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检索条件"任意字段=6th International Conference on Applied Parallel Computing"

共 2709 条记录，以下是11-20 订阅

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Image Preheating: Enables Fast Startup of Large-Scale parallel Containerized Jobs 6

Image Preheating: Enables Fast Startup of Large-Scale Parall...

引用

6th international conference on Software Engineering and Computer Science, CSECS 2023

作者： Yeting Gaohongbo Tangbo Yangke Chengdu Aircraft Design & Research Institute Department of Information Technology Chengdu China

ISBN: (纸本)9798350306378

Containers and Kubernetes are becoming the normative way to deploy services in the data center. this rich virtualization ecosystem simplifies application development and management, resolves complex dependencies between applications and execution environments, and improves the efficiency of application deployment. Unfortunately, the startup and deployment delays of containerized jobs significantly limit the efficiency of job execution. In particular, the larger the container image, the longer the image download time, which seriously affects the service processing capability of the system. In this paper, we propose the image preheating technique to accelerate the startup of large-scale distributed parallel containerized jobs. According to the different working scenarios, image preheating is divided into layer preheating and image preheating. First, layer preheating is applied in the scenario where a single machine pulls a single image to start a single container, and we explore the parallel nature of multiple threads in pulling different layers of images and propose a more efficient heuristic algorithm to accelerate parallel pulling of images. Second, image preheating is applied in the scenario where the entire parallel job is deployed and started on the cluster. As soon as one task image of the job is downloaded, the preheating is completed and the entire job starts. In addition, we propose a very practical approach called manual image preheating to provide a better experience for users executing the job. Simulated and real experiments show that the method we designed can improve the download speed of images by 23.54% and the start of jobs in the cluster by 3.73%. © 2023 IEEE.

关键词： Preheating

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Study on the Performance Optimization and Application of Big Model in Big Data Processing 6

Study on the Performance Optimization and Application of Big...

引用

6th international conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Wen, Zebin Wang, Ping Zhang, Jiuyang Xiong, Ping Guangdong Univ Sci & Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9798350377859;9798350377842

With the surge in data volume, big data processing faces unprecedented challenges, among which large models have become a hot research topic due to their powerful data processing capabilities. this paper delves into the performance bottlenecks of large models in big data processing and proposes a series of performance optimization strategies. through a review of existing data processing technologies and large model architectures, combined with optimization theory and practice, this study introduces a comprehensive optimization mechanism that includes resource scheduling, model computational efficiency, and storage and IO. Experiments were conducted in a cloud computing environment to validate these strategies. the results indicate that the optimization strategies significantly enhanced performance when processing different scales of data, improved load balancing and resource utilization, and increased system stability. this research enriches the theoretical study of big data processing and provides effective optimization avenues for the practical application of large models in fields such as data mining and parallel computing. It offers guidance for feature engineering and data preprocessing and paves the way for future research directions.

关键词： Big Data Processing Data Mining parallel computing Feature Engineering Data Preprocessing

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Enhancing Deep Learning Performance through parallel Processing: A Comprehensive Research Study 6

Enhancing Deep Learning Performance Through Parallel Process...

引用

6th international conference on Recent Trends in Advance computing, ICRTAC 2023

作者： thakur, Rudraditya Goel, Naman Bhavesh Vijaykumar, Vaidehi Vellore Institute of Technology Chennai School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350394702

the computational demands of training complex deep learning models have hindered the widespread adoption of this transformative technology. parallel processing techniques have emerged as a promising approach to address this challenge by distributing the computational workload across multiple processors. this research delves into the multifaceted dimensions of enhancing deep learning performance through parallel processing. It explores the theoretical underpinnings, parallelization strategies, hardware and software infrastructures, and application-specific impact of parallel processing in deep learning. © 2023 IEEE.

关键词： Application programs

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Research on Infrared Radiation Calibration Method Based on High Performance parallel computing 6

Research on Infrared Radiation Calibration Method Based on H...

引用

6th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Pang, Lingling Zhang, Ruolan Mao, Xiaodong Yu, Chunchao Yuan, Mingyao Yang, Zhixiong Kunming Institute of Physics Kunming650223 China Yunnan North Photoelectric Instrument Co.Ltd Kunming650200 China

ISBN: (纸本)9798350389418

Infrared radiometric calibration is a method to establish the relationship between the numerical quantized value of the imaging spectrum and the radiance brightness value of the target spectrum, and the calibration of the measured spectrum is an essential link in quantitative analysis. In practical applications, due to the instrument's stability and the influence of the atmospheric environment on the instrument, the calibration results are not well evaluated. In order to quantitatively assess the results of the radiometric calibration of blackbody data at different temperatures, we propose to quantitatively analyze the accuracy of the calibration results based on the noise equivalent spectral radiance (NESR). Taking the self-developed long-wave infrared hyperspectral imaging telemetry system as an example, the algorithm of the relationship between NESR and spectral signal-to-noise ratio (SNR) is parallel optimized based on OpenCL, which can accelerate the calculation of the value of NESR. the theory can be applied to the real-time determination of the radiometric calibration results of the imaging spectrometer. When the radiation calibration is complete, the calibration can be judged as good or bad immediately so as to improve the quality of the bright-temperature data, which has practical significance for further analyzing the telemetry gas. © 2024 IEEE.

关键词： Radiometry

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computing and Tracing Particle on Surface of Overlapping AMR 6

Computing and Tracing Particle on Surface of Overlapping AMR

引用

6th international conference on Software Engineering and Computer Science, CSECS 2023

作者： Yang, Hao Xia, Fang Tang, Dehong CAEP Software Center for High Performance Numerical Simulation Institute of Applied Physics and Computational Mathematics Beijing China

ISBN: (纸本)9798350306378

the computation on the surface of Overlapping Adaptive Mesh Refinement (AMR) is exclusively performed within the non-Overlapping zone of its multi-level patch-based mesh - a distinctive computing mode for Overlapping AMR. Effectuating surface computing based on Overlapping AMR necessitates the reciprocal mapping of two computing modes: Overlapping AMR multi-level computing and non-Overlapping AMR computing. this study consolidates the mapping relationship between these two modes into data mapping, algorithm mapping, and parallel unit mapping. Subsequently, solutions are proposed for the implementation of these mappings, thereby establishing a foundational method for conducting surface computing using Overlapping AMR. Building upon this foundation, the present article utilizes tracing particles on the surface of Overlapping AMR as a paradigm, elucidating how to leverage the aforementioned mapping relationships to construct a surface tracing particle function within our center's JASMIN (J parallel Adaptive Structured Mesh applications INfrastructure). Additionally, a corresponding method for drawing tracing particle trajectories is provided. Functional testing of these elements demonstrates that the application effects of the functions align with expectations. the successful realization of surface tracing particles based on multi-level AMR attests to the completeness of the three aforementioned mappings, providing guidance for practical algorithmic endeavors. the amalgamation of multi-level computing and surface computing based on overlapping AMR empowers users to consider diverse factors such as numerical format, interpolation format, and application performance. this capability enables users to construct applications tailored to their specific requirements, fostering the development of ideal applications. © 2023 IEEE.

关键词： Mapping

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A Comparative Study of Recent 2D Human Pose Estimation Methods 6

A Comparative Study of Recent 2D Human Pose Estimation Metho...

引用

6th international conference on computing and Informatics, ICCI 2024

作者： Elshami, Nada E. Salah, Ahmad Mohsen, Heba Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computers and Informatics Zagazig University Zagazig Egypt

ISBN: (纸本)9798350373875

Human Pose Estimation is a computer vision technique utilized in various fields such as healthcare, security, and sports to detect the pose of single or multi-person utilizing various machine learning and deep learning methodologies. these methodologies can be applied in a variety of ways, including top-down and bottom-up approaches. Furthermore, this technique can be used with images or videos, as well as in 2D or 3D domains. this paper delves into recent advancements in deep learning methods used for estimating the pose, covering commonly employed datasets, evaluation metrics, and the challenges encountered in this field. It aims to assist new researchers in getting familiar with this technique based on work published between 2019 and 2023, with a spotlight on 2D and omitting 3D. © 2024 IEEE.

关键词： Deep learning

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Battery Hierarchical Balancing Method Based on Series-parallel Reconfigurable Topology 6

Battery Hierarchical Balancing Method Based on Series-Parall...

引用

6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Zhang, Hongpeng Hu, Bin Yu, Zilei Wang, Jizhe Li, Wei Bai, Chenzhao Wang, Chenyong Sun, Yuqing Marine Engineering College Dalian Maritime University Dalian China Wuhan Rules & Research Institute China Classification Society Wuhan China

ISBN: (纸本)9798350374315

Inconsistencies within a battery pack will reduce its service life, and failure of a single battery within the pack will cause serious safety issues. In order to settle the matter, this paper proposes a series-parallel reconfigurable battery balancing topology and designs a balancing control strategy. Firstly, the operating principle of the offered balancing topology to change the series-parallel state of battery cells to complete balancing is introduced, and the fault removal method in the case of battery cell failure is given. Secondly, the battery state of charge is selected as the balancing variable and an easily expandable hierarchical balancing control strategy is designed. Finally, a simulation model is built in Simulink, and simulation experiments are carried out on the presented balancing method. the results demonstrate that the balancing topology given in this paper is 33.2% faster than the traditional balancing method. the experimental results also prove the necessity and effectiveness of the hierarchical balancing strategy. © 2024 IEEE.

关键词： State of charge

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Optimizing Edge computing for Telemedicine: A Deep Reinforcement Learning Approach to Task Offloading and Resource Allocation 6

Optimizing Edge Computing for Telemedicine: A Deep Reinforce...

引用

6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Zhang, Jinhao Shi, Changfeng Li, Bingle College of Medicine and Biological Information Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350353174

this study introduces an optimized Mobile Edge computing (MEC) framework specifically designed for telemedicine applications. By integrating deep reinforcement learning (DRL), we develop a system that enhances task offloading and resource management in edge computing environments. Our approach focuses on the critical needs of healthcare monitoring, such as urgency in emergency responses and sustainability in power usage. the proposed model employs a parallel processing architecture that minimizes energy consumption and processing time by intelligently managing data flow and computational tasks across multiple edge servers. Key innovations include the use of a Deep Deterministic Policy Gradient (DDPG) algorithm, which dynamically optimizes the balance between processing speed and energy efficiency, ensuring robust and timely medical services. through extensive simulations, our model demonstrates significant improvements in cost-effectiveness and operational efficiency compared to traditional methods, making it a promising solution for future smart healthcare systems. © 2024 IEEE.

关键词： Computation offloading

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Research on the Application of Intelligent computing and Trustworthy Machine Learning in Financial Complex Systems 6

Research on the Application of Intelligent Computing and Tru...

引用

6th international conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Liu, Zhiyi Zhang, Kai Zheng, Yejie Xu, Shaohua Qu, Jiaxin Eastmoney Informat Co Ltd Eastmoney AI Res Inst Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350377859;9798350377842

the complexity of financial systems and the rapid increase in data volume are making the use of intelligent computing and trustworthy machine learning more important in finance. this paper discusses how intelligent computing can be applied within complex financial systems and takes a deeper look at the theory behind trustworthy machine learning and how it is used in finance. By combining the structure of complex networks with the computing power of machine learning, the paper also explores the inner workings of large neural networks and considers how to apply the theory of dynamic systems to the tuning of these networks, to improve intelligent computing in complex financial systems. Experiments based on the "scientific intelligence + machine conjecture" approach were carried out for risk assessment and market forecasting. the results show that these technologies can improve how financial institutions manage risk, help investors get more reliable information about market trends, and meet the transparency needed for regulatory compliance. the use of intelligent computing and trustworthy machine learning in complex financial systems points to a future with lots of potential for innovation and new opportunities.

关键词： Intelligent computing Trustworthy Machine Learning Financial Complex Systems Fifth Paradigm

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Research on parallel Computation and Memory Optimization of Multi-Core Embedded DSP Technology in High Precision Missile Terminal Guidance Image Tracking Algorithm 6

Research on Parallel Computation and Memory Optimization of ...

引用

6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Xia, Fuming Jing, Bo Huo, Mingge Jiao, Xiaoxuan Wang, Shenglong Pan, Jinxin School of Aeronautical Engineering Air Force Engineering University Xi'an710038 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China

ISBN: (纸本)9798350374315

this paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision missile terminal guidance system. With the increasing requirement of missile strike accuracy in modern war, the vision-based terminal guidance technology has been widely concerned because of its strong autonomy and anti-jamming ability. However, such image processing algorithms are usually highly computation-intensive and data dependent, which requires high real-time and hardware resources. In this paper, the key steps of image tracking algorithm are analyzed and reconstructed deeply, and the architecture suitable for parallel processing of multi-core DSP is designed by modularization. the parallel computing advantages of multi-core DSP are given full play through task division and scheduling strategies to improve the execution efficiency and real-time performance of the algorithm. Secondly, the research also focuses on memory optimization, including the design of data cache strategy, reducing data redundancy, improving memory access locality, etc., and strives to maximize the data reading speed and reduce the memory bandwidth pressure under the condition of limited embedded system resources. To sum up, this research is committed to building an efficient and stable multi-core embedded DSP environment, parallel computing model and memory optimization scheme for high-precision missile terminal guidance image tracking algorithm, which has important theoretical value and practical significance for improving the intelligence level and combat efficiency of missile terminal guidance system. © 2024 IEEE.

关键词： Guided missiles

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