Qi Wu

Qi Wu

Applied Research Scientist at NVIDIA

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

publications

Standardized Data-Parallel Rendering Using ANARI

Ingo Wald, Stefan Zellmann, Jefferson Amstutz, Qi Wu, Kevin Griffin, and Milan Jaros

Published in @LDAV, 2024

Abstract

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.

From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization
of Tera-Scale Data

Daniel Zavorotny, Qi Wu, David Bauer, and Kwan-Liu Ma

Published in @EGPGV, 2025

Abstract

Machine learning has enabled the use of implicit neural representations (INRs) to efficiently compress and reconstruct massive scientific datasets. However, despite advances in fast INR rendering algorithms, INR-based rendering remains computationally expensive, as computing data values from an INR is significantly slower than reading them from GPU memory. This bottleneck currently restricts interactive INR visualization to professional workstations. To address this challenge, we introduce an INR rendering framework accelerated by a scalable, multi-resolution GPU cache capable of efficiently representing tera-scale datasets. By minimizing redundant data queries and prioritizing novel volume regions, our method reduces the number of INR computations per frame, achieving an average 5x speedup over the state-of-the-art INR rendering method while still maintaining high visualization quality. Coupled with existing hardware-accelerated INR compressors, our framework enables scientists to generate and compress massive datasets in situ on high-performance computing platforms and then interactively explore them on consumer-grade hardware post hoc.

HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization

Hamid Gadirov, Qi Wu, David Bauer, Kwan-Liu Ma, Jos B.T.M. Roerdink, and Steffen Frey

Published in @EuroVis, 2025

Abstract

We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT’s significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.

Glyph-Based Multiscale Visualization of Turbulent Multi-Physics Statistics

Arisa Cowe, Tyson Neuroth, Qi Wu, Martin Rieth, Jacqueline Chen, Myoungkyu Lee, and Kwan-Liu Ma

Published in @arxiv, 2025

Abstract

We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT’s significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.