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检索条件"任意字段=Proceedings of the IEEE 2001 symposium on parallel and large-data visualization and graphics"

共 388 条记录，以下是1-10 订阅

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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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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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Hybrid Image-/data-parallel Rendering Using Island parallelism 12

Hybrid Image-/Data-Parallel Rendering Using Island Paralleli...

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

作者： Zellmann, Stefan Wald, Ingo Barbosa, Joao Dermici, Serkan Sahistan, Alper Gudukbay, Ugur Univ Cologne Bonn Rhein Sieg Univ Appl Sci Cologne Germany NVIDIA Santa Clara CA USA INESC TEC Porto Portugal Univ Minho Braga Portugal Bilkent Univ Dept Comp Engn Ankara Turkey

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

In parallel ray tracing, techniques fall into one of two camps: imageparallel techniques aim at increasing frame rate by replicating scene data across nodes and splitting the rendering work across different ranks, and data-parallel techniques aim at increasing the size of the model that can be rendered by splitting the model across multiple ranks, but typically cannot scale much in frame rate. We propose and evaluate a hybrid approach that combines the advantages of both by splitting a set of N x M ranks into M islands of N ranks each and using data-parallel rendering within each island and image parallelism across islands. We discuss the integration of this concept into four wildly different parallel renderers and evaluate the efficacy of this approach based on multiple different data sets.

关键词： Scalability Pipelines data visualization graphics processing units Switches parallel processing Ray tracing

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proceedings - 2022 ieee 12th symposium on large data Analysis and visualization, LDAV 2022

Proceedings - 2022 IEEE 12th Symposium on Large Data Analysi...

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12th ieee symposium on large data Analysis and visualization, LDAV 2022

ISBN: (纸本)9781665491563

The proceedings contain 12 papers. The topics discussed include: high-quality progressive alignment of large 3D microscopy data;a prototype for pipeline-composable task-based visualization algorithms;topological analysis of ensembles of hydrodynamic turbulent flows an experimental study;distributed hierarchical contour trees;angular-based edge bundled parallel coordinates plot for the visual analysis of large ensemble simulation data;hybrid image-/data-parallel rendering using island parallelism;new triggers for automatic camera placement over time;massive data visualization techniques for use in virtual reality devices;exploration tool for effectively interpreting the visual metaphor process of sentiment visualization;toward bi-directional in situ visualization and analysis of blood flow simulations with dynamic deforming walls;and distributed volumetric neural representation for in situ visualization and analysis.

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Standardized data-parallel Rendering Using ANARI

Standardized Data-Parallel Rendering Using ANARI

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Ingo Wald Stefan Zellmann Jefferson Amstutz Qi Wu Kevin Griffin Milan Jaros Stefan Wesner NVIDIA University of Cologne Davis University of California IT4Innovations VSB Technical University of Ostrava

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

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： data analysis Prototypes data visualization Rendering (computer graphics) Standards

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GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications 11

GPU-based Image Compression for Efficient Compositing in Dis...

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ieee 11th symposium on large data Analysis and visualization (LDAV)

作者： Lipinksi, Riley Moreland, Kenneth Papka, Michael E. Marrinan, Thomas Univ St Thomas St Paul MN 55105 USA Oak Ridge Natl Lab Oak Ridge TN USA Argonne Natl Lab Argonne IL 60439 USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (纸本)9781665432832

visualizations of large-scale data sets are often created on graphics clusters that distribute the rendering task amongst many processes. When using real-time GPU-based graphics algorithms, the most time-consuming aspect of distributed rendering is typically the compositing phase - combining all partial images from each rendering process into the final visualization. Compositing requires image data to be copied off the GPU and sent over a network to other processes. While compression has been utilized in existing distributed rendering compositors to reduce the data being sent over the network, this compression tends to occur after the raw images are transferred from the GPU to main memory. In this paper, we present work that leverages OpenGL / CUDA interoperability to compress raw images on the GPU prior to transferring the data to main memory. This approach can significantly reduce the device-to-host data transfer time, thus enabling more efficient compositing of images generated by distributed rendering applications.

关键词： Computing methodologies Computer graphics Image compression Computing methodologies parallel computing methodologies parallel algorithms Massively parallel algorithms

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VITALflow: Visual Interactive Traffic Analysis with NetFlow

VITALflow: Visual Interactive Traffic Analysis with NetFlow

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ieee/IFIP Network Operations and Management symposium

作者： Tremel, Tina Koegel, Jochen Jauernig, Florian Meier, Sebastian Thom, Dennis Becker, Franziska Mueller, Christoph Koch, Steffen IsarNet Software Solut GmbH Munich Germany Univ Stuttgart Stuttgart Germany

ISBN: (纸本)9781665406017

Traffic analysis in large enterprise networks has become a vital task for network experts, as understanding application and user traffic is the basis for proper network management with respect to planning, formulating intents, or analyzing causes for implausible behavior. In such networks, NetFlow provides input to network monitoring systems that typically show time series visualizations along different data dimensions. We studied tasks and requirements of network experts and derived a visual analytics approach that improves their analytic workflow as it enables for exploration of large time spans quickly in a multidimensional manner. Our approach guides users and improves the scalability of analyses through a novel combination of a clustered time series view and filtering in interactive parallel sets into a coherent visual analysis framework. Clustering reveals typical patterns and deviations from the daily norm and serves as entry point to exploring, filtering and comparing multiple dimensions in the parallel sets view. In addition, we briefly discuss the feedback we received on two case studies with network experts.

关键词： Filtering Visual analytics Scalability Time series analysis data visualization Planning Behavioral sciences

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Adaptive Spatially Aware I/O for Multiresolution Particle data Layouts 35

Adaptive Spatially Aware I/O for Multiresolution Particle Da...

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35th ieee International parallel and Distributed Processing symposium (IPDPS)

作者： Usher, Will Huang, Xuan Petruzza, Steve Kumar, Sidharth Slattery, Stuart R. Reeve, Sam T. Wang, Feng Johnson, Chris R. Pascucci, Valerio Univ Utah SCI Inst Salt Lake City UT 84112 USA Utah State Univ Logan UT 84322 USA Univ Alabama Birmingham Birmingham AL USA Oak Ridge Natl Lab Oak Ridge TN USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781665440660

large-scale simulations on nonuniform particle distributions that evolve over time are widely used in cosmology, molecular dynamics, and engineering. Such data are often saved in an unstructured format that neither preserves spatial locality nor provides metadata for accelerating spatial or attribute subset queries, leading to poor performance of visualization tasks. Furthermore, the parallel I/O strategy used typically writes a file per process or a single shared file, neither of which is portable or scalable across different HPC systems. We present a portable technique for scalable, spatially aware adaptive aggregation that preserves spatial locality in the output. We evaluate our approach on two supercomputers, Stampede2 and Summit, and demonstrate that it outperforms prior approaches at scale, achieving up to 2.5v faster writes and reads for nonuniform distributions. Furthermore, the layout written by our method is directly suitable for visual analytics, supporting low-latency reads and attribute-based filtering with little overhead.

关键词： parallel I/O Load Balancing

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Hypergraph data analysis with PAOHVis 29

Hypergraph data analysis with PAOHVis

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29th Italian symposium on Advanced database Systems, SEBD 2021

作者： Buono, Paolo Ceriani, Miguel Costabile, Maria University of Bari via Orabona 4 Bari70125 Italy

Many data analysis activities exploit graphs to model complex relationships among data. Very often such relationships are better described using hypergraphs, whose edges can connect more than two nodes. Hypergraphs are suitable to model networks of business partners or co-authorship networks with multiple authors per article. This paper is about a technique that visualizes dynamic hypergraphs called PAOH (parallel Aggregated ordered Hypergraph). It represents vertices as parallel horizontal bars and hyperedges as parallel vertical lines and shows the evolution of hypergraphs representing them in discrete Time Slots. PAOH is described in details in an article published in 2021 in ieee Transaction on visualization and Computer graphics. It has been applied in several domains, such as ego-networks, co-authorship, as well as digital humanities, it is well-suited for medium-sized hypergraphs (50-500 vertices). PAOHVis is a tool, available online, that implements this technique. In this paper we briefly describe the tool and show the application of PAOH to the soccer domain, to support the analysis of players’ contributions to the goals of a team. © 2021 Copyright for this paper by its authors.

关键词： data visualization

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AutoMP: A Tool to Automate Performance Testing for Model Placement on GPUs

AutoMP: A Tool to Automate Performance Testing for Model Pla...

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International symposium on parallel and Distributed Processing with Applications, ISPA

作者： Zhe Yang Wei He Fenglong Cai Yonghui Xu Wei Guo Lizhen Cui School of Software Shandong University Jinan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

As AI applications are widely deployed in various fields, the computing cost of AI is skyrocketing. The high cost not only puts pressure on the environment but also poses challenges for researchers entering the field of deep learning. Green AI is gradually gaining attention, aiming to reduce computing costs and make AI application deployment more efficient and environmentally friendly. However, due to the wide variety of deep learning models, there are many challenges in efficiently deploying multiple models on GPUs. An unreasonable model deployment strategy will lead to insufficient utilization of GPU computing resources. We address some of these challenges through AutoMP, a tool that automates performance testing for model placement on GPUs. Through AutoMP, researchers can flexibly initiate a large number of experiments to study which models are suitable for inference tasks on the same GPU, thereby making GPU utilization more efficient. AutoMP provides a user-friendly visual interface and an API to meet users’ needs in different scenarios. AutoMP also provides a complete experimental analysis tool that generates visual charts of experimental data from multiple dimensions and gives experimental conclusions to assist researchers in making decisions. To date, AutoMP has been used by a large number of users for empirical research on deep learning model placement. The cumulative number of experiments has exceeded 10,000. The flexibility and extensibility of AutoMP and our own experience using it show that this tool plays a vital role in promoting Green AI.

关键词： Deep learning visualization Distributed processing Costs Computational modeling Green products graphics processing units Extensibility Artificial intelligence Testing

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