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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10372 条记录，以下是411-420 订阅

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Module-Based Approach for Detecting performance Bugs in Java Applications at Scale 49

Module-Based Approach for Detecting Performance Bugs in Java...

引用

49th Annual conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wen, Yingying Cheng, Guanjie Huawei Cloud Department of Architecture and Technique Innovation Hangzhou China Zhejiang University Department of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350331820

Hundreds of thousands of Java applications have been deployed in data centers at our production Cloud to support burst peak traffic. However, detecting performance bugs in Java can be difficult as they may not prevent the applications from running correctly, and may not even manifest at low loads. Profiling data collected from production provides insight into the actual running states of applications. In this paper, we aim to identify hot spots for further performance debugging by analyzing profiling data from tens of thousands of machines in the data center using a module-based approach. We present our practical experience with module classification, which allows for filtering of out-of-range modules and long-duration modules of high utilization. Our study proposes a heuristic solution to detect performance bugs in Java at scale. © 2023 IEEE.

关键词： program debugging

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Exploring Scalability of Value-Flow Graph Construction 24

Exploring Scalability of Value-Flow Graph Construction

引用

4th International conference on Artificial Intelligence, Automation and high performance Computing, AIAHPC 2024

作者： Xu, Rulin Peng, Hua Yang, Kai Mao, Xiaoguang College of Computer National University of Defense Technology China Changsha Public Security Bureau China Hunan Provincial Public Security Bureau China

ISBN: (纸本)9798400710049

In modern software systems, the construction of Value-Flow Graphs (VFGs) is a critical step for performing static analysis and vulnerability detection. However, traditional VFG construction methods are computation- ally complex, making them inefficient for large-scale code analysis. This paper proposes a parallelized VFG construction algorithm, P-VFGC, which accelerates the VFG construction process by leveraging multi-core resources. Firstly, we perform parallel pointer analysis to quickly determine the pointer relationships within the program. Subsequently, we employ parallel methods to construct memory SSA and partition the pro- gram’s basic blocks, laying the groundwork for VFG construction. Finally, by parallel processing of basic blocks, we connect the nodes and edges of the VFG to complete the graph construction. Experimental results demonstrate that P-VFGC exhibits good scalability and performance when handling large-scale code, significantly reducing the analysis time while maintaining a certain level of precision, achieving a maximum speedup of 4.53 times on an 8-node setup. © 2024 Copyright held by the owner/author(s).

关键词： Scalability

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Ensemble-Stacking Machine Learning Model for Multi-Class Skin Diseases Identification 5

Ensemble-Stacking Machine Learning Model for Multi-Class Ski...

引用

5th International conference on Biomedical Engineering, IBIOMED 2024

作者： Tandelon, Dennis Christiono, David Lawrence Andryani, Nur Afny Catur Gondokaryono, Srie P. Rahardja, July I. Danarti, Retno School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Department Jakarta Indonesia Indonesian Society of Dermatology & Venereology Padjadjaran University Faculty of Medicine Indonesia Dermatovenereology Department Indonesia Indonesian Society of Dermatology & Venereology Gadjah Mada University Faculty of Medicine Indonesia Dermatovenereology Department Indonesia

ISBN: (纸本)9798350389265

Algorithm development on skin diseases identification with high cardinality classification is still very challenging. In addition, the high similarity appearance among diseases makes the computer vision approach meet high difficulties on providing clear distinct feature representation. In general, some literature shows that the performance of single machine learning or single deep learning techniques can be further improved when merged by ensemble mechanism. This approach has been applied on binary skin's cancer diseases diagnosis prediction and successfully improve the prediction ability. This paper elaborates the potential of ensemble machine learning approaches using stacking techniques on conducting feature extraction and classification model for identifying more general skin diseases based on dermoscopy images and mobile phone images. The proposed model combines three different CNN architectures, ResNet50, Xception, and VGG16 to extract features from the image then stacked as input for the model trained using five different algorithms, which are logistic regression, support vector machines, multi-layer perceptron, k-nearest neighbors, random forests and the combination of these algorithm in stacking classifier as the base estimators and final estimator. The simulation results present the proposed ensemble - stacking model succeeds to improve the identification ability in all utilized datasets. In addition, the proposed ensemble - stacking models show better recall which indicates good sensitivity on handling false negative. It is beneficial for medical application. © 2024 IEEE.

关键词： Federated learning

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Charliecloud's layer-free, Git-based container build cache

Charliecloud's layer-free, Git-based container build cache

引用

2023 International conference on high performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Priedhorsky, Reid Ogas, Jordan Davis, Claude H. Rusty Hounshel, Z. Noah Lee, Ashlyn Stormer, Benjamin Goff, R. Shane High Performance Computing Division Los Alamos National Laboratory Los AlamosNM United States University of North Carolina Wilmington United States Colorado State University United States University of Texas Austin United States

ISBN: (纸本)9798400707858

A popular approach to deploying scientific applications in high performance computing (HPC) is Linux containers, which package an application and all its dependencies as a single unit. This image is built by interpreting instructions in a machine-readable recipe, which is faster with a build cache that stores instruction results for re-use. The standard approach (used e.g. by Docker and Podman) is a many-layered union filesystem, encoding differences between layers as tar archives. We describe a new approach, implemented in Charliecloud: store changing images in a Git repository. Our experiments show this performs similarly to layered caches on both build time and disk usage, with a considerable advantage for many-instruction recipes. Our approach also has structural advantages: better diff format, lower cache overhead, and better file de-duplication. These results show that a Git-based cache for layer-free container implementations is not only possible but may outperform the layered approach on important dimensions. © 2023 Owner/Author.

关键词： Containers

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An Advanced Deep Learning-Based high-Resolution μCT Images Construction Method for Cement Hydration Microstructure 20th

An Advanced Deep Learning-Based High-Resolution μCT Images ...

引用

20th International conference on Intelligent Computing (ICIC)

作者： Han, Ruiqi Zhang, Liangliang Zhang, Pan Wang, Lin Yang, Bo Xie, Yuhang Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Quan Cheng Lab Jinan 250100 Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Analyzing the microstructure images of cement can help identify and quantify the internal material structure, understand the hydration mechanism, and design cement materials. Micro-CT (mu CT), as an advanced physical imaging device, can image internal structural information of cement hydration at the microscopic scale. However, due to limitations in the equipment price, maintenance costs, and single imaging time of the equipment itself, the acquisition of high-definition mu CT images for cement hydration microstructure faces high scanning economic costs. Additionally, limited by the imaging mechanism of the physical devices, the size of the cement samples affects image resolution. This paper proposes a high-resolution mu CT images construction method for cement hydration microstructure based on deep learning. Real-ESRGAN network is introduced to improve the resolution of mu CT images obtained from physical devices after imaging, on a super-resolution reconstruction approach. In addition, a refined high-order degradation is designed and applied to Real-ESRGAN to suit the characteristics of mu CT image data. Experiment results validated the good performance of the proposed method in constructing high-resolution mu CT images and were extended to historical images imaged by devices that are currently technologically backward.

关键词： Micro-CT images Super-resolution Cement hydration Deep learning

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Few-Shot Arbitrary Super-Resolution Considering Consistency and Realness

Few-Shot Arbitrary Super-Resolution Considering Consistency ...

引用

2025 International conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Joonhee Bang, Junseo Chun, Se Young Department of Electrical and Computer Engineering Korea Republic of INMC & IPAI Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798331510756

Super-resolution (SR) is the task of reconstructing visually natural high-resolution (HR) images from low-resolution (LR) images. This field has made significant advancements with the development of deep learning, and recent research has focused on arbitrary-scale super-resolution, where a single model performs upsampling across continuous scales. However, most existing studies on arbitrary-scale super-resolution have relied on supervised learning models, which require large amounts of data and annotations. We propose a novel approach that demonstrates performance equal to or better than existing methods while utilizing fewer data, thus opening up new possibilities in this domain. This method also presents the potential for personalization, allowing the model to be adjusted according to individual user preferences or requirements. Specifically, we propose a method that integrates a few-shot arbitrary super-resolution upsampler trained with limited data into a zero-shot learning framework for super-resolution based on existing Diffusion Posterior Sampling (DPS) techniques. Additionally, this approach addresses the issues of insufficient detail representation in traditional regression-based SR methods and the lack of fidelity to the original images observed in generative-based SR methods. To achieve this, we employ a parallel diffusion model that uses DPS in both spatial and Fourier domains to simultaneously generate two types of images while maintaining consistency and realness. As a result, our method achieves performance in both PSNR and LPIPS that is competitive with supervised learning-based regression and generative SR methods. © 2025 IEEE.

关键词： Self-supervised learning

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Development and Analysis of a high-Efficiency T-Type Inverter based 3.7 kW Wireless EV Charging Applications

Development and Analysis of a High-Efficiency T-Type Inverte...

引用

2025 IEEE International Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Venugopal, R. Balaji, C. Shanmugam, Yuvaraja Srm Institute of Science and Technology Dept. of Electrical and Electronics Engineering Tamil Nadu Chennai603203 India Vignan's Foundation for Science Technology and Research Department of Eee Andhra Pradesh Vadlamudi522213 India

ISBN: (纸本)9798331529833

This paper presents the design and performance analysis of a T-type inverter optimized for wireless charging applications in electric vehicle (EV) batteries, aiming to enhance efficiency and reduce power losses. Traditional H-bridge inverter-based wireless power transfer (WPT) systems face challenges such as complex circuitry, voltage spikes, switching noise, and losses from switching, which impact overall efficiency. The proposed T-type inverter addresses these issues by improving voltage control and reducing switching losses, resulting in enhanced system efficiency. The system employs a series-series (S-S) compensation technique that provides better transmission control and performance, making it more adaptable to diverse load conditions. This compensation method, combined with optimized alignment tolerance and a high coupling coefficient, allows the system to efficiently use rectangular coils, which generate a more uniform magnetic field and minimize current loss. Designed for a 3.7 kW output at an operating frequency of 85 kHz, the T-type inverter-based WPT system was validated through MATLAB/Simulink simulations and a laboratory prototype. Results demonstrate an efficiency of 95.20% in simulation and 93.248% in hardware testing. Additionally, DC-DC efficiencies achieved are 94.78% in simulation and 96.177% in hardware. This research confirms the potential of T-type inverters in high-efficiency wireless EV charging systems. © 2025 IEEE.

关键词： Inductive power transmission

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Improving Regression in Spiking Neural Networks for Oceanographic Data Analysis

Improving Regression in Spiking Neural Networks for Oceanogr...

引用

2024 IEEE high performance Extreme Computing conference, HPEC 2024

作者： Kane, Alissa J. Chang, Yuchou University of Massachusetts Dartmouth Computer and Information Science Department North DartmouthMA02747 United States

ISBN: (纸本)9798350387131

Spiking neural networks (SNNs), as the third-generation neural networks, can work under an energy efficient mode. SNNs are different from the second-generation neural networks which consume a lot of energy and power. SNNs are suitable for oceanographic data analysis on the edge devices underwater since the devices have constrained power supply and limited communication bandwidth in underwater environments. Although SNNs have been widely used in classification tasks, SNN-based regression tasks are studied less because SNNs are generally considered to process discrete and sequential spikes. The existing regression model based on the membrane potential of Leaky Integrate-and-Fire (LIF) neuron uses constant settings and this mechanism may not be adaptive and capable of analyzing oceanographic data which are complicated and dynamic. In this paper, we proposed three novel regression models of Adaptive Threshold Adjustment, Heterogeneous Neurons, and Nonlinear Integration to improve the existing LIF-based model. Experimental results on real oceanographic data indicate that the proposed regression models outperform the existing model through qualitative and quantitative analysis. Those SNN regression models could be implemented on edge devices within underwater environments in the future. © 2024 IEEE.

关键词： Information leakage

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Design of a high speed rat whiskers tracking and symmetry analysis system based on FPGA

Design of a high speed rat whiskers tracking and symmetry an...

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45th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Wang, Xuecheng Zhao, Ziyao Liu, Yang Zhang, Milin Zhao, Zhe Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Inst Precis Med Beijing Peoples R China Tsinghua Univ Beijing Tsinghua Changgung Hosp Dept Orthoped Beijing Peoples R China

ISBN: (纸本)9798350324471

This paper presents a high-speed rat whisker tracking and symmetry analysis system based on FPGA. The system utilizes high-speed image sensors recording rat face videos at 120 and 1000 fps. The Xilinx Ultra96 single computer board is chosen as the platform to implement the system's processing system (PS) and the programmable logic (PL) part. The PL part is responsible for high-speed image processing and whisker tracking, while the PS part analyzes the symmetry of rat face using the tracking results from the PL part. With a processing speed FoM of 118.5 fps/GHz on the Xilinx Ultra96 single computer board and 275.47 fps/GHz on a laptop with Intel Core i5-11500T@1.5GHz, the presented system achieves excellent performance. The proposed whisker detection method has a precision of 98.2% when a threshold with a 4-degree error is selected, with an average error angle of 0.98 degrees across more than 10,000 video frames. Moreover, the proposed system is capable of local video processing within millisecond delays. These results demonstrate the feasibility of developing a high-speed, accurate, and efficient whisker tracking and symmetry analysis system for rat behavior research.

关键词： Field programmable gate arrays (FPGA)

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Optimized Bidirectional Long Short-Term Memory Network for Multivariate Solar Radiation Prediction Utilizing Gold Panning Algorithm

Optimized Bidirectional Long Short-Term Memory Network for M...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Liu, Ye Zeng, Shibo Li, Runze Yuan, Yulei College of Electronic Countermeasures National University of Defense Technology Changsha China College of Science National University of Defense Technology Changsha China School of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798331504205

This study is dedicated to optimising neural networks for more accurate multivariate solar radiation prediction using the Gold Rush algorithm. In the current research context, existing methods suffer from low accuracy and poor model stability. For this reason, this paper introduced artificial intelligence algorithms, especially neural networks, with a view to improving the prediction performance. This paper collected data on solar radiation and its related influencing factors from multiple data sources, and after data processing, this text used the Gold Rush algorithm to optimise the neural network. This text selected the indicators with high correlation for calculation, used the gold rush algorithm to optimise the algorithm to find the parameters and determine the optimal parameters, and then input the obtained data into the bi-directional long and short memory network for prediction. After testing and error analysis, the neural network model optimised based on the gold rush algorithm has high accuracy and stability in solar radiation prediction. However, the model also has some drawbacks, such as high computational complexity and sensitivity to parameter adjustment. © 2025 IEEE.

关键词： Solar irradiance

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