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Visualizing Hierarchical Performance Profiles of Parallel Codes Using CallFlow

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第4期27卷 2455-2468页

作者： Huu Tan Nguyen Bhatele, Abhinav Jain, Nikhil Kesavan, Suraj P. Bhatia, Harsh Gamblin, Todd Ma, Kwan-Liu Bremer, Peer-Timo Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA NVIDIA Inc Santa Clara CA 95050 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94551 USA

Calling context trees (CCTs) couple performance metrics with call paths, helping understand the execution and performance of parallel programs. To identify performance bottlenecks, programmers and performance analysts visually explore CCTs to form and validate hypotheses regarding degraded performance. However, due to the complexity of parallel programs, existing visual representations do not scale to applications running on a large number of processors. We present CallFlow, an interactive visual analysis tool that provides a high-level overview of CCTs together with semantic refinement operations to progressively explore CCTs. Using a flow-based metaphor, we visualize a CCT by treating execution time as a resource spent during the call chain, and demonstrate the effectiveness of our design with case studies on large-scale, production simulation codes.

关键词： data visualization Tools visualization Layout Vegetation Histograms Measurement Performance analysis software visualization visual analytics hierarchical data coordinated and multiple views

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SelfPose: 3D Egocentric Pose Estimation From a Headset Mounted Camera

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2023年第6期45卷 6794-6806页

作者： Tome, Denis Alldieck, Thiemo Peluse, Patrick Pons-Moll, Gerard Agapito, Lourdes Badino, Hernan de la Torre, Fernando UCL Dept Comp Sci London WC1E 6BT England Tech Univ Carolo Wilhelmina Braunschweig Comp Graph Lab D-38106 Braunschweig Germany Facebook Inc Facebook Real Lab Menlo Pk CA 94025 USA Max Planck Intelligent Syst Perceiving Syst D-70569 Stuttgart Germany Carnegie Mellon Univ Comp Sci Pittsburgh PA 15213 USA

We present a new solution to egocentric 3D body pose estimation from monocular images captured from a downward looking fish-eye camera installed on the rim of a head mounted virtual reality device. This unusual viewpoint leads to images with unique visual appearance, characterized by severe self-occlusions and strong perspective distortions that result in a drastic difference in resolution between lower and upper body. We propose a new encoder-decoder architecture with a novel multi-branch decoder designed specifically to account for the varying uncertainty in 2D joint locations. Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches. To tackle the severe lack of labelled training data for egocentric 3D pose estimation we also introduced a large-scale photo-realistic synthetic dataset. xR-EgoPose offers 383K frames of high quality renderings of people with diverse skin tones, body shapes and clothing, in a variety of backgrounds and lighting conditions, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of the art results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.

关键词： Three-dimensional displays Cameras Pose estimation Two dimensional displays visualization Head Training 3D human pose estimation egocentric VR/AR character animation

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Composition and Configuration Patterns in Multiple-View visualizations

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 1514-1524页

作者： Chen, Xi Zeng, Wei Lin, Yanna AI-maneea, Hayder Mahdi Roberts, Jonathan Chang, Remco Chinese Acad Sci Shenzhen Inst ofAdvanced Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Bangor Univ Bangor Gwynedd Wales Univ Basrah Basra Iraq Tufts Univ Medford MA 02155 USA

Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there;and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from ieee VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.

关键词： Multiple views design pattern quantitative analysis example-based design

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ETL Model for Pandemic data Processing

ETL Model for Pandemic Data Processing

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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Lukáš Tomaščik Norbert Ádám Nikola Geciová Branislav Madoš Peter Poprík Heidar Khorshidiyeh Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia Department of Mathematics and Theoretical Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This paper presents the development and implementation of an Extract-Transform-Load (ETL) model aimed at processing pandemic data. The model gathers data from various official sources, transforms it for easy analysis, and provides insights through graphical representations. Implemented using Python, the system automates data extraction and visualization, offering an accessible tool for understanding pandemic trends.

关键词： Analytical models Pandemics data visualization Transforms Market research data processing data models data mining Informatics Machine intelligence

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XGraphRAG: Interactive Visual analysis for Graph-based Retrieval-Augmented Generation

XGraphRAG: Interactive Visual Analysis for Graph-based Retri...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Ke Wang Bo Pan Yingchaojie Feng Yuwei Wu Jieyi Chen Minfeng Zhu Wei Chen State Key Lab of CAD&CG Zhejiang University Zhejiang University

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing large Language Model (LLM)’s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an intermediate representation to capture better structured relational knowledge in the corpus, elevating the precision and comprehensiveness of generation results. However, developers usually face challenges in analyzing the effectiveness of GraphRAG on their dataset due to GraphRAG’s complex information processing pipeline and the overwhelming amount of LLM invocations involved during graph construction and query, which limits GraphRAG interpretability and accessibility. This research proposes a visual analysis framework that helps RAG developers identify critical recalls of GraphRAG and trace these recalls through the GraphRAG pipeline. Based on this framework, we develop XGraphRAG, a prototype system incorporating a set of interactive visualizations to facilitate users’ analysis process, boosting failure cases collection and improvement opportunities identification. Our evaluation demonstrates the effectiveness and usability of our approach. Our work is open-sourced and available at https://***/Gk0Wk/XGraphRAG.

关键词： large language models Visual analytics Retrieval augmented generation Pipelines Knowledge based systems data visualization Prototypes Real-time systems Usability Optimization

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The Design and Implementation of the analysis and visualization System of Deformation Monitoring by InSAR Based on HBase

The Design and Implementation of the analysis and visualizat...

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2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2022

作者： Su, Qun Li, Dongkun Cai, Xiaoying He, Yuanrong Wen, Yingchuan School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Environmental Science and Engineering Xiamen University of Technology Xiamen China School of Cultural Industries and Tourism Xiamen University of Technology Xiamen China

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

visualization is one of the most important way for the efficient application of big data. Considering the main data characteristics of InSAR, such as large amount of data and regular batch increase of data, and deformation analysis, and the main elements of deformation analysis, such as cumulative deformation, deformation points, and related topological relationships, this paper uses the keep-maininformation thinning algorithm, preprocessing mechanism, and caching mechanism, and combines HBase, redis, and relational database(R-DB), to build a fast and efficient analysis and visualization system of deformation monitoring. It is of great reference and practical application value. © 2022 ieee.

关键词： visualization

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Bayesian-Assisted Inference from Visualized data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 989-999页

作者： Kim, Yea-Seul Kayongo, Paula Grunde-McLaughlin, Madeleine Hullman, Jessica Univ Washington Seattle WA 98195 USA Northwestern Univ Boston MA USA Univ Penn Philadelphia PA 19104 USA

A Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of information about the parameter value captured by the new observations. Extending recent work applying Bayesian models to understand and evaluate belief updating from visualizations, we show how the predictions of Bayesian inference can be used to guide more rational belief updating. We design a Bayesian inference-assisted uncertainty analogy that numerically relates uncertainty in observed data to the user's subjective uncertainty, and a posterior visualization that prescribes how a user should update their beliefs given their prior beliefs and the observed data. In a pre-registered experiment on 4,800 people, we find that when a newly observed data sample is relatively small (N=158), both techniques reliably improve people's Bayesian updating on average compared to the current best practice of visualizing uncertainty in the observed data. For large data samples (N=5208), where people's updated beliefs tend to deviate more strongly from the prescriptions of a Bayesian model, we find evidence that the effectiveness of the two forms of Bayesian assistance may depend on people's proclivity toward trusting the source of the data. We discuss how our results provide insight into individual processes of belief updating and subjective uncertainty, and how understanding these aspects of interpretation paves the way for more sophisticated interactive visualizations for analysis and communication.

关键词： Bayesian cognition Belief updating Uncer ainty visualization Adaptive visualization

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A Novel Directed Graph Convolutional Neural Network for Rolling Bearings in Fault Diagnosis

A Novel Directed Graph Convolutional Neural Network for Roll...

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2023 ieee International Conference on Unmanned Systems, ICUS 2023

作者： Gao, Shoupeng Li, Yueyang Zhao, Dong School of Electrical Engineering University of Jinan Jinan China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (纸本)9798350316308

As deep learning is broadly operationalized, fault detection and diagnosis are becoming increasingly intelligent. This has resulted in a significant increase in research efforts aimed at understanding and learning the different characteristics of data structured as graphs. However, in fault diagnosis, due to the vast and complex relationships between the data, we often overlook the important feature of the direction of relationships between data, resulting in confusion of some fault categories when performing classification. To address this problem, we first transform time series data into a directed graph structure using a weighted directed visualization graph (WDVG) method. To reduce the impact of distant nodes perceived as noise, the edges are assigned weights based on the difference between the sampling points. Secondly, in order to mine the directional features of relationships between nodes in large-scale graph structures, our proposed approach named DSGIN integrates graph isomorphism network (GIN) and GraphSAGE to extract directional features. Ultimately, we achieve the graph classification task. We validate the effectiveness of WDVG and DSGIN through actual datasets. © 2023 ieee.

关键词： Failure analysis

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Training and analysis of Hyperparameters in Neural Networks for Computer Vision Applications: A Didactic Approach 20

Training and Analysis of Hyperparameters in Neural Networks ...

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20th ieee Jubilee World symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Barreto, Saulo Cardoso Szemes, Peter Tamas Faculty of Engineering Department of Mechatronics Debrecen Hungary

ISBN: (纸本)9781665497046

Machine Learning models are known for having millions of parameters considering different applications, such as object recognition and self-driving cars. To avoid spending more time than necessary in the training stage and more importantly, to achieve a satisfactory model, the hyperparameters should be correctly defined. The main goal of the proposed project was to identify and evaluate the influence of hyperparameters in the training of neural networks for computer vision applications. Besides that, we aim to provide a didactic approach to analyze the acquired generalization capability of a model and easy visualization of the impact of different parameters during the training phase. © 2022 ieee.

关键词： Training Analytical models Computer vision Computational modeling Neural networks Machine learning data models

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On-Chip Sensors data Collection and analysis for SoC Health Management 36

On-Chip Sensors Data Collection and Analysis for SoC Health ...

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36th ieee International symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2023

作者： Shibin, Konstantin Jenihhin, Maksim Jutman, Artur Devadze, Sergei Tsertov, Anton Tallinn University of Technology Tallinn Estonia Testonica Lab Oü Tallinn Estonia

ISBN: (纸本)9798350315004

data produced by on-chip sensors in modern SoCs contains a large amount of information such as occurring faults, aging status, accumulated radiation dose, performance characteristics, environmental and other operational parameters. Such information provides insight into the overall health of a system's hardware as well as the operability of individual modules. This gives a chance to mitigate faults and avoid using faulty units, thus enabling hardware health management. Raw data from embedded sensors cannot be immediately used to perform health management tasks. In most cases, the information about occurred faults needs to be analyzed taking into account the history of the previously reported fault events and other collected statistics. For this purpose, we propose a special structure called Health Map (HM) that holds the information about functional resources, occurring faults and maps relationships between these. In addition, we propose algorithms for aggregation and classification of data received from on-chip sensors. The proposed Health Map contains detailed information on a particular system level (e.g., module, SoC, board) that can be compiled into a summary of hardware health status that in its turn enables distributed hierarchical health management by using this information at a higher level of system hierarchy, thus increasing the system's availability and effective lifetime. © 2023 ieee.

关键词： System-on-chip

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