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...
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ISBN:
(纸本)9781538668733
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.
Visualizing the velocity decomposition of a group of objects has applications to many studied data types, such as Lagrangian-based flow data or geospatial movement data. Traditional visualization techniques are often ...
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ISBN:
(纸本)9781467385176
Visualizing the velocity decomposition of a group of objects has applications to many studied data types, such as Lagrangian-based flow data or geospatial movement data. Traditional visualization techniques are often subject to a trade-off between visual clutter and loss of detail, especially in a large scale setting. The use of 2D velocity histograms can alleviate these issues. While they have been used throughout domain specific areas on a basic level, there has been very little work in the visualization community on leveraging them to perform more advanced visualization tasks. In this work, we develop an interactive system which utilizes velocity histograms to visualize the velocity decomposition of a group of objects. In addition, we extend our tool to utilize two schemes for histogram generation: an on-the-fly sampling scheme as well as an in situ scheme to maintain interactivity in extreme scale applications.
This paper describes the adaptation to a distributed computational setting of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression. Additionally, we extend the algorithm ...
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ISBN:
(纸本)9798331516932;9798331516925
This paper describes the adaptation to a distributed computational setting of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression. Additionally, we extend the algorithm to efficiently compute connected components in distributed structured and unstructured grids, based either on the connectivity of the underlying mesh or a feature mask. Our implementation is seamlessly integrated with the distributed extension of the Topology ToolKit (TTK), ensuring robust performance and scalability. To demonstrate the practicality and efficiency of our algorithms, we conducted a series of scaling experiments on large-scale datasets, with sizes of up to 40963 vertices on up to 64 nodes and 768 cores.
The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze l...
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ISBN:
(纸本)9781728184685
The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze largedata sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical dataanalysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.
Power is becoming a major design constraint in the world of high-performance computing (HPC). This constraint affects the hardware being considered for future architectures, the ways it will run software, and the desi...
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ISBN:
(纸本)9781467385176
Power is becoming a major design constraint in the world of high-performance computing (HPC). This constraint affects the hardware being considered for future architectures, the ways it will run software, and the design of the software itself. Within this context, we explore tradeoffs between power and performance. visualization algorithms themselves merit special consideration, since they are more data-intensive in nature than traditional HPC programs like simulation codes. This data-intensive property enables different approaches for optimizing power usage. Our study focuses on the isosurfacing algorithm, and explores changes in power and performance as clock frequency changes, as power usage is highly dependent on clock frequency. We vary many of the factors seen in the HPC context-programming model (MPI vs. OpenMP), implementation (generalized vs. optimized), concurrency, architecture, and data set-and measure how these changes affect power-performance properties. The result is a study that informs the best approaches for optimizing energy usage for a representative visualization algorithm.
Scientific simulations typically store only a small fraction of computed timesteps due to storage and I/O bandwidth limitations. Previous work has demonstrated the compressibility of floating-point volume data, but su...
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ISBN:
(纸本)9781538606179
Scientific simulations typically store only a small fraction of computed timesteps due to storage and I/O bandwidth limitations. Previous work has demonstrated the compressibility of floating-point volume data, but such compression often comes with a tradeoff between computational complexity and the achievable compression ratio. This work demonstrates the use of special-purpose video encoding hardware on the GPU which is present but (to the best of our knowledge) completely unused in current GPU-equipped super computers such as Titan. We show that lossy encoding allows the output of far more data at sufficient quality for a posteriori rendering and analysis. We also show that the encoding can be computed in parallel to general-purpose computation due to the special-purpose hardware. Finally, we demonstrate such encoded volumes are inexpensive to decode in memory during analysis, making it unnecessary to ever store the decompressed volumes on disk.
In this paper we propose an approach in which interactive visualization and analysis are combined with batch tools for the processing of largedata collections. large and heterogeneous data collections are difficult t...
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In this paper we propose an approach in which interactive visualization and analysis are combined with batch tools for the processing of largedata collections. large and heterogeneous data collections are difficult to analyze and pose specific problems to interactive visualization. Application of the traditional interactive processing and visualization approaches as well as batch processing encounter considerable drawbacks for such large and heterogeneous data collections due to the amount and type of data. Computing resources are not sufficient for interactive exploration of the data and automated analysis has the disadvantage that the user has only limited control and feedback on the analysis process. In our approach, an analysis procedure with features and attributes of interest for the analysis is defined interactively. This procedure is used for off-line processing of large collections of data sets. The results of the batch process along with "visual summaries" are used for further analysis. visualization is not only used for the presentation of the result, but also as a tool to monitor the validity and quality of the operations performed during the batch process. Operations such as feature extraction and attribute calculation of the collected data sets are validated by visual inspection. This approach is illustrated by an extensive case study, in which a collection of confocal microscopy data sets is analyzed.
Today, computational fluid dynamics (CFD) research is almost impossible without computer-generated visualizations of the very large amounts of data resulting from numerical simulations. Although good techniques now ex...
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Today, computational fluid dynamics (CFD) research is almost impossible without computer-generated visualizations of the very large amounts of data resulting from numerical simulations. Although good techniques now exist for analysis of scalar data, most existing techniques for the visualization of vector fields-the predominant data type in CFD-meet only part of what is required. Common techniques such as arrow plots, streamlines, and particles work well for 2D applications, but for 3D data sets they often lead to cluttered displays. For tensors, which are much more complex and abstract entities the problem is even more severe. There is a real need for visualization, but there are no simple solutions. Many researchers have recognized this challenge and developed new techniques. We restrict ourselves to open research issues. We proceed in three ways. First, we propose a classification of existing vector and tensor field visualization techniques based on work by Delmarcelle and Hesselink (1994) and point out research gaps in this classification scheme. Second, we discuss feature-based visualization, which shows higher level descriptions derived from elementary data. Third, we consider the role of visualization in the research process, again revealing gaps in our current know-how concerning visualization of vector and tensor fields.
Dealing with the curse of dimensionality is a key challenge in high-dimensional datavisualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like di...
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ISBN:
(纸本)9781509056590
Dealing with the curse of dimensionality is a key challenge in high-dimensional datavisualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world dataanalysis scenarios.
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...
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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.
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