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检索条件"任意字段=14th Symposium on Large Data Analysis and Visualization"

共 540 条记录，以下是1-10 订阅

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Proceedings - 2024 IEEE 14th symposium on large data analysis and visualization, LDAV 2024

Proceedings - 2024 IEEE 14th Symposium on Large Data Analysi...

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14th IEEE symposium on large data analysis and visualization, LDAV 2024

ISBN: (纸本)9798331516925

the proceedings contain 12 papers. the topics discussed include: efficient analysis and visualization of high-resolution computed tomography data for the exploration of enclosed cuneiform tablets;visuals on the house: optimizing HPC workflows with no-cost CPU visualization;adaptive multi-resolution encoding for interactive large-scale volume visualization through functional approximation;web-based visualization and analytics of petascale data: equity as a tide that lifts all boats;graphical representation through a user interface for in situ scientific visualization with ascent;and distributed path compression for piecewise linear Morse-Smale segmentations and connected components.

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Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components 14

Distributed Path Compression for Piecewise Linear Morse-Smal...

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14th symposium on large data analysis and visualization

作者： Will, Michael Lukasczyk, Jonas Tierny, Julien Garth, Christoph RPTU Kaiserslautern Landau Kaiserslautern Germany CNRS Paris France Sorbonne Univ Paris France

ISBN: (纸本)9798331516932;9798331516925

this paper describes the adaptation to a distributed computational setting of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression. Additionally, we extend the algorithm to efficiently compute connected components in distributed structured and unstructured grids, based either on the connectivity of the underlying mesh or a feature mask. Our implementation is seamlessly integrated with the distributed extension of the Topology ToolKit (TTK), ensuring robust performance and scalability. To demonstrate the practicality and efficiency of our algorithms, we conducted a series of scaling experiments on large-scale datasets, with sizes of up to 40963 vertices on up to 64 nodes and 768 cores.

关键词： Distributed algorithms Scientific visualization

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Standardized data-Parallel Rendering Using ANARI 14

Standardized Data-Parallel Rendering Using ANARI

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14th symposium on large data analysis and visualization

作者： Wald, Ingo Zellmann, Stefan Amstutz, Jefferson Wu, Qi Griffin, Kevin Jaros, Milan Wesner, Stefan NVIDIA Santa Clara CA 95051 USA Univ Cologne Cologne Germany Univ Calif Davis Davis CA USA VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic

ISBN: (纸本)9798331516932;9798331516925

We propose and discuss a paradigm that allows for expressing data-parallel rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel rendering, describe two different implementations of this paradigm, and use multiple sample integrations into existing applications to show how easy it is to adopt, and what can be gained from doing so.

关键词： anari distributed rendering ray tracing sci-vis

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A Customized Validator Recommender System for PoS Networks Using Similarity-Based Circular visualization 14

A Customized Validator Recommender System for PoS Networks U...

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14th symposium on large data analysis and visualization

作者： Lee, Jaeuk Kim, Jisu Han, Hyunwoo Lee, Kyungwon Ajou Univ Dept Digital Media Suwon South Korea Ajou Univ Dept Business Intelligence Suwon South Korea Stamper Co Ltd Seoul South Korea

ISBN: (纸本)9798331516932;9798331516925

this study analyzes the impact of validator behavior on investor rewards in proof-of-stake (PoS) based blockchain networks and proposes a visualization system to assist investors in selecting appropriate validators. this system enables personalized evaluations through five adjustable indicators tailored to the investor's preferences. By utilizing similarity-based circular visualizations and radar charts, it facilitates the selection and comparison of validators. Additionally, it provides time-series data-based line graphs and raw data-based table views to support detailed comparative analysis among validators. the introduction of such a multifaceted evaluation methodology in staking investments is expected to contribute to the formation of user-customized portfolios and the establishment of optimized investment strategies.

关键词： Circular visualization Blockchain Networks Similarity-Based analysis Customized Recommendations

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Graphical Representation through a User Interface for In Situ Scientific visualization with Ascent 14

Graphical Representation through a User Interface for In Sit...

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14th symposium on large data analysis and visualization

作者： Heinemann, Colleen Amstutz, Jefferson Insley, Joseph A. Mateevitsi, Victor A. Papka, Michael E. Rizzi, Silvio Univ Illinois Urbana IL 61801 USA Argonne Natl Lab Argonne IL 60439 USA NVIDIA Santa Clara CA USA Univ Illinois Chicago IL USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (纸本)9798331516932;9798331516925

this work presents a graphical user interface (GUI) designed to allow users to interactively create scientific visualizations and workflows with the in situ visualization software, Ascent [5]. Traditionally, in situ pipelines are configured through a YAML file, which can be cumbersome. Our GUI offers an alternative by automating the conversion of user inputs into a format that Ascent can process, eliminating the need for manual file editing. this interactive capability increases the potential for a more diverse group of users to leverage Ascent's powerful visualization tools.

关键词： In situ visualization graphical user interface (GUI) HPC simulations scientific visualization Ascent

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Adaptive Multi-Resolution Encoding for Interactive large-Scale Volume visualization through Functional Approximation 14

Adaptive Multi-Resolution Encoding for Interactive Large-Sca...

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14th symposium on large data analysis and visualization

作者： Sun, Jianxin Lenz, David Yu, Hongfeng Peterka, Tom Univ Nebraska Lincoln NE 68583 USA Argonne Natl Lab Argonne IL USA

ISBN: (纸本)9798331516932;9798331516925

Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Volume visualization directly rendered from functional approximation generates high-quality rendering results without high-order artifacts caused by trilinear interpolations. However, querying an encoded functional approximation is computationally expensive, especially when the input dataset is large, making functional approximation impractical for interactive visualization. In this paper, we proposed a novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query. We also design a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness. Our method can not only dramatically decrease the caching time, one of the main contributors to input latency, but also effectively improve the cache hit rate through prefetching. Our approach significantly outperforms the traditional function approximation method in terms of input latency while maintaining comparable rendering quality.

关键词： Functional approximation large-scale data Multi-resolution Volume visualization

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Exploring large-Scale Scientific data in Virtual Reality 14

Exploring Large-Scale Scientific Data in Virtual Reality

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14th symposium on large data analysis and visualization

作者： Adeniji, Idunnuoluwa A. Insley, Joseph A. Joiner, David Mateevitsi, Victor A. Papka, Michael E. Rizzi, Silvio Argonne Natl Lab Argonne IL 60439 USA Kean Univ Union NJ 07083 USA Northern Illinois Univ De Kalb IL 60115 USA Univ Illinois Chicago IL USA

ISBN: (纸本)9798331516932;9798331516925

the conventional method of data exploration primarily relies on 2D and 3D visualization tools. However, with the advent of lower-cost virtual reality (VR) hardware, a transformation is underway. this study presents a novel data pipeline developed from ParaView (Open source Scientific visualization software) to the Unity Game Engine (Cross platform used to develop interactive contents such as games, animations and in this case VR) to investigate this transition. Specifically, we apply VR technology to the exploration of particle-based scientific datasets, focusing on data generated by a Hardware/Hybrid Accelerated Cosmology Code (HACC) simulation. this project applies VR to the identification of patterns and clusters within HACC particle-based datasets. We enable effective user interaction by integrating VR into the broader field of large data exploration, which includes features like data interaction, manipulation, and in-depth analysis. We implement custom interactions to enable interrogation of underlying data streams to provide deeper insight.

关键词： data assimilation

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Out-of-Core Dimensionality Reduction for large data via Out-of-Sample Extensions 14

Out-of-Core Dimensionality Reduction for Large Data via Out-...

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14th symposium on large data analysis and visualization

作者： Reichmann, Luca Haegele, David Weiskopf, Daniel Univ Stuttgart Stuttgart Germany

ISBN: (纸本)9798331516932;9798331516925

Dimensionality reduction (DR) is a well-established approach for the visualization of high-dimensional data sets. While DR methods are often applied to typical DR benchmark data sets in the literature, they might suffer from high runtime complexity and memory requirements, making them unsuitable for large data visualization especially in environments outside of high-performance computing. To perform DR on large data sets, we propose the use of out-of-sample extensions. Such extensions allow inserting new data into existing projections, which we leverage to iteratively project data into a reference projection that consists only of a small manageable subset. this process makes it possible to perform DR out-of-core on large data, which would otherwise not be possible due to memory and runtime limitations. For metric multidimensional scaling (MDS), we contribute an implementation with out-of-sample projection capability since typical software libraries do not support it. We provide an evaluation of the projection quality of five common DR algorithms (MDS, PCA, t-SNE, UMAP, and autoencoders) using quality metrics from the literature and analyze the trade-off between the size of the reference set and projection quality. the runtime behavior of the algorithms is also quantified with respect to reference set size, out-of-sample batch size, and dimensionality of the data sets. Furthermore, we compare the out-of-sample approach to other recently introduced DR methods, such as PaCMAP and TriMAP, which claim to handle larger data sets than traditional approaches. To showcase the usefulness of DR on this large scale, we contribute a use case where we analyze ensembles of streamlines amounting to one billion projected instances.

关键词： Dimensionality reduction out-of-core out-of-sample evaluation

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Visuals on the House: Optimizing HPC Workflows with No-Cost CPU visualization 14

Visuals on the House: Optimizing HPC Workflows with No-Cost ...

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14th symposium on large data analysis and visualization

作者： Mateevitsi, Victor A. Sewell, Andres Bode, Mathis Fischer, Paul Goebbert, Jens Henrik Insley, Joseph A. Kavroulakis, Ioannis Konioris, Damaskinos Lan, Yu-Hsiang Min, Misun Papageorgiou, Dimitrios Papka, Michael E. Petruzza, Steve Rizzi, Silvio Tomboulides, Ananias Argonne Natl Lab Argonne IL 60439 USA Utah State Univ Logan UT 84322 USA Forschungszentrum Julich Julich Germany Aristotle Univ Thessaloniki Thessaloniki Greece Univ Illinois Chicago IL 60680 USA Northern Illinois Univ De Kalb IL 60115 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9798331516932;9798331516925

the rise of heterogeneous resources in modern High Performance Computing (HPC) systems has propelled the scientific community beyond the exascale threshold. To maximize simulation performance on HPCs, applications increasingly rely on device resources, such as GPUs, leading to under-utilization of host resources, particularly CPUs. In situ analysis and visualization techniques minimize data movement by operating on data in-memory, but this still involves blocking operations that incur a small penalty on simulation performance. We explore a novel instrumentation approach where GPU-based timestep data is copied from device memory to host memory, enabling CPUs to concurrently perform visualization and analysis tasks. this strategy allows simulations to continue uninterrupted by an in situ library's analysis and visualization processes.

关键词： High Performance Computing (HPC) In Situ visualization Computational Fluid Dynamics (CFD) GPU Computing Parallel Processing

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Efficient analysis and visualization of High-Resolution Computed Tomography data for the Exploration of Enclosed Cuneiform Tablets 14

Efficient Analysis and Visualization of High-Resolution Comp...

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14th symposium on large data analysis and visualization

作者： Olbrich, Stephan Beckert, Andreas Michel, Cecile Schroer, Christian Ehteram, Samaneh Schropp, Andreas Paetzold, Philipp Univ Hamburg Hamburg Germany Ctr Natl Rech Sci CNRS ArScAn Nanterre France Deutsch Elektronen Synchrotron DESY Hamburg Germany

ISBN: (纸本)9798331516932;9798331516925

Cuneiform is the earliest known system of writing, first developed for the Sumerian language of southern Mesopotamia in the second half of the 4th millennium BC. Cuneiform signs are obtained by impressing a stylus on fresh clay tablets. For certain purposes, e.g. authentication by seal imprint, some cuneiform tablets were enclosed in clay envelopes, which cannot be opened without destroying them. the aim of our interdisciplinary project is the non-invasive study of clay tablets. A portable X-ray micro-CT scanner is developed to acquire density data of such artifacts on a high-resolution, regular 3D grid at collection sites. the resulting volume data is processed through feature-preserving denoising, extraction of high-accuracy surfaces using a manifold dual marching cubes algorithm and extraction of local features by enhanced curvature rendering and ambient occlusion. For the non-invasive study of cuneiform inscriptions, the tablet is virtually separated from its envelope by curvature-based segmentation. the computational- and data-intensive algorithms are optimized for near-real-time offline usage with limited resources at collection sites. To visualize the complexity-reduced and octree-based compressed representation of surfaces, we develop and implement an interactive application. To facilitate the analysis of such clay tablets, we implement shape-based feature extraction algorithms to enhance cuneiform recognition. Our workflow supports innovative 3D display and interaction techniques such as autostereoscopic displays and gesture control.

关键词： Computed tomography Parallel data analysis Scientific visualization 3D presentation and interaction Application case study

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