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ieee symposium on large data Analysis and visualization (LDAV)

作者： Yangguang Liao Hiroaki Matsui Oliver Kreylos Louise H. Kellogg KeckCAVE UC Davis CIG UC Davis KeckCAVE and CIG UC Davis

ISBN: (纸本)9781538668740

One of the greatest challenges facing scientists doing large computation of vector fields in a distributed parallel setting is the need for optimal parallel algorithms for flow visualization. To address this need, we present a new flow visualization method based on parallel 3D line integral convolution (LIC). Our approach uses the fact that 3D LIC only needs limited local information to design an embarrassingly parallel model with a trade-off between the additional memory cost of external cells and the time cost for communication. All data required for each process can be stored in either a local data block or the external cells which are sets of exterior data surrounding the local partition. One problem for parallel LIC is that equal domain size decomposition of the data cannot guarantee balanced parallel processes. To achieve a load-balanced visualization process, we repartition data using an estimate of the LIC computation time. In addition, to minimize the memory cost, we introduce a vector-driven external cell expansion method to reduce the required memory cost. We find that we can use fewer external cells with minimal loss of visual quality. We evaluate the performance of our visualization method by first comparing its parallel scalability with traditional integral field line visualization. Next, we compare our new partition method with other data partition methods to verify that the workload of our model is more balanced. Finally, we compare our external cell expansion method with a traditional layer-based external cell expansion method. Consequently, together with the new partition and external cell expansion methods, our parallel 3D LIC visualization proves to be an efficient and well-balanced parallel flow visualization with limited extra memory cost and a large saving of communication time.

关键词： data visualization Three-dimensional displays Convolution Scalability Load management Solid modeling Distributed databases

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Textout: Detecting Text-Layout Bugs in Mobile Apps via visualization-Oriented Learning

Textout: Detecting Text-Layout Bugs in Mobile Apps via Visua...

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International symposium on Software Reliability Engineering (ISSRE)

作者： Yaohui Wang Hui Xu Yangfan Zhou Michael R. Lyu Xin Wang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781728149837

Layout bugs commonly exist in mobile apps. Due to the fragmentation issues of smartphones, a layout bug may occur only on particular versions of smartphones. It is quite challenging to detect such bugs for state-of-the-art commercial automated testing platforms, although they can test an app with thousands of different smartphones in parallel. The main reason is that typical layout bugs neither crash an app nor generate any error messages. In this paper, we present our work for detecting text-layout bugs, which account for a large portion of layout bugs. We model text-layout bug detection as a classification problem. This then allows us to address it with sophisticated image processing and machine learning techniques. To this end, we propose an approach which we call Textout. Textout takes screenshots as its input and adopts a specifically-tailored text detection method and a convolutional neural network (CNN) classifier to perform automatic text-layout bug detection. We collect 33,102 text-region images as our training dataset and verify the effectiveness of our tool with 1,481 text-region images collected from real-world apps. Textout achieves an AUC (area under the curve) of 0.956 on the test dataset and shows an acceptable overhead. The dataset is open-source released for follow-up research.

关键词： convolutional neural nets data visualisation learning (artificial intelligence) mobile computing pattern classification smart phones text detection Smartphone data visualisation Pattern recognition Mobile Computing Bugs misinformation Mobile Applications layouts

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Multi-Resolution Climate Ensemble Parameter Analysis with Nested parallel Coordinates Plots

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2017年第1期23卷 81-90页

作者： Wang, Junpeng Liu, Xiaotong Shen, Han-Wei Lin, Guang Ohio State Univ Columbus OH 43210 USA Purdue Univ W Lafayette IN 47907 USA

Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.

关键词： parallel coordinates plots parameter analysis multi-resolution climate ensembles

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A large data visualization Framework for SPARC64 fx HPC Systems - Case Study: K Computer Operational Environment -

A Large Data Visualization Framework for SPARC64 fx HPC Syst...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Jorji Nonaka Kenji Ono Naohisa Sakamoto Kengo Hayashi Motohiko Matsuda Fumiyoshi Shoji Kentaro Oku Masahiro Fujita Kazuma Hatta RIKEN R-CCS Kyushu University Kobe University KASHIKA LTE Inc.

ISBN: (纸本)9781538668740

Leading-edge supercomputer systems have been designed to achieve the highest computational performance possible for running a wide variety of large-scale simulations, and the pre- and post-processing are usually not considered in the main design feature. Although supercomputer systems may have peculiar CPU architecture, the auxiliary computational systems tend to use commodity based hardware and software in the form of servers and clusters. In the case of the K computer operational environment, at RIKEN R-CCS, the supercomputer itself is based on SPARC64 fx CPU architecture, and pre- and post-processing servers are based on traditional x86 CPU architecture. In this poster we present a large data visualization environment developed for this peculiar HPC operational environment, presenting some of the efforts made to meet the large data visualization needs. It is publicly known that the next-generation leading-edge Japanese supercomputer will abandon this CPU architecture in favor of another architecture, but we expect that some of the knowledge obtained in this development will also be useful for this future coming supercomputer system.

关键词： data visualization Libraries Rendering (computer graphics) Hardware Supercomputers Servers Three-dimensional displays

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Techniques for data-parallel Searching for Duplicate Elements 7

Techniques for Data-Parallel Searching for Duplicate Element...

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

作者： Lessley, Brenton Moreland, Kenneth Larsen, Matthew Childs, Hank Univ Oregon Eugene OR 97403 USA Sandia Natl Labs Albuquerque NM USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781538606179

We study effective shared-memory, data-parallel techniques for searching for duplicate elements. We consider several data-parallel approaches, and how hash function, machine architecture, and data set can affect performance. We conclude that most choices of algorithm and hash function are problematic for general usage. However, we demonstrate that the choice of the Hash-Fight algorithm with the FNV1a hash function has consistently good performance over all configurations.

关键词： Sorting Computer architecture Indexing Algorithm design and analysis Runtime Electronic mail

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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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An Application-Aware data Replacement Policy for Interactive large-Scale Scientific visualization 31

An Application-Aware Data Replacement Policy for Interactive...

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31st ieee International parallel and Distributed Processing symposium Workshops (IPDPS)

作者： Yu, Lina Yu, Hongfeng Jiang, Hong Wang, Jun Univ Nebraska Lincoln NE 68583 USA Univ Texas Arlington Arlington TX 76019 USA Univ Iowa Iowa City IA USA

ISBN: (纸本)9780769561493

The unprecedented amounts of data generated from large scientific simulations impose a grand challenge in data analytics, and I/O simply becomes a major performance bottleneck. To address this challenge, we present an application-aware I/O optimization technique in support of interactive large-scale scientific visualization. We partition a scientific data into blocks, and carefully place data blocks in a memory hierarchy according to a characterization of data access patterns of user visualization operations. We conduct an empirical study to explore the parameter space to derive optimal solutions. We use real-world large-scale simulation datasets to demonstrate the effectiveness of our approach.

关键词： I/O optimization scientific visualization data replacement large-scale data

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In Situ visualization and Analysis to Design large Scale Experiments in Computational Fluid Dynamics

In Situ Visualization and Analysis to Design Large Scale Exp...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Bennett Bernardoni Nicola Ferrier Joseph Insley Michael E. Papka Saumil Patel Silvio Rizzi Argonne National Laboratory University of Illinois at Urbana-Champaign Argonne National Laboratory Argonne National Laboratory Northern Illinois University

ISBN: (纸本)9781538668740

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.

关键词： Histograms data visualization Analytical models Instruments Bridges Adaptation models data models

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Implicit Sphere Shadow Maps 10

Implicit Sphere Shadow Maps

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ieee Pacific visualization symposium (ieee PacificVis)

作者： Krone, Michael Reina, Guido Zahn, Sebastian Tremel, Tina Bahnmueller, Carsten Ertl, Thomas Univ Stuttgart Visualizat Res Ctr Stuttgart Germany

ISBN: (纸本)9781509057382

Particle data are commonly visualized by rendering a sphere for each particle. Since interactive rendering usually relies on fast local lighting, the spatial arrangement of the spheres is often very hard to perceive. That is, larger functional structures formed by the particles are not easily recognizable. Using global effects such as ambient occlusion or shadows adds important depth cues. In this work, we present Implicit Sphere Shadow Maps (ISSM), an application-tailored approach for large, dynamic particle data sets. This approach can be combined with state-of-the-art object-space ambient occlusion to further emphasize the spatial structure of molecules. We compare our technique against state-of-the-art methods for interactive rendering with respect to image quality and performance.

关键词： Computer graphics [I.3.7]: Three-Dimensional graphics and Realism-Color, shading shadowing, and texture Computer graphics [I.3.8]: Applications-Molecular visualization

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Image and Distribution Based Volume Rendering for large data Sets

Image and Distribution Based Volume Rendering for Large Data...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Ko-Chih Wang Naeem Shareef Han-Wei Shen The Ohio State University

Analyzing scientific datasets created from simulations on modern supercomputers is a daunting challenge due to the fast pace at which these datasets continue to grow. Low cost post analysis machines used by scientists to view and analyze these massive datasets are severely limited by their deficiencies in storage bandwidth, capacity, and computational power. Trying to simply move these datasets to these platforms is infeasible. Any approach to view and analyze these datasets on post analysis machines will have to effectively address the inevitable problem of data loss. Image based approaches are well suited for handling very large datasets on low cost platforms. Three challenges with these approaches are how to effectively represent the original data with minimal data loss, analyze the data in regards to transfer function exploration, which is a key analysis tool, and quantify the error from data loss during analysis. We present a novel image based approach using distributions to preserve data integrity. At each view sample, view dependent data is summarized at each pixel with distributions to define a compact proxy for the original dataset. We present this representation along with how to manipulate and render large scale datasets on post analysis machines. We show that our approach is a good trade off between rendering quality and interactive speed and provides uncertainty quantification for the information that is lost.

关键词： data visualization Transfer functions Histograms Uncertainty Rendering (computer graphics) data analysis Entropy

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