2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)最新文献

{"title":"LDAV 2018 Copyright Page","authors":"","doi":"10.1109/ldav.2018.8739216","DOIUrl":"https://doi.org/10.1109/ldav.2018.8739216","url":null,"abstract":"","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123767846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Binary-Swap Algorithms for Odd Factors of Processes","authors":"K. Moreland","doi":"10.1109/LDAV.2018.8739210","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739210","url":null,"abstract":"A key component of most large-scale rendering systems is a parallel image compositing algorithm, and the most commonly used compositing algorithms are binary swap and its variants. Although shown to be very efficient, one of the classic limitations of binary swap is that it only works on a number of processes that is a perfect power of 2. Multiple variations of binary swap have been independently introduced to overcome this limitation and handle process counts that have factors that are not 2. To date, few of these approaches have been directly compared against each other, making it unclear which approach is best. This paper presents a fresh implementation of each of these methods using a common software framework to make them directly comparable. These methods to run binary swap with odd factors are directly compared. The results show that some simple compositing approaches work as well or better than more complex algorithms that are more difficult to implement.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125495819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VOIDGA: A View-Approximation Oriented Image Database Generation Approach","authors":"Jonas Lukasczyk, E. Kinner, J. Ahrens, H. Leitte, C. Garth","doi":"10.1109/LDAV.2018.8739204","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739204","url":null,"abstract":"Figure 1.Comparison between the generated views (left and right) and the ground truth (middle) for the asteroid impact dataset yA31 at cycle time 29945. The orange and blue surfaces are contours of the temperature (0:2 eV) and water density field (0:002 g/cm3), respectively. The views have been approximated using only 24 depth images with a resolution of 5122 pixels, whereas the original triangulation has around 3 million triangles. Our approach first computes the position of each depth image pixel in world coordinates, and then renders the resulting geometry either via a point cloud (left) or a surface triangulation (right). The used depth images have been chosen with VOIDGA to bound the maximum approximation error for the current view.In this work, we propose a novel view-approximation oriented image database generation approach (VOIDGA) that enables the adequate generation of arbitrary views. Our approach utilizes Depth Image Based Rendering (DIBR) techniques to derive novel views based on a set of depth images. In contrast to approaches that store a huge amount of images to cover a wide range of possible view directions, VOIDGA identifies and stores only those images that significantly contribute to the overall view-approximation quality while bounding the resulting approximation error. This further reduces the size of image databases and the number of images that need to be processed by DIBR algorithms. We demonstrate VOIDGA on several challenging real-world examples, and compare our approximations against ground truth renderings using two image-based metrics.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123376976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Visualization","authors":"G. Abram, P. Navrátil, Pascal Grossett, D. Rogers, J. Ahrens","doi":"10.1109/LDAV.2018.8739241","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739241","url":null,"abstract":"Figure 1.Datasets rendered by Galaxy using 64-ray cross-node ambient occlusion shadow sampling: (left) volumetric asteroid impact simulation; (center) geometric limestone karst core sample scan; (right) n-body Cosmic Web dark matter simulation. The long-range ambient-occlusion effects in Asteroid and Cosmic Web cannot be performed by conventional sort-last distributed ray tracers, where rays must stop at local data boundaries.We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121132150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual Analysis of Simulation Uncertainty Using Cost-Effective Sampling","authors":"A. Preston, Yiran Li, F. Sauer, K. Ma","doi":"10.1109/LDAV.2018.8739182","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739182","url":null,"abstract":"Studying large, complex simulations entails understanding their uncertainties. However, visualization tools that rapidly quantify simulation uncertainty may require precise tuning, give limited information, or struggle to disentangle uncertainty sources. We propose a fast, scalable regression-based approach that uses bootstrapping on small samples of simulation data to model the effect of uncertainty from discreteness. We test the approach on three types of simulations with unique sources of uncertainty: particles (dark matter), ensembles (ocean), and discretized flows (traffic). We create a visualization tool to facilitate this modeling, showing training data and predictions in real time. Scientists, who need to provide only modest supervision, can use our tool to quickly understand how initial conditions and parameterizations affect observable quantities, their uncertainties, and their agreement with experimental data. We show that our tool offers a speedup of several orders of magnitude over comparable uncertainty calculation approaches.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116835505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Citation Network Visualization of Reference Papers Based on Influence Groups","authors":"Gyeongcheol Choi, Suhyun Lim, Taerin Yoon, Kyungwon Lee","doi":"10.1109/LDAV.2018.8739176","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739176","url":null,"abstract":"Figure1:Citation Network Visualization of Reference Papers based on Influence Groups. In the circular main view (a), the research paper published according to the year can be identified by the node (b), and the size of the node represents the influence of the paper. The red node (c) is the bridge research paper that links the paper to the paper. If you click the node, you can check the detail information of the research paper, the number of citations and the ego network in the subview (d). (http://13.124.91.164)Survey of relevant research papers are very important in academic research to fully understanding the research trends in a given field of interest. Many researchers use keyword searches to investigate their related research. However, it is often difficult to find the desired research papers only through a keyword search. Here we propose a citation network visualization technique that constructs the genealogy of a research paper. It allows the user to trace the genealogy of the reference papers that form the basis of their research via large citation data sets, while also providing a visualization of how the research paper influences its research field.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126955823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Informative Metric for Summarizing HPC System Status","authors":"Yawei Hui, Byung-Hoon Park, C. Engelmann","doi":"10.1109/LDAV.2018.8739234","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739234","url":null,"abstract":"It remains a major challenge to effectively summarize and visualize in a comprehensive form the status of a complex computer system, such as the Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). In the ongoing research highlighted in this poster, we present system information entropy (SIE), a newly developed system metric that leverages the powers of traditional machine learning techniques and information theory. By compressing the multi-variant multi-dimensional event information recorded during the operation of the targeted system into a single time series of SIE, we demonstrate that the historical system status can be sensitively summarized in form of SIE and visualized concisely and comprehensively.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131372582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Situ Visualization and Analysis to Design Large Scale Experiments in Computational Fluid Dynamics","authors":"Bennett Bernardoni, N. Ferrier, J. Insley, M. Papka, Saumil Patel, S. Rizzi","doi":"10.1109/LDAV.2018.8739172","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739172","url":null,"abstract":"Nek5000 is a massively-parallel computational fluid dynamics code, which is widely used and researched, including as part of a co-design center of the Exascale Computing Project (ECP). As computation capacity reaches exascale, storage bandwidth remains stable leading to a larger percentage of time spent performing I/O. In situ analysis overcomes this issue by processing the data before it is written to disk. One method for accomplishing in situ analysis is through SENSEI, a generic in situ interface that enables the use of many existing in situ infrastructures with little modification to the simulation. In this work, we present the instrumentation of Nek5000 with SENSEI and evaluate its ability to accelerate the development of large scale simulation campaigns.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134462685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SpRay: Speculative Ray Scheduling for Large Data Visualization","authors":"Hyungman Park, D. Fussell, P. Navrátil","doi":"10.1109/LDAV.2018.8739224","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739224","url":null,"abstract":"Figure 1:All images rendered with our speculative ray tracing technique. First three columns: a massive channel-flow turbulence DNS dataset. Last two columns: an RM fluid instability dataset and an Enzo Astrophysics AMR dataset (left and right). The number of triangles from left to right: DNS2 (1.8 billion), DNS1-side and DNS1-back (0.9 billion), RM (108 million), and Enzo (8 million). Five images on the bottom row show ambient occlusion shading, and the rest show three-bounce path tracing. For all datasets, 32 samples per pixel were used to render images at 1024×1024 resolution, and one diffuse ray and 16 shadow rays were generated at every hit point. Using Stampede2 Skylake at the Texas Advanced Computing Center, each node with 192 GB memory, at least eight nodes are required to render the DNS dataset with speculation enabled.With modern supercomputers offering petascale compute capability, scientific simulations are now producing terascale data. For comprehensive understanding of such large data, ray tracing is becoming increasingly important for 3D-rendering in visualization due to its inherent ability to convey physically realistic visual information to the user. Implementing efficient parallel ray tracing systems on supercomputers while maximizing locality and parallelism is challenging because of the overhead incurred by ray communication across the cluster of compute nodes and data loading from storage. To address the problem, reordering rendering computations by means of ray batching and scheduling has been proposed to temporarily avoid inherent dependencies in the rendering computations and amortize the cost of expensive data moving operations over ray batches. In this paper, we introduce a novel speculative ray scheduling method that builds upon this insight but radically changes the approach to resolving dependencies by allowing redundant computations to a certain extent. To evaluate the method, we measure the performance of different implementations for both out-of-core and in situ rendering setups. Results show that compared to a well-known scheduling method, our approach on ambient occlusion and path tracing achieves up to 2.3× speedup for the scenes comprising up to billions of triangles extracted from terascale scientific data.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122295894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Multiple Large Fluid-Structure Interaction Simulations in Virtual Reality","authors":"Daniel Orban, Seth Johnson, H. Runesha, Lingyu Meng, Bethany Juhnke, A. Erdman, F. Samsel, Daniel F. Keefe","doi":"10.1109/LDAV.2018.8739222","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739222","url":null,"abstract":"Scientific visualization tools are rapidly embracing the necessary challenge of simultaneously visualizing multiple parameterized simulation data sets [8]. In the new paradigm, scientists hope to understand parameter relationships and stochastic trends that exist in a parameter space [6], [7]. At the same time, virtual reality (VR) environments have enabled exciting possible opportunities for exploring and comparing time varying spatial data sets [3]. Although VR offers a unique perspective to view 3D and 4D data, it requires high framerates for interactivity and optimized use of precious GPU memory. Accurate simulations, on the other hand, are often very large due to dynamic unstructured mesh resolutions and small timesteps, making it difficult to simply render even one data set. To solve this, large data visualization frameworks often use data sampling and efficient rendering techniques to engage the GPU [1], [8]. Even then, VR is mostly used to add a stereoscopic view, and is rarely an integral part of interactive data instance comparison [3].","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"67 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120810464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}