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MSz: An Efficient parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors

ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2025年第1期31卷 130-140页

作者： Li, Yuxiao Liang, Xin Wang, Bei Qiu, Yongfeng Yan, Lin Guo, Hanqi Ohio State Univ Columbus OH 43210 USA Univ Kentucky Lexington KY USA Univ Utah Wellsville UT USA Iowa State Univ Iowa City IA USA

This research explores a novel paradigm for preserving topological segmentations in existing error-bounded lossy compressors. Today's lossy compressors rarely consider preserving topologies such as Morse-Smale complexes, and the discrepancies in topology between original and decompressed datasets could potentially result in erroneous interpretations or even incorrect scientific conclusions. In this paper, we focus on preserving Morse-Smale segmentations in 2D/3D piecewise linear scalar fields, targeting the precise reconstruction of minimum/maximum labels induced by the integral line of each vertex. The key is to derive a series of edits during compression time. These edits are applied to the decompressed data, leading to an accurate reconstruction of segmentations while keeping the error within the prescribed error bound. To this end, we develop a workflow to fi x ex trema an d in tegral lines alternatively until convergence within finite iterations. We accelerate each workflow component with shared-memory/GPU parallelism to make the performance practical for coupling with compressors. We demonstrate use cases with fluid dynamics, ocean, and cosmology application datasets with a significant acceleration with an NVIDIA A100 GPU.

关键词： Lossycompression feature-preservingcompression Morse-Smalesegmentations shared-memoryparallelism

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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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data Movement Visualized: A Unified Framework for Tracking and Visualizing data Movements in Heterogeneous Architectures 17

Data Movement Visualized: A Unified Framework for Tracking a...

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17th Pacific visualization Conference (PacificVis)

作者： Kesavan, Suraj P. Bhatia, Harsh Dasu, Keshav Pearce, Olga Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9798350393811;9798350393804

Whereas rapidly increasing heterogeneous compute capabilities continue to facilitate further scalability, modern applications often instead get limited by suboptimal data movement, as more and more data needs to be shipped across different hardware components (i.e., CPUs, GPUs, and other types of accelerators). We posit that understanding and improving data movement in modern use-cases require a holistic understanding of the underlying Hardware usage as well as the Communication patterns within the overall context of the Application, or as we call it, the HAC domain. Collecting and correlating HAC data currently requires interacting with several profiling tools and libraries, resulting in a tedious workflow that is neither scalable nor portable. Furthermore, existing tools for visualizing data movement profiles also focus on these domains individually, rather than offering a holistic view. We present a unified framework for tracking and visualizing data movement trends in large-scale applications deployed on heterogeneous architectures. Our framework has two interoperable components. (1) DMTracker is a lean software layer that provides a simple interface for configurable HAC profiling of GPU-enabled applications and abstracts away the complexity in using several profiling tools, resulting in a streamlined and time-correlated event history across the HAC domains. (2) DMVis is a web-based tool that combines several linked visualizations to offer holistic visual insights into the runtime behavior and resources utilization of applications, proving pivotal in identifying computationally expensive tasks and data transfers across devices. In this paper, we present the design and prototype of our framework, developed in consultation with domain experts and demonstrated on two case studies, including one for a large language model training. Initial impressions from the experts indicate a positive turn in their usual workflow of observing and tuning the performance through im

关键词： data Movement GPU acceleration Heterogeneous architectures Machine learning Performance Performance analysis software visualization visual analytics

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Interactive visualization of Time-Varying Flow Fields Using Particle Tracing Neural Networks 17

Interactive Visualization of Time-Varying Flow Fields Using ...

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17th Pacific visualization Conference (PacificVis)

作者： Han, Mengjiao Li, Jixian Sane, Sudhanshu Gupta, Shubham Wang, Bei Petruzza, Steve Johnson, Chris R. Univ Utah SCI Inst Salt Lake City UT 84112 USA Utah State Univ Logan UT 84322 USA

ISBN: (纸本)9798350393811;9798350393804

Lagrangian representations of flow fields have gained prominence for enabling fast, accurate analysis and exploration of time-varying flow behaviors. In this paper, we present a comprehensive evaluation to establish a robust and efficient framework for Lagrangian-based particle tracing using deep neural networks (DNNs). Han et al. (2021) first proposed a DNN-based approach to learn Lagrangian representations and demonstrated accurate particle tracing for an analytic 2D flow field. In this paper, we extend and build upon this prior work in significant ways. First, we evaluate the performance of DNN models to accurately trace particles in various settings, including 2D and 3D time-varying flow fields, flow fields from multiple applications, flow fields with varying complexity, as well as structured and unstructured input data. Second, we conduct an empirical study to inform best practices with respect to particle tracing model architectures, activation functions, and training data structures. Third, we conduct a comparative evaluation of prior techniques that employ flow maps as input for exploratory flow visualization. Specifically, we compare our extended model against its predecessor by Han et al. (2021), as well as the conventional approach that uses triangulation and Barycentric coordinate interpolation. Finally, we consider the integration and adaptation of our particle tracing model with different viewers. We provide an interactive web-based visualization interface by leveraging the efficiencies of our framework, and perform high-fidelity interactive visualization by integrating it with an OSPRay-based viewer. Overall, our experiments demonstrate that using a trained DNN model to predict new particle trajectories requires a low memory footprint and results in rapid inference. Following best practices for large 3D datasets, our deep learning approach using GPUs for inference is shown to require approximately 46 times less memory while being more than 400 times f

关键词： Flow visualization Lagrangian-based particle tracing deep learning neural networks scientific machine learning

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Proceedings - 2023 ieee 16th Pacific visualization symposium, PacificVis 2023

Proceedings - 2023 IEEE 16th Pacific Visualization Symposium...

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16th ieee Pacific visualization symposium, PacificVis 2023

ISBN: (纸本)9798350321241

The proceedings contain 26 papers. The topics discussed include: understanding people’s needs in viewing diverse social opinions about controversial topics;a study of zooming, interactive lenses and overview+detail techniques in collaborative map-based tasks;parallel assemblies plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes;an empirical guide for visualization consistency in multiple coordinated views;visualizing interaction networks and evidence in biomedical corpora;efficient raycasting of volumetric depth images for remote visualization of large volumes at high frame rates;investigating animal infectious diseases with visual analytics;interactive transformations and visual assessment of noisy event sequences: an application in en-route air traffic control;and a visual analytics inspired approach to correlate and understand multiple mechanical tensor fields.

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Reinforcement Learning for Load-Balanced parallel Particle Tracing

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2023年第6期29卷 3052-3066页

作者： Xu, Jiayi Guo, Hanqi Shen, Han-Wei Raj, Mukund Wurster, Skylar W. Peterka, Tom Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Broad Inst MIT & Harvard Stanley Ctr Psychiat Res Cambridge MA 02142 USA

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

关键词： Costs Heuristic algorithms Estimation Load modeling data models Computational modeling Adaptation models Distributed and parallel particle tracing dynamic load balancing reinforcement learning

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Proceedings - 2023 ieee 13th symposium on large data Analysis and visualization, LDAV 2023

Proceedings - 2023 IEEE 13th Symposium on Large Data Analysi...

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13th ieee symposium on large data Analysis and visualization, LDAV 2023

ISBN: (纸本)9798350330182

The proceedings contain 5 papers. The topics discussed include: sub-linear time sampling approach for large-scale data visualization using reinforcement learning;speculative progressive raycasting for memory constrained isosurface visualization of massive volumes;a distributed-memory parallel approach for volume rendering with shadows;low-cost post hoc reconstruction of HPC simulations at full resolution;and towards adaptive refinement for multivariate functional approximation of scientific data.

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Evaluation of Pointing Ray Techniques for Distant Object Referencing in Model-Free Outdoor Collaborative Augmented Reality

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2022年第11期28卷 3896-3906页

作者： Li, Yuan Tahmid, Ibrahim A. Lu, Feiyu Bowman, Doug A. Virginia Tech Ctr Human Comp Interact Blacksburg VA 24061 USA Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

Referencing objects of interest is a common requirement in many collaborative tasks. Nonetheless, accurate object referencing at a distance can be challenging due to the reduced visibility of the objects or the collaborator and limited communication medium. Augmented Reality (AR) may help address the issues by providing virtual pointing rays to the target of common interest. However, such pointing ray techniques can face critical limitations in large outdoor spaces, especially when the environment model is unavailable. In this work, we evaluated two pointing ray techniques for distant object referencing in model-free AR from the literature: the Double Ray technique enhancing visual matching between rays and targets, and the parallel Bars technique providing artificial orientation cues. Our experiment in outdoor AR involving participants as pointers and observers partially replicated results from a previous study that only evaluated observers in simulated AR. We found that while the effectiveness of the Double Ray technique is reduced with the additional workload for the pointer and human pointing errors, it is still beneficial for distant object referencing.

关键词： Augmented Reality Collaboration Ray visualization Model-Free Outdoor

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Angular-based Edge Bundled parallel Coordinates Plot for the Visual Analysis of large Ensemble Simulation data 12

Angular-based Edge Bundled Parallel Coordinates Plot for the...

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12th ieee symposium on large-Scale data Analysis and visualization (LDAV)

作者： Watanabe, Keita Sakamoto, Naohisa Nonaka, Jorji Maejima, Yasumitsu Kobe Univ Kobe Hyogo Japan RIKEN R CCS Kobe Hyogo Japan

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

With the continuous increase in the computational power and resources of modern high-performance computing (HPC) systems, large-scale ensemble simulations have become widely used in various fields of science and engineering, and especially in meteorological and climate science. It is widely known that the simulation outputs are large time-varying, multivariate, and multivalued datasets which pose a particular challenge to the visualization and analysis tasks. In this work, we focused on the widely used parallel Coordinates Plot (PCP) to analyze the interrelations between different parameters, such as variables, among the members. However, PCP may suffer from visual cluttering and drawing performance with the increase on the data size to be analyzed, that is, the number of polylines. To overcome this problem, we present an extension to the PCP by adding B ' ezier curves connecting the angular distribution plots representing the mean and variance of the inclination of the line segments between parallel axes. The proposed Angular-based parallel Coordinates Plot (APCP) is capable of presenting a simplified overview of the entire ensemble data set while maintaining the correlation information between the adjacent variables. To verify its effectiveness, we developed a visual analytics prototype system and evaluated by using a meteorological ensemble simulation output from the supercomputer Fugaku.

关键词： Human-centered computing visualization visualization techniques Human-centered computing visualization application domains Visual analytics

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A Prototype for Pipeline-Composable Task-Based visualization Algorithms 12

A Prototype for Pipeline-Composable Task-Based Visualization...

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12th ieee symposium on large-Scale data Analysis and visualization (LDAV)

作者： Petersen, Marvin Werner, Kilian Schnorr, Andrea Kuhlen, Torsten Wolfgang Garth, Christoph Rhein Westfal TH Aachen Aachen Germany Tech Univ Kaiserslautern Kaiserslautern Germany

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

For next generation platforms, the paradigm of task-based parallelism has the potential to overcome some of the accompanying challenges. This paradigm has already been applied by the visualization community to specific algorithms and problems. However, one advantage of the task-based paradigm-the interleaving of workshould lead to better utilization of resources and ultimately lower execution times if the paradigm is applied to whole pipelines. In order to investigate this potential, we build a prototype framework for composable task-based parallel visualization algorithms. With this we explore the combination of a strictly task-based approach with the addition of a pipeline layer for visualization algorithms. This additional layer eases the composition of larger task-based parallel visualization applications without the need to explicitly define the exact connection in a task graph between algorithms. In this manner, task-based visualization algorithms can be designed towards a common interface, be easily combined, and still benefit from the advantages of the task-based paradigm across algorithm boundaries, such as latency hiding. We explore the design implications of this combination and show initial results of the scalability and the impact of task interleaving on the runtime of exemplary pipelines.

关键词： task-based parallelism pipeline composition scientific visualization distributed parallel HPX

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