Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user's view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Volume visualization plays a significant role in revealing important intrinsic patterns of 3D scientific datasets. However, these datasets are often large, making it challenging for interactive visualization systems to deliver a seamless user experience because of high input latency that arises from I/O bottlenecks and limited fast memory resources with high miss rates. To address this issue, we have proposed a deep learning-based prefetching method called RmdnCache, which optimizes the data flow across the memory hierarchy to reduce the input latency of large-scale volume visualization. Our approach accurately prefetches the content of the next view to fast memory using learning-based prediction while rendering the current view. The proposed deep learning architecture consists of two networks, RNN and MDN in respective spaces, which work together to predict both the location and likelihood distribution of the next view for defining an optimal prefetching range. Our method outperforms existing state-of-the-art prefetching algorithms in reducing overall input latency for visualizing real-world large-scale volumetric datasets.

The ubiquity of sensors and IoT devices has led to an explosion in data availability in modern agriculture. The large volume and heterogeneity of the data, together with the complexity of data processing requirements, pose huge obstacles for achieving the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). Current data management and analysis paradigms are to a large extent traditional, in which data collecting, curating, integration, loading, storing, sharing and analyzing still involve too much human effort and know-how. The experts, researchers and the farm operators need to understand the data and the whole process of data management pipeline to make full use of the data. The essential problem of the traditional paradigm is the lack of a layer of orchestrational intelligence which can understand, organize and coordinate the data processing utilities to maximize data management and analysis outcome. The emerging reasoning and tool mastering abilities of large language models (LLM) make it a potentially good fit to this position, which helps a shift from the traditional user-driven paradigm to AI-driven paradigm. In this paper, we propose and explore the idea of a LLM based copilot for autonomous agricultural data management and analysis. Based on our previously developed platform of Agricultural Data Management and Analytics (ADMA), we build a proof-of-concept multi-agent system called ADMA Copilot, which can understand user’s intent, makes plans for data processing pipeline and accomplishes tasks automatically, in which three agents: a LLM based controller, an input formatter and an output formatter collaborate together. Different from existing LLM based solutions, by defining a meta-program graph, our work decouples control flow and data flow to enhance the predictability of the behavior of the agents. Experiments demonstrate the intelligence, autonomy, efficacy, efficiency, extensibility, flexibility and privacy of our system. Comparison is also made between ours and existing systems to show the superiority and potential of our system.

Explanations of classifiers can provide recourse to those impacted, i.e., the ability to facilitate those impacted to understand and potentially change the classification. Contrastive explanations provide this recourse by producing an alternative input close to the original such that the label is changed to the desired, implying actions in the difference between the original and contrastive inputs. However, quantitatively evaluating contrastive explanations remains a challenging task. In particular, some state-of-the-art contrastive explanation algorithms for decisions made by deep neural networks can produce inputs that are out-of-distribution or adversarial and are thus either infeasible for a user to achieve or do not change the label according to the underlying data distribution, respectively. Past work has termed contrastive examples valid if they cross the decision boundary of the classifier. However, this definition does not encompass these failure modes and thus is not suitable to evaluate contrastive methods when used for recourse. In this paper, we define a new type of validity, called distributional validity, that checks for these failure modes. We experiment with the distributional validity of state-of-the-art contrastive explanation methods and find that the best contrastive method depends on the architecture of the classification model.

Approximate Nearest Neighbor (ANN) search has become a fundamental operation in numerous applications, including recommendation systems, computer vision, and natural language processing. The advent of Large Language Models (LLMs) arouses new interest in developing more efficient ANN algorithms, which will be the core functionality of vector databases as long-term memory of LLM. Multiple types of index structures, such as hashing-based, tree-based, and quantization-based, have been developed for ANN, and recently, graph-based algorithms have become the SOTA paradigm with the best trade-off between recall rate and query latency. However, almost all the existing graph-based index structures can only be hosted in memory due to the otherwise frequent I/O operations during searching if the graph-based index is stored on disk. The problem follows that for extremely large datasets, it is infeasible to accommodate the whole graph-based index in memory, and furthermore, it is difficult to build the whole index in memory at once. Thus, it is favorable if the graph-based index can be stored purely on disk and loaded into memory on demand during searching on the graph. There are existing efforts, such as DiskANN, which try to store graph-based index structure on disk while still keeping a compressed version of the dataset in memory to reduce disk I/O and speed up distance calculation. In this paper, we introduce LM-DiskANN, a novel dynamic graph-based ANN index that is designed specifically to be hosted on disk while keeping a low memory footprint by storing complete routing information in each node. By conducting extensive experiments on multiple benchmark datasets, we demonstrate that LM-DiskANN achieves a similar recall-latency curve while consuming much less memory compared with SOTA graph-based ANN indexes. Furthermore, its scalability and adaptability make it a promising solution for future big data applications.

Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the de facto data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.

Medical image synthesis is important in diverse healthcare applications, such as computer-aided diagnosis, medical image analysis, and educational tools. While Generative Adversarial Networks (GANs) have shown remarkable success in generating natural images, their application to medical images often falls short in faithfully capturing essential anatomical features. In this paper, we introduce a new approach that focuses on tissue-specific color encoding to enhance medical image synthesis using GANs. Our method deviates from the conventional practice of directly training GANs on gray-scale medical images. Instead, we initiate the process by generating and encoding various gray-scale representations of distinct tissues into separate color channels within composite images. These tissue-specific color images are then utilized to train a GAN model. The GAN, once trained, excels in producing high-quality synthetic images for individual tissues, and when combined, these tissue images yield final synthesized images that better portray the intricate tissue characteristics found in medical data. We have conducted an experimental study to validate the effectiveness of our approach in comparison to alternative methods with both qualitative and quantitative assessments to evaluate the quality of synthesized individual tissues and their combined final results.

Cover crops offer a range of agricultural and environmental benefits, such as reducing soil erosion, increasing carbon and enhancing water storage, increasing forage production, protecting soil nutrients, and so on. However, adoption of cover crop farming remains limited among Nebraska’s farmers. To promote awareness of the value of cover crop farming, we harness modern tools and technology and develop a new web-based tool capable of quantifying potential forage production, forage quality, and environmental benefits when planting cover crops, taking into account factors like climate, soil types, and seeding periods. Our tool incorporates the concept of data provenance to capture simulation configurations and results. This implementation can enhance data integrity and facilitate knowledge sharing within the scientific community, supporting further research and broader public benefits. The tool also includes reporting functions with visualizations illustrating distributions of potential forage, transpiration, nitrogen uptake, and more. Based on factors such as cover crop types, planting and termination dates, locations, and soil types, our tool provides valuable insights, enabling farmers to experiment with different cover crops on their land, ultimately leading to improved environmental outcomes for the broader Nebraska community.

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

Soil measurement and evaluation are crucial to various aspects of agriculture, including agricultural productivity, nutrient management, water management, and pH Regulation. Hyperspectral imaging is an advanced technique used to capture and analyze a wide range of light wavelengths (or spectral bands) across the electromagnetic spectrum. Hyperspectral imaging in soil research involves the use of this advanced imaging technique to analyze the spectral properties of soils. It allows researchers to capture detailed information about the composition, texture, and conditions of soil across a wide range of wavelengths in the electromagnetic spectrum. This in-depth spectral analysis provides valuable insights for studying soil health, nutrient content, moisture levels, and other critical parameters. However, existing hyperspectral analysis of soil relies on using imaging systems to exclusively capture information from the soil surface. This yields a two-dimensional image in which each pixel represents a spectrum vector. In this paper, we provide a new 3D hyperspectral data capturing features deep into the soil where each voxel represents a spectrum vector. For effective analysis of this type of new hyperspectral data, we develop a 3D visualization tool to not only directly visualize individual spectrum of the soil volume but also provide a way to cluster such high dimensional data leveraging a deep learning-based method through autoencoder.

We present a novel web-based framework, named Pixel-Based Edge Bundling (PBEB), for effectively and interactively visualizing large graphs. Our framework combines an image-based edge-bundling method and a parallel texture-based processing scheme, allowing us to effectively and efficiently compute edge similarities using kernel density estimation and subsequently group these edges into bundles based on their similarities. We discuss several challenges related to developing large-graph visualization on web-based platforms. To accelerate the edge bundling process and enable interactivity in web-based environments, we leverage texture-based parallel processing, a standard feature of WebGL. Our framework optimizes an end-to-end process, from bundling to rendering, enabling practical and interactive visualization of large graphs in a web-based setting. We demonstrate the superior performance of our framework by conducting comparisons with existing web-based and CUDA-based edge-bundling methods using various standard graphics cards on different devices.

Deep Neural Networks (DNNs) have performed admirably in classification tasks. However, the characterization of their classification uncertainties, required for certain applications, has been lacking. In this work, we investigate the issue by assessing DNNs’ ability to estimate conditional probabilities and propose a framework for systematic uncertainty characterization. Denoting the input sample as x and the category as y, the classification task of assigning a category y to a given input x can be reduced to the task of estimating the conditional probabilities p(y|x), as approximated by the DNN at its last layer using the softmax function. Since softmax yields a vector whose elements all fall in the interval (0, 1) and sum to 1, it suggests a probabilistic interpretation to the DNN’s outcome. Using synthetic and real-world datasets, we look into the impact of various factors, e.g., probability density f(x) and inter-categorical sparsity, on the precision of DNNs’ estimations of p(y|x), and find that the likelihood probability density and the inter-categorical sparsity have greater impacts than the prior probability to DNNs’ classification uncertainty.

Studying the growth dynamics of developing plants is of critical importance in plant sciences. The traditional methods rely on either manual measurement, which involves tedious labor work, or 2D image-based approaches, which cannot fully characterize plants in 3D. Given the advances of scanners and 3D reconstruction methods, scientists begin to pay more attention to 3D models to improve accuracy. However, existing methods mostly focus on the growth of a whole plant rather than its detailed substructures. In this paper, we have developed an end-to-end pipeline to detect the key events on both the whole plant and the specific components. Our method is achieved by building 3D models from images, segmenting individual components, and capturing traits. We implement an experiment on maizes for evaluation and successfully detect events in the process of growth.

Nowadays, floating-point temporal-spatial datasets are routinely generated from scientific observational apparatuses or computer simulations at an unprecedented pace. The sheer amount of these large volumetric datasets on the order of terabytes or petabytes consume massive resources in terms of bandwidth, storage and computational power. On the other hand, scientists, equipped with low-end post-analysis machines, often find it impossible to visualize and analyze these massive datasets with such limited resources in hand, not to mention their ultimate goal of real time analysis and visualization. To solve this discrepancy, a compact data representation has to be generated and a trade-off between resource consumption and analytical precision has to be found. There are many existing volumetric representation generating methods, almost all of which adopts some kind of hand-engineered heuristics to extract the effective portion of the datasets. However, the trade-off between resource consumption and analytical quality could not be well established due to the introduction of hand-engineered heuristics. In this paper, we present a deep learning based method that can adaptively capture the inherently complicated dynamics of temporal-spatial volumetric datasets without introducing any hand engineered features. We train an autoencoder based neural network with quantization and adaptation. Compared with existing methods, our method could learn data representation at a much lower compressed/uncompressed rate while preserving the details of original datasets. Also, our method could adapt with different data distribution and conduct compression and decompression in real time. Through extensive experiments, we show the effectiveness and efficiency of our approach over existing methods.

We explore the usage of deep convolutional neural network for clustering the time steps of a spatial-temporal scientific dataset. Our approach first takes the scientific dataset as training data and trains a deep convolutional autoencoder. A low-dimensional feature space or latent space can be extracted by inferencing the encoding part of the network. As a result, each time step is transformed into a feature descriptor that can be compared with each other in the feature space. In this way, we can cluster time steps according to their feature descriptors, and each group of time steps has a similar characterization. We demonstrate the effectiveness of our approach using a real-world simulation dataset of water contamination. Multiple variables and their combinations of this dataset are fed into our approach. The trained network enables the clustering of the time steps and facilitates scientists to examine their large spatial-temporal datasets.

In this paper, we present a unified graph clustering framework based on an asynchronous approach. We study the similarities among the Louvain algorithm and the Infomap algorithm. Based on their common features, we build an end-to-end optimized distributed framework for implementing both algorithms. By extending the existing asynchronous distributed framework for large-scale graphs traversal, we ensure both workload and communication balanced. Our extensive experiments show that our framework is correct and effective with different large real-world and synthetic datasets using up to 32,768 processors for the Louvain algorithm and 16,384 processors for the Infomap algorithm. The quality and the scalability of our framework are superior to the existing work.

In large-scale enterprise data centers for big data analytics, long batched jobs and short interactive jobs are usually mixed. Hybrid job schedulers, consisting of one centralized scheduler for long jobs and multiple distributed schedulers for short jobs, have become a promising alternative because they can significantly shorten latencies of short jobs via independent and parallelized assignment of short tasks by distributed schedulers and lower chances of head-of-line blocking via a number of performance optimization techniques.However, short jobs are still facing long job latencies under hybrid job schedulers due to workload fluctuation and straggler task problem. In this paper, we propose Eirene to optimize the latency performance of short jobs via two schemes tightly coupled into the general architecture of hybrid job schedulers. Coordinated Cold Data Migration leverages high task waiting time of short jobs under heavily-loaded periods and migrates cold data from disks to local memory for the initial phase of reading input so as to shorten task runtime and queueing time. On the other hand, Scheduler-Aware Task Cloning exploits spare computing resources under lightly-loaded periods and performs proactive task cloning for short jobs to mitigate the straggler problem.We implement a prototype of Eirene based on Eagle, a state-of-the-art hybrid job scheduler. Experimental results show that, under heavy loads, Eirene is able to improve 50-percentile (P50), 75-percentile (P75), 90-percentile (P90) latency performance of short jobs by up to 44.4%, 80.3%, 84.1% respectively compared with Eagle under the Facebook trace with a cluster of 50000 nodes.

Analysis of hyperspectral images is of great importance in many scientific disciplines. Obtaining the spectral and spatial information simultaneously from time-varying hyperspectral images is a challenging task due to their high dimensionality. In this paper, we design an interface that allows users to study hyperspectral images interactively and obtain spectral features and enhanced images at the same time. The image fusion results change dynamically with the regions of interest selected by users and convey both the spatial and spectral information. We show the usefulness of our approach using time-varying hyperspectral plant images. We compare our method with existing hyperspectral image analysis techniques. Our evaluation indicates that our interface can help users determine important bands, identify regions of interest, and generate image fusion results for time-varying hyperspectral plant images.

It is common to find a mixture of both long batch jobs and latency-sensitive short jobs in enterprise data centers. Recently hybrid job schedulers emerge as attractive alternatives of conventional centralized job schedulers.

We have leveraged STARE indexing to package partitioned data chunks from diverse datasets into netCDF files, distributed them on a cluster of 16 lightweight nodes with their placements spatiotemporally co-aligned, and demonstrated a few integrative analyses using netCDF parallel I/O and Python MPI, with single-user performance and scalability comparable to, or even better than, that of a parallel array database management system (ADBMS) such as SciDB. However, records of the node location and STARE index ranges for each data chunk, similar to the chunk maps of SciDB, must be maintained and consulted by the I/O and analysis code for coordinating the analytic operations in parallel, in order to achieve the good performance and scalability.

Eye tracking is a frequently used technique to collect data capturing users' strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers' eye tracking behaviors.

Generating 3D digital representations of plants is indispensable for researchers to gain a detailed understanding of plant dynamics. Emerging high-throughput plant phenotyping techniques can capture plant point clouds that, however, often contain imperfections and make it a changeling task to generate accurate 3D reconstructions. We present an end-to-end pipeline to reconstruct surfaces from point clouds of maize and rice plants. In particular, we propose a two-step clustering approach to accurately segment the points of each individual plant component according to maize and rice properties. We further employ surface fitting and edge fitting to ensure the smoothness of resulting surfaces. Realistic visualization results are obtained through post-processing, including texturing and lighting. Our experimental study has explored the parameter space and demonstrated the effectiveness of our pipeline for high-throughput plant phenotyping.

We present a new distributed community detection algorithm for large graphs based on the Louvain method. We exploit a distributed delegate partitioning to ensure the workload and communication balancing among processors. In addition, we design a new heuristic strategy to carefully coordinate the community constitution in a distributed environment, and ensure the convergence of the distributed clustering algorithm. Our intensive experimental study has demonstrated the scalability and the correctness of our algorithm with various large-scale real-world and synthetic graph datasets using up to 32,768 processors.

Due to hardware upgrades and server consolidation, it is not uncommon to witness a few generations of servers deployed in the same datacenters. As a result, variants of fair job schedulers are proposed to enforce fairness for constrained jobs that have hardware or software constraints on task placement. However, the other important characteristics resulted from server heterogeneity, performance variation, is unfortunately overlooked by state-of-art fair job schedulers with placement constraints. In this paper, we propose Eunomia, a performance-variation-aware fair job scheduler, to address the unfairness issue due to performance variation in heterogeneous clusters. Eunomia introduces a key metric, called progress share, which is defined as the ratio between the accumulated task progress given the current allocation and the accumulated task progress if the user can monopolize the cluster. Eunomia aims to equalize progress share of jobs as much as possible, so as to achieve the same slowdown of jobs from different users due to resource sharing and placement constraints, regardless of performance variation. Evaluation results show that Eunomia is able to deliver better share fairness compared with state-of-art schedulers without performance loss.

Community detection is essential to various graph analysis applications. Infomap is a graph clustering algorithm capable of achieving high-quality communities. However, it remains a very challenging problem to effectively apply Infomap on large graphs. By analyzing communication and workload patterns of Infomap and leveraging a distributed delegate partitioning and distribution method, we develop a new heuristic strategy to carefully coordinate the community constitution from the vertices of a graph in a distributed environment, and achieve the convergence of the distributed clustering algorithm. We have implemented our optimized algorithm using MPI (Message Passing Interface), which can be easily employed or extended to massively distributed computing systems. We analyze the correctness of our algorithm, and conduct an intensive experimental study to investigate the communication and computation cost of our distributed algorithm, which has not shown in previous work. The results demonstrate the scalability and the correctness of our distributed Infomap algorithm with large-scale real-world datasets.

A Big Earth Data platform has been constructed based on a parallel distributed database management system, SciDB, to demonstrate visual analytics with interactive animation on diverse datasets. This high-performing capability is achieved by exploiting transparent multimodal parallelization, largely enabled by a unifying indexing scheme, STARE, that provides unparalleled variety scaling. Such a platform not only supports effortless interactive data exploration and analysis but also has the potential to systemize machine learning undertakings with diverse and voluminous Earth Science data.

Medical imaging enables researchers and practitioner to uncover the characteristics of diseases (e.g., human cancer) in great detail. However, the sheer size of resulting imaging data and the high dimension of derived features become a major challenge in data analysis, diagnosis, and knowledge discovery. We present a novel visual analytics system, named iVAR, targeted at observing the comprehensive quantification of tumor phenotypes by effectively exploring a large number of quantitative image features. Our system is comprised of multiple linked views combining visualization of three-dimensional volumes and tumors reconstructed by computed tomography (CT) images, and a radiomic analysis of high-dimensional features quantifying tumor image intensity, shape and texture, and three non-image clinical features. Thus, it offers insights into the overall distribution of quantitative imaging features and also enables detailed analysis of the relationship between features. We demonstrate our system through use case scenarios on a real-world large-scale CT dataset with lung cancer.

We have devised and implemented a key technology, SpatioTemporal Adaptive-Resolution Encoding (STARE), in an array database management system, i.e. SciDB, to achieve unparalleled variety scaling for Big Earth Data, enabling rapid-response visual analytics. STARE not only serves as a unifying data representation homogenizing diverse varieties of Earth Science Datasets, but also supports spatiotemporal data placement alignment of these datasets to optimize a major class of Earth Science data analyses, i.e. those requiring spatiotemporal coincidence. Using STARE, we tailor a data partitioning and distribution strategy for the data access patterns of our scientific analysis, leading to optimal use of distributed resources. With STARE, rapid-response visual analytics are made possible through a high-level query interface, allowing geoscientists to perform data exploration visually, intuitively and interactively. We envision a system based on these innovations to relieve geoscientists of most laborious data management chores so that they may focus better on scientific issues and investigations. A significant boost in scientific productivity may thus be expected. We demonstrate these advantages with a prototypical system including comparisons to alternatives.

We investigate the impact of data placement on two Big Data technologies, Spark and SciDB, with a use case from Earth Science where data arrays are multidimensional. Simultaneously, this investigation provides an opportunity to evaluate the performance of the technologies involved. Two datastores, HDFS and Cassandra, are used with Spark for our comparison. It is found that Spark with Cassandra performs better than with HDFS, but SciDB performs better yet than Spark with either datastore. The investigation also underscores the value of having data aligned for the most common analysis scenarios in advance on a shared nothing architecture. Otherwise, repartitioning needs to be carried out on the fly, degrading overall performance.

We have implemented a flexible User Defined Operator (UDO) for labeling connected components of a binary mask expressed as an array in SciDB, a parallel distributed database management system based on the array data model. This UDO is able to process very large multidimensional arrays by exploiting SciDB's memory management mechanism that efficiently manipulates arrays whose memory requirements far exceed available physical memory. The UDO takes as primary inputs a binary mask array and a binary stencil array that specifies the connectivity of a given cell to its neighbors. The UDO returns an array of the same shape as the input mask array with each foreground cell containing the label of the component it belongs to. By default, dimensions are treated as non-periodic, but the UDO also accepts optional input parameters to specify periodicity in any of the array dimensions. The UDO requires four stages to completely label connected components. In the first stage, labels are computed for each subarray or chunk of the mask array in parallel across SciDB instances using the weighted quick union (WQU) with half-path compression algorithm. In the second stage, labels around chunk boundaries from the first stage are stored in a temporary SciDB array that is then replicated across all SciDB instances. Equivalences are resolved by again applying the WQU algorithm to these boundary labels. In the third stage, relabeling is done for each chunk using the resolved equivalences. In the fourth stage, the resolved labels, which so far are “flattened” coordinates of the original binary mask array, are renamed with sequential integers for legibility. The UDO is demonstrated on a 3-D mask of 0(10n) elements, with 0(108) foreground cells and o(106) connected components. The operator completes in 19 minutes using 84 SciDB instances.

We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. In the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. HTM is also an indexing scheme, and when applied to all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. With HTM most geospatial set operations become integer interval operations with further performance advantages.

We present a study of scientific data analytics on heterogeneous architectures using the Legion runtime system. Legion is a new programming model and runtime system targeting distributed heterogeneous architectures. It introduces logical regions as a new abstraction for describing the structures and usages of program data. We describe how to leverage logical regions to express important properties of program data, such as locality and independence, for scientific data analytics that can consist of multiple operations with different data types. Our approach can help users simplify programming on the data partition, data organization, and data movement for distributed-memory heterogeneous architectures, thereby facilitating a simultaneous execution of multiple analytics operations on modern and future supercomputers. We demonstrate the scalability and the usability of our approach by a hybrid data partitioning and distribution scheme for different data types using both CPUs and GPUs on a heterogeneous system.

We present a novel geohydrologic data visualization framework and apply the interface automata theory in support of time-varying multivariate data visualization tasks. The framework tackles heterogeneous geohydrologic data that has unique and complex data structures. The interface automata can generate a series of interactions and interfaces that are adapted to user selection and provide an intuitive method for visualizing and analyzing geohydrologic data. The interface automata can not only clearly guide user exploration, but also enhance user experience by eliminating automation surprises. In addition, our design can significantly reduce the entire system maintenance overhead, and enhance the system extendability for new datasets and data types. Our framework has been applied to a scientific geohydrologic visualization and analysis system, named INSIGHT, for the Nebraska Department of Natural Resources (NDNR). The new framework has brought many advantages that do not exist in the previous approaches, and is more efficient and extendable for visualizing geohydrologic data.

It is found that data placement on the networked nodes of a cluster based on the shared-nothing architecture (SNA) should align in the physical (i.e. spatiotemporal) space for most geoscience Big Data analysis systems in order to minimize data movements and thus achieve optimal performance and efficiency. This is due to the fact that data analysis in geosciences predominantly requires spatiotemporal coincidence. If individual datasets are considered separately in their placement on the cluster nodes, these systems often have to move data between nodes when an analysis involves two or more datasets. In this paper, we first report our discoveries from a data placement alignment experiment with two Big Data technologies, SciDB and Spark+HDFS, and then elucidate some of the far-reaching implications of this discovery.

Feature extraction and tracking is a fundamental operation used in many geoscience applications. In this paper, we present a scalable method for computing and tracking features on distributed memory machines for large-scale geospatial data. We carefully apply new communication schemes to minimize the data exchanged among the computing nodes in building and updating the global connectivity information of features. We present a theoretical complexity analysis, and show that our method can significantly reduce the communication cost compared to the traditional method.

Directly visualizing a large graph as a node-link diagram often incurs visual clutter. Edge bundling can effectively address this issue and concisely reveal the main graph structure with reduced visual clutter. Although researchers have devoted noticeable efforts to develop acceleration methods, it remains a challenging task to efficiently conduct edge bundling on devices with a limited computing capacity, such as ubiquitous smart mobile devices. We present a new method for visualizing a node-link diagram based on force-directed edge bundling. We use textures to encode the data of lines and forces, and employ shaders to conduct the iterative line refinement on GPUs. We name this method as Texture-Based Edge Bundling (TBEB) as the major steps are done using textures. We demonstrate the high performance of TBEB using standard graphics cards. TBEB makes it feasible to interactively visualize large graphs on web-based platforms.

We present a parallel hierarchical graph clustering algorithm that uses modularity as clustering criteria to effectively extract community structures in large graphs of different types. In order to process a large complex graph (whose vertex number and edge number are around 1 billion), we design our algorithm based on the Louvain method by investigating graph partitioning and distribution schemes on distributed memory architectures and conducting clustering in a divide-and-conquer manner. We study the relationship between graph structure property and clustering quality, carefully deal with ghost vertices between graph partitions, and propose a heuristic partition method suitable for the Louvain method. Compared to the existing solutions, our method can achieve nearly well-balanced workload among processors and higher accuracy of graph clustering on real-world large graph datasets.

Data staging and in-situ/in-transit data processing are emerging as attractive approaches for supporting extreme scale scientific workflows. These approaches improve end-to-end performance by enabling runtime data sharing between coupled simulations and data analytics components of the workflow. However, the complex and dynamic data exchange patterns exhibited by the workflows coupled with the varied data access behaviors make efficient data placement within the staging area challenging. In this paper, we present an adaptive data placement approach to address these challenges. Our approach adapts data placement based on application-specific dynamic data access patterns, and applies access pattern-driven and location-aware mechanisms to reduce data access costs and to support efficient data sharing between the multiple workflow components. We experimentally demonstrate the effectiveness of our approach on Titan Cray XK7 using a real combustion-analyses workflow. The evaluation results demonstrate that our approach can effectively improve data access performance and overall efficiency of coupled scientific workflows.

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. A new distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.

Studying the dynamic evolution of time-varying volumetric data is essential in countless scientific endeavors. The ability to isolate and track features of interest allows domain scientists to better manage large complex datasets both in terms of visual understanding and computational efficiency. This work presents a new trajectory-based feature tracking technique for use in joint particle/volume datasets. While traditional feature tracking approaches generally require a high temporal resolution, this method utilizes the indexed trajectories of corresponding Lagrangian particle data to efficiently track features over large jumps in time. Such a technique is especially useful for situations where the volume dataset is either temporally sparse or too large to efficiently track a feature through all intermediate timesteps. In addition, this paper presents a few other applications of this approach, such as the ability to efficiently track the internal properties of volumetric features using variables from the particle data. We demonstrate the effectiveness of this technique using real world combustion and atmospheric datasets and compare it to existing tracking methods to justify its advantages and accuracy.

Increasingly severe I/O bottlenecks on High-End Computing machines are prompting scientists to process simulation output data online while simulations are running and before storing data on disk. There are several options to place data analytics along the I/O path: on compute nodes, on separate nodes dedicated to analytics, or after data is stored on persistent storage. Since different placements have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. The FlexIO middleware described in this paper makes it easy for scientists to obtain such flexibility, by offering simple abstractions and diverse data movement methods to couple simulation with analytics. Various placement policies can be built on top of FlexIO to exploit the trade-offs in performing analytics at different levels of the I/O hierarchy. Experimental results demonstrate that FlexIO can support a variety of simulation and analytics workloads at large scale through flexible placement options, efficient data movement, and dynamic deployment of data manipulation functionalities.

As system scales and application complexity grow, managing and processing simulation data has become a significant challenge. While recent approaches based on data staging and in-situ/in-transit data processing are promising, dynamic data volumes and distributions, such as those occurring in AMR-based simulations, make the efficient use of these techniques challenging. In this paper we propose cross-layer adaptations that address these challenges and respond at runtime to dynamic data management requirements. Specifically we explore (1) adaptations of the spatial resolution at which the data is processed, (2) dynamic placement and scheduling of data processing kernels, and (3) dynamic allocation of in-transit resources. We also exploit coordinated approaches that dynamically combine these adaptations at the different layers. We evaluate the performance of our adaptive cross-layer management approach on the Intrepid IBM-BlueGene/P and Titan Cray-XK7 systems using Chombo-based AMR applications, and demonstrate its effectiveness in improving overall time-to-solution and increasing resource efficiency.

Severe I/O bottlenecks on High End Computing platforms call for running data analytics in situ. Demonstrating that there exist considerable resources in compute nodes un-used by typical high end scientific simulations, we leverage this fact by creating an agile runtime, termed GoldRush, that can harvest those otherwise wasted, idle resources to efficiently run in situ data analytics. GoldRush uses fine-grained scheduling to "steal" idle resources, in ways that minimize interference between the simulation and in situ analytics. This involves recognizing the potential causes of on-node resource contention and then using scheduling methods that prevent them. Experiments with representative science applications at large scales show that resources harvested on compute nodes can be leveraged to perform useful analytics, significantly improving resource efficiency, reducing data movement costs incurred by alternate solutions, and posing negligible impact on scientific simulations.

Abstract Geodesic grids are commonly used to model the surface of a sphere and are widely applied in numerical simulations of geoscience applications. These applications range from biodiversity, to climate change and to ocean circulation. Direct volume rendering of scalar fields defined on a geodesic grid facilitates scientists in visually understanding their large scale data. Previous solutions requiring to first transform the geodesic grid into another grid structure (e.g., hexahedral or tetrahedral grid) for using graphics hardware are not acceptable for large data, because such approaches incur significant computing and storage overhead. In this paper, we present a new method for efficient ray casting of geodesic girds by leveraging the power of Graphics Processing Units (GPUs). A geodesic grid can be directly fetched from storage or streamed from simulations to the rendering stage without the need of any intermediate grid transformation. We have designed and implemented a new analytic scheme to efficiently perform value interpolation for ray integration and gradient calculations for lighting. This scheme offers a more cost‐effective rendering solution over the existing direct rendering approach. We demonstrate the effectiveness of our rendering solution using real‐world geoscience data.

Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.

With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.

Effective 3D streamline placement and visualization play an essential role in many science and engineering disciplines. The main challenge for effective streamline visualization lies in seed placement, i.e., where to drop seeds and how many seeds should be placed. Seeding too many or too few streamlines may not reveal flow features and patterns either because it easily leads to visual clutter in rendering or it conveys little information about the flow field. Not only does the number of streamlines placed matter, their spatial relationships also play a key role in understanding the flow field. Therefore, effective flow visualization requires the streamlines to be placed in the right place and in the right amount. This paper introduces hierarchical streamline bundles, a novel approach to simplifying and visualizing 3D flow fields defined on regular grids. By placing seeds and generating streamlines according to flow saliency, we produce a set of streamlines that captures important flow features near critical points without enforcing the dense seeding condition. We group spatially neighboring and geometrically similar streamlines to construct a hierarchy from which we extract streamline bundles at different levels of detail. Streamline bundles highlight multiscale flow features and patterns through clustered yet not cluttered display. This selective visualization strategy effectively reduces visual clutter while accentuating visual foci, and therefore is able to convey the desired insight into the flow data.

Effective analysis and visualization of time-varying multivariate data is crucial for understanding complex and dynamic variable interaction and temporal evolution. Advances made in this area are mainly on query-driven visualization and correlation exploration. Solutions and techniques that investigate the important aspect of causal relationships among variables have not been sought. In this paper, we present a new approach to analyzing and visualizing time-varying multivariate volumetric and particle data sets through the study of information flow using the information-theoretic concept of transfer entropy. We employ time plot and circular graph to show information transfer for an overview of relations among all pairs of variables. To intuitively illustrate the influence relation between a pair of variables in the visualization, we modulate the color saturation and opacity for volumetric data sets and present three different visual representations, namely, ellipse, smoke, and metaball, for particle data sets. We demonstrate this information-theoretic approach and present our findings with three time-varying multivariate data sets produced from scientific simulations.

Current simulations of turbulent flames are instrumented with particles to capture the dynamic behavior of combustion in next-generation engines. Categorizing the set of many millions of particles, each of which is featured with a history of its movement positions and changing thermo-chemical states, helps understand the turbulence mechanism. We introduce a dual-space method to analyze such data, starting by clustering the time series curves in the phase space of the data, and then visualizing the corresponding trajectories of each cluster in the physical space. To cluster time series curves, we adopt a model-based clustering technique in a two-stage scheme. In the first stage, the characteristics of shape and relative position are particularly concerned in classifying the time series curves, and in the second stage, within each group of curves, clustering is further conducted based on how the curves change over time. In our work, we perform the model-based clustering in a semi-supervised manner. Users' domain knowledge is integrated through intuitive interaction tools to steer the clustering process. Our dual-space method has been used to analyze particle data in combustion simulations and can also be applied to other scientific simulations involving particle trajectory analysis work.

Abstract Most 3D vector field visualization techniques suffer from the problem of visual clutter, and it remains a challenging task to effectively convey both directional and structural information of 3D vector fields. In this paper, we present a novel visualization framework that combines the advantages of clustering methods and illustrative rendering techniques to generate a concise and informative depiction of complex flow structures. Given a 3D vector field, we first generate a number of streamlines covering the important regions based on an entropy measurement. Then we decompose the streamlines into different groups based on a categorization of vector information, wherein the streamline pattern in each group is ensured to be coherent or nearly coherent. For each group, we select a set of representative streamlines and render them in an illustrative fashion to enhance depth cues and succinctly show local flow characteristics. The results demonstrate that our approach can generate a visualization that is relatively free of visual clutter while facilitating perception of salient information of complex vector fields.

In flow visualization, field lines are often used to convey both global and local structure and movement of the flow. One challenge is to find and classify the representative field lines. Most existing solutions follow an automatic approach that generates field lines characterizing the flow and arranges these lines into a single picture. In our work, we advocate a user-centric approach to exploring 3D vector fields. Our method allows the user to sketch 2D curves for pattern matching in 2D and field lines clustering in 3D. Specifically, a 3D field line whose view-dependent 2D projection is most similar to the user drawing will be identified and utilized to extract all similar 3D field lines. Furthermore, we employ an automatic clustering method to generate field-line templates for the user to locate subfields of interest. This semi-automatic process leverages the user's knowledge about the flow field through intuitive user interaction, resulting in a promising alternative to existing flow visualization solutions. With our sketch-based interface, the user can effectively dissect the flow field and make more structured visualization for analysis or presentation.

In addition to their role as simulation engines, modern supercomputers can be harnessed for scientific visualization. Their extensive concurrency, parallel storage systems, and high-performance interconnects can mitigate the expanding size and complexity of scientific datasets and prepare for in situ visualization of these data. In ongoing research into testing parallel volume rendering on the IBM Blue Gene/P (BG/P), we measure performance of disk I/O, rendering, and compositing on large datasets, and evaluate bottlenecks with respect to system-specific I/O and communication patterns. To extend the scalability of the direct-send image compositing stage of the volume rendering algorithm, we limit the number of compositing cores when many small messages are exchanged. To improve the data-loading stage of the volume renderer, we study the I/O signatures of the algorithm in detail. The results of this research affirm that a distributed-memory computing architecture such as BG/P is a scalable platform for large visualization problems.

The ever-increasing amounts of simulation data produced by scientists demand high-end parallel visualization capability. However, image compositing, which requires interprocessor communication, is often the bottleneck stage for parallel rendering of large volume data sets. Existing image compositing solutions either incur a large number of messages exchanged among processors (such as the direct send method), or limit the number of processors that can be effectively utilized (such as the binary swap method). We introduce a new image compositing algorithm, called 2-3 swap, which combines the flexibility of the direct send method and the optimality of the binary swap method. The 2-3 swap algorithm allows an arbitrary number of processors to be used for compositing, and fully utilizes all participating processors throughout the course of the compositing. We experiment with this image compositing solution on a supercomputer with thousands of processors, and demonstrate its great flexibility as well as scalability.

The ability to identify and present the most essential aspects of time-varying data is critically important in many areas of science and engineering. This paper introduces an importance-driven approach to time-varying volume data visualization for enhancing that ability. By conducting a block-wise analysis of the data in the joint feature-temporal space, we derive an importance curve for each data block based on the formulation of conditional entropy from information theory. Each curve characterizes the local temporal behavior of the respective block, and clustering the importance curves of all the volume blocks effectively classifies the underlying data. Based on different temporal trends exhibited by importance curves and their clustering results, we suggest several interesting and effective visualization techniques to reveal the important aspects of time-varying data.

We present the design of a scalable parallel pathline construction method for visualizing large time-varying 3D vector fields. A 4D (i.e., time and the 3D spatial domain) representation of the vector field is introduced to make a timeaccurate depiction of the flow field. This representation also allows us to obtain pathlines through streamline tracing in the 4D space. Furthermore, a hierarchical representation of the 4D vector field, constructed by clustering the 4D field, makes possible interactive visualization of the flow field at different levels of abstraction. Based on this hierarchical representation, a data partitioning scheme is designed to achieve high parallel efficiency. We demonstrate the performance of parallel pathline visualization using data sets obtained from terascale flow simulations. This new capability will enable scientists to study their time-varying vector fields at the resolution and interactivity previously unavailable to them. 1.

We have developed a novel analytic capability for scientists and engineers to obtain insight from ongoing large-scale parallel unstructured mesh simulations running on thousands of processors. The breakthrough is made possible by a new approach that visualizes partial differential equation (PDE) solution data simultaneously while a parallel PDE solver executes. The solution field is pipelined directly to volume rendering, which is computed in parallel using the same processors that solve the PDE equations. Because our approach avoids the bottlenecks associated with transferring and storing large volumes of output data, it offers a promising approach to overcoming the challenges of visualization of petascale simulations. The submitted video demonstrates real-time on-the-fly monitoring, interpreting, and steering from a remote laptop computer of a 1024-processor simulation of the 1994 Northridge earthquake in Southern California.

Parallel supercomputing has traditionally focused on the inner kernel of scientific simulations: the solver. The front and back ends of the simulation pipeline - problem description and interpretation of the output - have taken a back seat to the solver when it comes to attention paid to scalability and performance, and are often relegated to offline, sequential computation. As the largest simulations move beyond the realm of the terascale and into the petascale, this decomposition in tasks and platforms becomes increasingly untenable. We propose an end-to-end approach in which all simulation components - meshing, partitioning, solver, and visualization - are tightly coupled and execute in parallel with shared data structures and no intermediate I/O. We present our implementation of this new approach in the context of octree-based finite element simulation of earthquake ground motion. Performance evaluation on up to 2048 processors demonstrates the ability of the end-to-end approach to overcome the scalability bottlenecks of the traditional approach

Vector field visualization approaches can broadly be categorized into approaches that directly visualize local or integrated flow and approaches that analyze the topological structure and visualize extracted features. Our goal was to come up with a method that falls into the first category, yet reveals structural information. We have developed a dense flow visualization method that shows the overall flow behavior while accentuating structural information without performing a topological analysis. Our method is based on a geometry-based flow integration step and a texture-based visual exploration step. The flow integration step generates a density field, which is written into a texture. The density field is generated by tracing particles under the influence of the underlying vector field. When using a quasi-random seeding strategy for initialization, the resulting density is high in attracting regions and low in repelling regions. Density is measured by the number of particles per region accumulated over time. We generate one density field using forward and one using backward propagation. The density fields are explored using texture-based rendering techniques. We generate the two output images separately and blend the results, which allows us to distinguish between inflow and outflow regions. We obtained dense flow visualizations that display the overall flow behavior, emphasize critical and separating regions, and indicate flow direction in the neighborhood of these regions. We have test our method for isolated first-order singularities and real data sets.

This paper presents a parallel visualization pipeline implemented at the Pittsburgh Supercomputing Center (PSC) for studying the largest earthquake simulation ever performed. The simulation employs 100 million hexahedral cells to model 3D seismic wave propagation of the 1994 Northridge earthquake. The time-varying dataset produced by the simulation requires terabytes of storage space. Our solution for visualizing such terascale simulations is based on a parallel adaptive rendering algorithm coupled with a new parallel I/O strategy which effectively reduces interframe delay by dedicating some processors to I/O and preprocessing tasks. In addition, a 2D vector field visualization method and a 3D enhancement technique are incorporated into the parallel visualization framework to help scientists better understand the wave propagation both on and under the ground surface. Our test results on the HP/Compaq AlphaServer operated at the PSC show that we can completely remove the I/O bottlenecks commonly present in time-varying data visualization. The high-performance visualization solution we provide to the scientists allows them to explore their data in the temporal, spatial, and variable domains at high resolution. The new high-resolution explorability, likely not available to most computational science groups, will help lead to many new insights.