A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization
D. Wang, P. Grosset, J. Pulido, T. M. Athawale, J. Tian, K. Zhao, Z. Lukic, A. Huebl, Z. Wang, J. Ahrens, and D. Tao
[Paper]
(Supercomputing 2024, Atlanta, USA.)
Abstract
Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost storage efficiency encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality multi-resolution data compression for both uniform and AMR simulations. Initially, to extend the usability of multi-resolution techniques, our workflow employs a compression-oriented Region of Interest (ROI) extraction method, transforming uniform data into a multi-resolution format. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to understand the potential impacts of lossy compression. Experimental evaluation demonstrates that our workflow achieves significant compression quality improvements.
A General Framework for Error-Controlled Unstructured Scientific Data Compression
Q. Gong, Z. Wang, V. Reshniak, X. Liang, J. Chen, Q. Liu, T. M. Athawale, Y. Ju, A. Rangarajan, S. Ranka, N. Podhorszki, R. Archibald, and S. Klasky
[Paper (to be posted soon)]
(IEEE eScience 2024, Osaka, Japan.)
Abstract
Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compression ratios. We present a multi-component, error-bounded compression framework designed to enhance the compression of floating-point unstructured mesh data, which is common in scientific applications. Our approach involves interpolating mesh data onto a rectilinear grid and then separately compressing the grid interpolation and the interpolation residuals. This method is general, independent of mesh types and typologies, and can be seamlessly integrated with existing lossy compressors for improved performance. We evaluated our framework across twelve variables from two synthetic datasets and two real-world simulation datasets. The results indicate that the multi-component framework consistently outperforms state-of-the-art lossy compressors on unstructured data, achieving, on average, a 2.3 − 3.5× improvement in compression ratios, with error bounds ranging from 1×10−6 to 1×10−2. We further investigate the impact of hyperparameters, such as grid spacing and error allocation, to deliver optimal compression ratios in diverse datasets.
Data-Driven Computation of Probabilistic Marching Cubes for Efficient Level-Set Uncertainty Visualization
T. M. Athawale, Z. Wang, C. R. Johnson, and D. Pugmire
[Paper] [BibTex] [Presentation]
(EuroVis 2024-Short Papers, Odense, Denmark.)
Abstract
Uncertainty visualization is an important emerging research area. Being able to visualize data uncertainty can help scientists improve trust in analysis and decision-making. However, visualizing uncertainty can add computational overhead, which can hinder the efficiency of analysis. In this paper, we propose novel data-driven techniques to reduce the computational requirements of the probabilistic marching cubes (PMC) algorithm. PMC is an uncertainty visualization technique that studies how uncertainty in data affects level-set positions. However, the algorithm relies on expensive Monte Carlo (MC) sampling for the multivariate Gaussian uncertainty model because no closed-form solution exists for the integration of multivariate Gaussian. In this work, we propose the eigenvalue decomposition and adaptive probability model techniques that reduce the amount of MC sampling in the original PMC algorithm and hence speed up the computations. Our proposed methods produce results that show negligible differences compared with the original PMC algorithm demonstrated through metrics, including root mean squared error, maximum error, and difference images. We demonstrate the performance and accuracy evaluations of our data-driven methods through experiments on synthetic and real datasets.
Performance Improvements of Poincare Analysis for Exascale Fusion Simulations
D. Pugmire, J. Y. Choi, S. Klasky, K. Moreland, E. Suchyta, T. M. Athawale, Z. Wang, C.-S. Chang, S.-H. Ku, and R. Hager
[Paper] [BibTex] [Presentation]
(VisGap 2024-The Gap Between Visualization Research and Visualization Software, Odense, Denmark.)
Abstract
Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.
FunMC²: A Filter for Uncertainty Visualization of Marching Cubes On Multicore Devices
Z. Wang*, T. M. Athawale*, K. Moreland*, J. Chen, C. R. Johnson, and D. Pugmire
* The authors with equal contribution to the paper
[Paper] [BibTex] [Source code] [Presentation slides]
(Eurographics Symposium on Parallel Graphics and Visualization (EGPGV) workshop co-held with EuroVis 2023, Leipzig, Germany.)
Abstract
Visualization is an important tool for scientists to extract understanding from complex scientific data. Scientists need to understand the uncertainty inherent in all scientific data in order to interpret the data correctly. Uncertainty visualization has been an active and growing area of research to address this challenge. Algorithms for uncertainty visualization can be expensive, and research efforts have been focused mainly on structured grid types. Further, support for uncertainty visualization in production tools is limited. In this paper, we adapt an algorithm for computing key metrics for visualizing uncertainty in Marching Cubes (MC) to muti-core devices and present the design, implementation, and evaluation for a Filter for uncertainty visualization of Marching Cubes on Multi-Core devices (FunMC²). FunMC² accelerates the uncertainty visualization of MC significantly, and it is portable across multi-core CPUs and GPUs. Evaluation results show that FunMC² based on OpenMP runs around 11× to 41× faster on multi-core CPUs than the corresponding serial version using one CPU core. FunMC² based on a single GPU is around 5× to 9× faster than FunMC² running by OpenMP. Moreover, FunMC² is flexible enough to process ensemble data with both structured and unstructured mesh types. Furthermore, we demonstrate that FunMC² can be seamlessly integrated as a plugin into ParaView, a production visualization tool for post-processing.
Advancing Comprehension of Quantum Application Outputs: A Visualization Technique
P. Senapati, T. M. Athawale, D. Pugmire, Q. Guan
[Paper] [Presentation slides]
(In ACM QCCC'2023 workshop co-located with High-Performance Distributed (HPDC) Conference, Orlando, FL, USA.)
Abstract
Noise in quantum computers presents a challenge for the users of quantum computing despite the rapid progress we have seen in the past few years in building quantum computers. Existing works have addressed the noise in quantum computers using a variety of
mitigation techniques since error correction requires a large number of qubits which is infeasible at present. One of the consequences of quantum computing noise is that users are unable to reproduce similar output from the same quantum computer at different times,
let alone from various quantum computers. In this work, we have made initial attempts to visualize quantum basis states for all the circuits that were used in quantum machine learning from various quantum computers and noise-free quantum simulators. We have opened up a pathway for further research into this field where we will be able to isolate noisy states from non-noisy states leading to efficient error mitigation. This is where our work provides an important step in the direction of efficient error mitigation. Our work also provides a ground for quantum noise visualization in the case of large numbers of qubits.
Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
M. Han, T. M. Athawale, D. Pugmire, and C. R. Johnson
[Preprint (arXiv), Supplemental material] [BibTex] [Preview video] [Presentation video] [Presentation slides] [Source code]
(IEEE VIS 2022 conference, Oklahoma City, USA.)
Abstract
Visualizing the uncertainty of ensemble simulations is challenging
due to the large size and multivariate and temporal features of ensemble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.
Uncertainty Visualization of the Marching Squares and Marching Cubes Topology Cases
T. M. Athawale, S. Sane, and C. R. Johnson
[Preprint (arXiv)] [BibTex] [Preview video] [Presentation slides] [Presentation Video]
(IEEE VIS 2021, New Orleans, LA, USA (virtual).)
Abstract
Marching squares (MS) and marching cubes (MC) are widely used algorithms for level-set visualization of scientific data. In this paper, we address the challenge of uncertainty visualization of the topology cases of the MS and MC algorithms for uncertain scalar field data sampled on a uniform grid. The visualization of the MS and MC topology cases for uncertain data is challenging due to their exponential nature and a possibility of multiple topology cases per cell of a grid. We propose the topology case count and entropy-based techniques for quantifying uncertainty in the topology cases of the MS and MC algorithms when noise in data is modeled with probability distributions. We demonstrate the applicability of our techniques for independent and correlated uncertainty assumptions. We visualize the quantified topological uncertainty via color mapping proportional to uncertainty, as well as with interactive probability queries in the MS case and entropy isosurfaces in the MC case. We demonstrate the utility of our uncertainty quantification framework in identifying the isovalues exhibiting relatively high topological uncertainty. We illustrate the effectiveness of our techniques via results on synthetic, simulation, and hixel datasets.
Visualizing Interactions Between Solar Photovoltaic Farms and the Atmospheric Boundary Layer
T. M. Athawale*, B. Stanislawski*, S. Sane, and C. R. Johnson
* Both authors contributed equally to the paper
[Preprint] [BibTex] [Presentation slides] [Presentation video (at 24:50)]
(EnergyVis '21 workshop co-located with 12th ACM International Conference on Future Energy Systems (e-Energy' 21), June 28-July 2, 2021, Torino, Italy (virtual). ACM, New York, NY, USA, 5 pages, pp 377-381)
Abstract
The efficiency of solar panels depends on the operating temperature. As the panel temperature rises, efficiency drops. Thus, the solar energy community aims to understand the factors that influence the operating temperature, which include wind speed, wind direction, turbulence, ambient temperature, mounting configuration, and solar cell material. We use high-resolution numerical simulations to model the flow and thermal behavior of idealized solar farms. Because these simulations model such complex behavior, advanced visualization techniques are needed to investigate and understand the results. Here, we present advanced 3D visualizations of numerical simulation results to illustrate the flow and heat transport in an idealized solar farm. The findings can be used to understand how flow behavior influences module temperatures, and vice versa.
Visualization of Uncertain Multivariate Data via Feature Confidence Level-Sets
S. Sane, T. M. Athawale, and C. R. Johnson
[Preprint] [Bibtex] [Preview video] [Presentation slides] [Presentation video (talk# 3)]
(EuroVis 2021-23rd EG/VGTC Conference on Visualization, Zurich, Switzerland (virtual).)
Abstract
Recent advancements in multivariate data visualization have opened new research opportunities for the visualization community. In this paper, we propose an uncertain multivariate data visualization technique called feature confidence level-sets. Conceptually, feature level-sets refer to level-sets of multivariate data. Our proposed technique extends the existing idea of univariate confidence isosurfaces to multivariate feature level-sets. Feature confidence level-sets are computed by considering the trait for a specific feature, a confidence interval, and the distribution of data at each grid point in the domain. Using uncertain multivariate data sets, we demonstrate the utility of the technique to visualize regions with uncertainty in relation to the specific trait or feature, and the ability of the technique to provide secondary feature structure visualization based on uncertainty.
A Statistical Framework for Visualization of Positional Uncertainty in Deep Brain Stimulation Electrodes
T. M. Athawale, K. A. Johnson, C. R. Butson, and C. R. Johnson
[arXiv] [Preprint] [Bibtex] [Poster] [Source code (MATLAB)]
(2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC), Vancouver, BC, Canada, 2019, pp. 54-55.)
Abstract
Deep brain stimulation (DBS) is an FDA-approved neurosurgical procedure for treating patients with movement disorders such as Parkinson's disease. Patient-specific computational modeling and visualization play a key role for efficient surgical and therapeutic decision-making relevant to DBS. The computational models analyze DBS post-operative brain imaging, e.g., computed tomography (CT), to understand the DBS electrode positions within the patient's brain. The DBS stimulation settings for optimal patient response depend upon a physician's knowledge regarding precise electrode positions. The finite resolution of brain imaging, however, restricts our understanding regarding precise DBS electrode positions. In our contribution, we study the problem of the quantification of positional uncertainty in the DBS electrodes caused by the finite resolution of post-operative imaging. We propose a Monte Carlo statistical framework, which takes the advantage of our analytical characterization of the DBS electrode geometry to understand the spatial uncertainty in DBS electrodes. Our statistical framework quantifies the uncertainty in two positional parameters of the DBS electrodes, namely, the longitudinal axis direction and the positions at sub-voxel levels. We interactively visualize quantified uncertainties by employing volume rendering and isosurfaces. We show that the spatial variations in the DBS electrode positions are significant for finite resolution imaging, and interactive visualization can be instrumental for efficient interpretation of the positional variations in the DBS lead.
FunMC²: A Filter for Uncertainty Visualization of Marching Cubes On Multicore Devices
Advancing Comprehension of Quantum Application Outputs: A Visualization Technique 
Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles
Uncertainty Visualization of the Marching Squares and Marching Cubes Topology Cases
Visualizing Interactions Between Solar Photovoltaic Farms and the Atmospheric Boundary Layer
Visualization of Uncertain Multivariate Data via Feature Confidence Level-Sets