AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets

Mi, Peng; Sun, Maoyuan; Masiane, Moeti; Cao, Yong; North, Chris

doi:10.3390/informatics3040018

Cited by 2 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of GPUs for scientific computing and visualization is not new (Mi et al 2016;Liu et al 2013;Moritz et al 2019;Mostak 2016). There are two main features that make GPUs very attractive for handling big data.…”

Section: Data Visualization With Gpusmentioning

confidence: 99%

See 1 more Smart Citation

GPU-Enabled Visual Analytics Framework for Big Transportation Datasets

Adu-Gyamfi

2019

J. Big Data Anal. Transp.

View full text Add to dashboard Cite

Transportation agencies rely on a variety of data sources for condition monitoring of their assets and making critical decisions such as infrastructure investments and project prioritization. Recent exponential increase in the volumes of these datasets has been causing significant information overload problems for data analysts; data curation process has increasingly become time consuming as legacy CPU-based systems are reaching their limits for processing and visualizing relevant trends in these massive datasets. There is a need for new tools that can consume these new datasets and provide analytics at rates resonant with the speed of human thought. The current paper proposes a new framework that allows for both multidimensional visualization and analytics to be carried seamlessly on large transportation datasets. The framework stores data in a massively parallel database and leverages the immense computational power available in graphical processing units (GPUs) to carry out data analytics and rendering on the fly via a Structured Query Language which interacts with the underlying GPU database. A front-end is designed for near-instant rendering of queried results on simple charts and maps to enable decision makers to drill down insights quickly. The framework is used to develop applications for analyzing big transportation datasets with over 100 million rows. Performance benchmarking experiments conducted showed that the methodology developed is able to provide real-time visual updates for big data in less than 100 ms. The performance of the developed framework was also compared with CPU-based visual analytics platforms such as Tableau and D3.

show abstract

Section: Data Visualization With Gpusmentioning

confidence: 99%

“…Different GPU-based architectures have been explored for large data visualization. Mi et al (2016) proposed a fullblown, GPU-centric design for exploring large time series and multidimensional datasets. In their design, both data storage and processing are handled in the GPU memory.…”

Section: Data Visualization With Gpusmentioning

confidence: 99%

GPU-Enabled Visual Analytics Framework for Big Transportation Datasets

Adu-Gyamfi

2019

J. Big Data Anal. Transp.

View full text Add to dashboard Cite

show abstract

“…GPUs were designed for video games and graphics. The remarkable advances in performance and programmability make GPUs popular for general purpose computation [33]. GPUs have been shown to produce rapid speed-ups in even straightforward implementations of the repulsive force calculations in force-directed graph layout [34,35].…”

Section: Graph Layout On the Gpumentioning

confidence: 99%

Interactive Graph Layout of a Million Nodes

Sun

Masiane

et al. 2016

Informatics

Self Cite

View full text Add to dashboard Cite

Sensemaking of large graphs, specifically those with millions of nodes, is a crucial task in many fields. Automatic graph layout algorithms, augmented with real-time human-in-the-loop interaction, can potentially support sensemaking of large graphs. However, designing interactive algorithms to achieve this is challenging. In this paper, we tackle the scalability problem of interactive layout of large graphs, and contribute a new GPU-based force-directed layout algorithm that exploits graph topology. This algorithm can interactively layout graphs with millions of nodes, and support real-time interaction to explore alternative graph layouts. Users can directly manipulate the layout of vertices in a force-directed fashion. The complexity of traditional repulsive force computation is reduced by approximating calculations based on the hierarchical structure of multi-level clustered graphs. We evaluate the algorithm performance, and demonstrate human-in-the-loop layout in two sensemaking case studies. Moreover, we summarize lessons learned for designing interactive large graph layout algorithms on the GPU.

show abstract

AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets

Cited by 2 publications

References 24 publications

GPU-Enabled Visual Analytics Framework for Big Transportation Datasets

GPU-Enabled Visual Analytics Framework for Big Transportation Datasets

Interactive Graph Layout of a Million Nodes

Contact Info

Product

Resources

About