Parallel-bus Pa...

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International conference on 3D Vision (3DV)

作者： Miyashita, Leo Tabata, Satoshi Ishikawa, Masatoshi Univ Tokyo 7-3-1 HongoBunkyo Ku Tokyo Japan Tokyo Univ Sci 1-3 KagurazakaShjinjuku Ku Tokyo Japan

ISBN: (纸本)9781665456708

High-speed 3D shape sensing is an essential technology for three-dimensional recognition and manipulation in dynamic scenes. However, conventional high-speed sensing methods mainly focus on the image capturing speed and the actual processing time to obtain a point cloud is not optimized in the measurement scheme and sensing pattern configuration. On the other hand, measurement latency is critical to respond in real-time and physically handle the dynamic scenes. This paper introduces a physically stereo-rectified projector-camera system for high-speed and low-latency 3D sensing with fast sequential memory access in the decoding process. Moreover, we configure a structured light pattern named "parallel-bus pattern" with a De Bruijn torus and clock lines to maximize information density and robustly decode the pattern as data transfer in a parallel bus interface. We measured dynamically moving and deforming objects with the proposed system and evaluated the measurement performance. As a result, the developed 3D sensing system with the parallel-bus pattern achieved 27 K-points measurement at higher than 1000 fps with 0.336 ms latency and 0.838 mm accuracy on average.

关键词： De Bruijn torus high speed image processing low latency spatially encoded structured light

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PFN: A Target Item-enhanced Click-Through Rate Prediction via parallel Fusion Network 29

PFN: A Target Item-enhanced Click-Through Rate Prediction vi...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Lin Liu, Fang'ai Li, Yinghua Zhuang, Xuqiang Zhao, Xiaohui Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Normal University School of Mathematics and Statistics Jinan250358 China

ISBN: (纸本)9798350330717

The purpose of the click-through rate is to predict the probability that a user is most likely to click on a recommended item, garnering extensive attention in both academia and industry. In recent studies, it has been shown that high-quality user representation and feature interaction contribute significantly to improving accuracy in prediction tasks. However, the current methods still face two challenging problems. First, the behavior sequences contain complex interest features, and it is difficult to effectively capture the latent dominant interests. Besides, most models neglect the latent synergy between fine-grained features (i.e., they pay less attention to key feature interaction). To address these problems, in this paper, a target item-enhanced parallel fusion network (PFN) is proposed. First, the user's historical interests are enhanced through a transformer. Then, the Pearson function is employed to gauge the strength of the relationship between the enhanced interest features and the target item, thus accentuating the user's latent dominant interests. Third, feature interaction is learned via an equal interaction network, and then a soft-attention network is used to filter out unnecessary noise and retain fine-grained feature interaction to enhance the expressive ability of latent feature synergies. In addition, a multi-layer perceptron network is used to model the above features and learn the high-order representation of users' more relevant interests. We have conducted extensive experiments on four public datasets and the PFN shows excellent performance. © 2023 IEEE.

关键词： Click-through rate Feature interaction Multi-dominant interest attention network Target item

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Off-grid Control Technology for distributed Mobile Energy Storage System Based on Multi-machine parallel Operation

Off-grid Control Technology for Distributed Mobile Energy St...

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

作者： Dongqing Liu Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

In off-grid mode, the voltage and current stability of microgrids are poor, which can lead to poor performance of distributed mobile energy storage system (DMESS) off-grid control. To address this issue, a method based on multi-machine parallel operation for off-grid control of DMESS is proposed. A model of multi-machine parallel operation for DMESS is designed, and the power allocation ratio of parallel machines is determined reasonably. Through the dual-loop control technology of outer loop capacitor voltage and inner loop capacitor current, the dynamic performance and load disturbance resistance of the energy storage system are improved, realizing stable control of voltage and current during microgrid off-grid operation. Experimental results demonstrate that when a 45 kW, 10 kVar load is injected into the microgrid, the designed technology can restore voltage and maintain voltage stability at 0.7 s after the load injection. When the second energy storage system is injected, stable current can be maintained, achieving current equalization. This indicates that the proposed method can effectively control the stability of microgrid voltage and current under off-grid operation and achieve current equalization, demonstrating excellent control performance.

关键词： Renewable energy sources Scalability Capacitors Microgrids parallel machines Control systems Stability analysis Voltage control Optimization Energy storage

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