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ieee Pacific visualization symposium

IEEE Pacific Visualization Symposium

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11th ieee Pacific visualization symposium, PacificVis 2018

ISBN: (纸本)9781538614242

The proceedings contain 29 papers. The topics discussed include: optimal algorithms for compact linear layouts;development of an integrated visualization system for phenotypic character networks;image and distribution based volume rendering for large data sets;topologically controlled lossy compression;information guided data sampling and recovery using bitmap indexing;in situ prediction driven feature analysis in jet engine simulations;access pattern learning with long short-term memory for parallel particle tracing;dynamic data repartitioning for load-balanced parallel particle tracing;uncertainty visualization for secondary structures of proteins;and modeling and visualization of uncertainty-aware geometry using multi-variate normal distributions.

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SpRay: Speculative Ray Scheduling for large data visualization 8

SpRay: Speculative Ray Scheduling for Large Data Visualizati...

引用

8th ieee symposium on large-Scale data Analysis and visualization (LDAV)

作者： Park, Hyungman Fussell, Donald Navratil, Paul Univ Texas Austin Elect & Comp Engn Austin TX 78712 USA Univ Texas Austin Comp Sci Austin TX 78712 USA Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA

ISBN: (纸本)9781538668733

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 2.3x speedup for the scenes comprising up to billions of triangles extracted from terascale scientific data.

关键词： Human-Centered Computing visualization visualization Techniques Computing Methodologies Computer graphics Rendering Ray Tracing

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2018 ieee 8th symposium on large data Analysis and visualization, LDAV 2018

2018 IEEE 8th Symposium on Large Data Analysis and Visualiza...

引用

8th ieee symposium on large data Analysis and visualization, LDAV 2018

ISBN: (纸本)9781538668733

The proceedings contain 20 papers. The topics discussed include: method for improving RadViz's navigation function based on focusing and filtering;parallel partial reduction for large-scale data analysis and visualization;in situ visualization and analysis to design large scale experiments in computational fluid dynamics;citation network visualization of reference papers based on influence groups;visual analysis of simulation uncertainty using cost-effective sampling;foundations of multivariate functional approximation for scientific data;lifted Wasserstein matcher for fast and robust topology tracking;and VOIDGA: a view-approximation oriented image database generation approach.

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parallel Partial Reduction for large-Scale data Analysis and visualization

Parallel Partial Reduction for Large-Scale Data Analysis and...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Wenbin He Hanqi Guo Tom Peterka Sheng Di Franck Cappello Han-Wei Shen Department of Computer Science and Engineering The Ohio State University Columbus OH USA Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9781538668740

We present a novel partial reduction algorithm to aggregate sparsely distributed intermediate results that are generated by data-parallel analysis and visualization algorithms. Applications of partial reduction include flow trajectory analysis, big data online analytical processing, and volume rendering. Unlike traditional full parallel reduction that exchanges dense data across all processes, the purpose of partial reduction is to exchange only intermediate results that correspond to the same query, such as line segments of the same flow trajectory. To this end, we design a three-stage algorithm that minimizes the communication cost: (1) partitioning the result space into groups; (2) constructing and optimizing the reduction partners for each group; and (3) initiating collective reduction operations for all groups concurrently. Both theoretical and empirical analyses show that our algorithm outperforms the traditional methods when the intermediate results are sparsely distributed. We also demonstrate the effectiveness of our algorithm for flow visualization, big log data analysis, and volume rendering.

关键词： data visualization Distributed databases Rendering (computer graphics) Trajectory data analysis Histograms parallel processing

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DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using data-parallel Primitives

DPP-PMRF: Rethinking Optimization for a Probabilistic Graphi...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Brenton Lessley Talita Perciano Colleen Heinemann David Camp Hank Childs E. Wes Bethel University of Oregon Lawrence Berkeley Nat’l Lab

ISBN: (纸本)9781538668740

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).

关键词： Optimization Image segmentation Arrays Libraries graphics processing units Probabilistic logic

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Comparing Binary-Swap Algorithms for Odd Factors of Processes

Comparing Binary-Swap Algorithms for Odd Factors of Processe...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Kenneth Moreland Sandia National Laboratories

ISBN: (纸本)9781538668740

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.

关键词： Rendering (computer graphics) Pipelines Laboratories data visualization Program processors Bridges Geometry

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parallel Multi-Layer Ghost Cell Generation for Distributed Unstructured Grids 7

Parallel Multi-Layer Ghost Cell Generation for Distributed U...

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ieee 7th symposium on large data Analysis and visualization (LDAV)

作者： Patchett, John M. Nouanesengesy, Boonthanome Pouderoux, Joachim Ahrens, James Hagen, Hans TU Kaiserslautern Los Alamos Natl Lab Kaiserslautern Germany Los Alamos Natl Lab Los Angeles CA USA Kitware SAS Villeurbanne France TU Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781538606179

Ghost cells are important for distributed memory parallel operations that require neighborhood information, and are required for correctness on the boundaries of local data partitions. Ghost cells are one or more layers of grid cells surrounding the external boundary of the local partition, which are owned by other data partitions. They are used by the local partition when neighbor information is required. Finding ghost cells in structured data is trivial and can normally be done by calculating on cell indices. Obtaining ghost cells for unstructured grid data, however, is a nontrivial task. It requires an analysis of the connectivity of the grid in order to find neighbor cells. When the grid is distributed, the operation is further complicated by the need to determine which processes own the neighbor cells, and coordinating communication with them. This is a problem when operating on unstructured grid data sets that do not already have ghost cells. parallel visualization algorithms will usually assume that a cell does not exist if it is not in the local data partition. Without ghost cells, this leads to operations that need neighborhood information, such as point data interpolated from cell data, being calculated incorrectly at partition boundaries. Production visualization tools generally support the existence of ghost cells, but not their generation, especially for unstructured grids. In the literature, there is no documented algorithm for generating in parallel one or more layers of ghost cells for unstructured grid data which has already been partitioned. We present a new algorithm to compute ghost cells in parallel on distributed unstructured data sets with no global cell or point IDs. Given a partitioned data set without ghost cells, this algorithm is capable of producing any number of layers of ghost cells necessary to support parallel operations. Performance results and timing comparisons to ParaView's D3 filter are presented. A number of optimizations to th

关键词： I.3.2 [COMPUTER graphics]: graphics Systems-Distributed/network graphics G.1.0 [Mathematics of Computing]: Numerical Analysis-parallel Algorirthms

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Maximal Clique Enumeration with data-parallel Primitives 7

Maximal Clique Enumeration with Data-Parallel Primitives

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ieee 7th symposium on large data Analysis and visualization (LDAV)

作者： Lessley, Brenton Perciano, Talita Mathai, Manish Childs, Hank Bethel, E. Wes Univ Oregon Eugene OR 97403 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781538606179

The enumeration of all maximal cliques in an undirected graph is a fundamental problem arising in several research areas. We consider maximal clique enumeration on shared-memory, multi-core architectures and introduce an approach consisting entirely of data-parallel operations, in an effort to achieve efficient and portable performance across different architectures. We study the performance of the algorithm via experiments varying over benchmark graphs and architectures. Overall, we observe that our algorithm achieves up to a 33-time speedup and 9-time speedup over state-of-the-art distributed and serial algorithms, respectively, for graphs with higher ratios of maximal cliques to total cliques. Further, we attain additional speedups on a GPU architecture, demonstrating the portable performance of our data-parallel design.

关键词： Arrays Algorithm design and analysis Vegetation data visualization graphics processing units

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Galaxy: Asynchronous Ray Tracing for large High-Fidelity visualization

Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Vis...

引用

ieee symposium on large data Analysis and visualization (LDAV)

作者： Greg Abram Paul Navrátil Pascal Grossett David Rogers James Ahrens Texas Advanced Computing Center Los Alamos National Laboratory

ISBN: (纸本)9781538668740

Figure *** 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 *** 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.

关键词： Task analysis Rendering (computer graphics) Distributed databases Message systems Geometry Lighting Ray tracing

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SpRay: Speculative Ray Scheduling for large data visualization

SpRay: Speculative Ray Scheduling for Large Data Visualizati...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Hyungman Park Donald Fussell Paul Navrátil Texas Advanced Computing Center The University of Texas at Austin Computer Science The University of Texas at Austin

ISBN: (纸本)9781538668740

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 *** 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

关键词： Rendering (computer graphics) Ray tracing data visualization Processor scheduling Cameras Supercomputers

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