Abstract Because of intensive inter‐node communications, image compositing has always been a bottleneck in parallel visualization systems. In a heterogeneous networking environment, the variation of link bandwidth and latency adds more uncertainty to the system performance. In this paper, we present a pipelining image compositing algorithm in heterogeneous networking environments, which is able to rearrange the direction of data flow of a compositing pipeline under strict ordering constraint. We introduce a novel directional image compositing operator that specifies not only the color and α channels of the output but also the direction of data flow when performing compositing. Based on this new operator, we thoroughly study the properties of image compositing pipelines in heterogeneous environments. We develop an optimization algorithm that could find the optimal pipeline from an exponentially large searching space in polynomial time. We conducted a comprehensive evaluation on the ns‐3 network simulator. Experimental results demonstrate the efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, we present an efficient level of detail algorithm for texture‐based flow visualization. Our goal is to enhance visual perception and performance and generate smooth animation. To achieve our goal, we first model an adaptive input texture taking into account flow patterns to output view‐dependent high‐quality images. Then, we compute field lines only from sparse sampling points of the input noise texture for outputting volume line integral convolution textures and skip empty space utilizing two quantized binary histograms. To improve image quality, we implement anti‐aliasing through adjusting the line integral convolution step size and thickness of trajectory lines with an opacity function. We further extend our solution to unsteady flow. Flow structures and evolution are clearly shown through smooth animation achieved with coherent evolution of particles, handling of discontinuous flow lines, and spatio‐temporal linear constraint of the underlying noise volume. In the result section, we show high‐quality level of detail of three‐dimensional texture‐based flow visualization with high performance. We also demonstrate that our algorithm can achieve smooth evolution for unsteady flow with spatio‐temporal coherence. Copyright © 2015 John Wiley & Sons, Ltd.

Many extreme-scale scientific applications generate colossal amounts of data that require an increasing number of processors for parallel visualization. Among the three well-known parallel architectures, i.e. sort-first/middle/last, sort-last, which comprises of two stages, i.e. image rendering and composition, is often preferred due to its adaptability to load balancing. We propose a generalized method, namely, Grouping More and Pairing Less (GMPL), for order-independent image composition in sort-last parallel rendering. GMPL is of two-fold novelty: i) it takes a prime factorization-based approach for processor grouping, which not only obviates the common restriction in existing methods on the total number of processors to fully utilize computing resources, but also breaks down processors to the lowest level with a minimum number of peers in each group to achieve high concurrency and save communication cost; ii) within each group, it employs an improved direct send method to narrow down each processor's pairing scope to further reduce communication overhead and increase composition efficiency. The performance superiority of GMPL over existing methods is evaluated through rigorous theoretical analysis and further verified by extensive experimental results on a high-performance visualization cluster.

Collaboration is prevalent for network security teams to protect networking environments, yet few network visualization tools are designed for collaborative analysis. With the increasing complexity and volume of dynamic networks, it is important to adopt strategies of joint decision-making through developing collaborative visualization approaches. In this paper, we present a formal user study to evaluate how paired users collaborate under co-located and distributed collaboration environments to tackle the problems of intrusion detection. Ten paired participants are requested to use network visualization patterns to identify attacks existed in the datasets. We observe participants behaviors and collect their performances from the aspects of coordination and communication, which include prioritizing goals and directions, dividing and balancing workloads, and negotiating analysis decisions while maintaining situational awareness. Based on the results, we conclude several coordination strategies and summarize the values of communication for collaborative detection. We also discuss human-related factors in the process of joint decision-making. Our study provides useful information for future design and development of collaborative visualization systems.

In the cloud computing system, the schedule of computing resources is a critical portion of cloud computing study. An effective load balancing strategy is able to markedly improve the task throughput of cloud computing. Virtual machines are selected as a fundamental processing unit of cloud computing. The resources in cloud computing will increase sharply and vary dynamically due to the utilization of virtualization technology. Therefore, implementation of load balancing in cloud computing has become complicated and it is difficult to achieve. Multi-agent genetic algorithm (MAGA) is a hybrid algorithm of GA, whose performance is far superior to that of the traditional GA. This paper demonstrates the advantage of MAGA over traditional GA, and then exploits multi-agent genetic algorithms to solve the load balancing problem in cloud computing, by designing a load balancing model on the basis of virtualization resource management. Finally, by comparing MAGA with Minimum strategy, the experiment results prove that MAGA is able to achieve better performance of load balancing.

With the development of science, research on data analysis is becoming increasingly important. However, sometimes it is difficult to draw a conclusion from a complex data set. Data Visualization has been widely used for people to understand more about their datasets by representing the data in a way that visually highlights the relationships. As the size of data grows exponentially, keeping multiple local copies of the data becomes unrealistic for a collaborative research project. In this paper, we design and develop a cybertool, CT-IANP, which supports collaborative research in the area of atmospheric nucleation. The paper shows how Java 3D, Web-based tools, and other techniques are used to achieve the goal.

We present a non-photo realistic rendering technique for interactive exploration of isosurfaces generated from remote volumetric data. Instead of relying on the conventional smooth shading technique to render the isosurfaces, a point-based technique is used to represent and render the isosurfaces in a remote client-server environment. The non-photo realistic nature of the proposed rendering method enables the server to transmit only the essential surface features, which substantially reduces the network traffic. The algorithm also utilizes frame coherence and efficiently encodes the isosurface configuration inside each voxel cell to further minimize the network overhead. Finally, our algorithm can adjust the point distributions using different illumination settings to adapt to different network speeds.

Next-generation scientific applications require the capability to visualize large archival data sets or on-going computer simulations of physical and other phenomena over wide-area network connections. To minimize the latency in interactive visualizations across wide-area networks, we propose an approach that adaptively decomposes and maps the visualization pipeline onto a set of strategically selected network nodes. This scheme is realized by grouping the modules that implement visualization and networking subtasks and mapping them onto computing nodes with possibly disparate computing capabilities and network connections. Using estimates for communication and processing times of subtasks, we present a polynomial-time algorithm to compute a decomposition and mapping to achieve minimum end-to-end delay of the visualization pipeline. We present experimental results using geographically distributed deployments to demonstrate the effectiveness of this method in visualizing data sets from three application domains.

Remote visualization is an enabling technology aiming to resolve the barrier of physical distance. While many researchers have developed innovative algorithms for remote visualization, previous work has focused little on systematically investigating optimal configurations of remote visualization architectures. In this paper, we study caching and prefetching, an important aspect of such architecture design, in order to optimize the fetch time in a remote visualization system. Unlike a processor cache or web cache, caching for remote visualization is unique and complex. Through actual experimentation and numerical simulation, we have discovered ways to systematically evaluate and search for optimal configurations of remote visualization caches under various scenarios, such as different network speeds, sizes of data for user requests, prefetch schemes, cache depletion schemes, etc. We have also designed a practical infrastructure software to adaptively optimize the caching architecture of general remote visualization systems, when a different application is started or the network condition varies. The lower bound of achievable latency discovered with our approach can aid the design of remote visualization algorithms and the selection of suitable network layouts for a remote visualization system.

Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets.

We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users with a variety of local computing capabilities, we introduce an adaptive data selection method based on an "enhanced time-space partitioning" (ETSP) tree that assists with effective visibility culling, as well as multiresolution data selection. By traversing the tree, our data management algorithm can quickly identify the visible regions of data, and, for each region, adaptively choose the lowest resolution satisfying user-specified error tolerances. Only necessary data elements are accessed and sent to the visualization pipeline. To further address the issue of sharing large-scale data among geographically distributed collaborative teams, we have designed an infrastructure for integrating our data management technique with a distributed data storage system provided by logistical networking (LoN). Data sets at different resolutions are generated and uploaded to LoN for wide-area access. We describe a parallel volume rendering system that verifies the effectiveness of our data storage, selection and access scheme.

Typically there is a high coherence in data values between neighboring time steps in an iterative scientific software simulation; this characteristic similarly contributes to a corresponding coherence in the visibility of volume blocks when these consecutive time steps are rendered. Yet traditional visibility culling algorithms were mainly designed for static data, without consideration of such potential temporal coherency. We explore the use of temporal occlusion coherence (TOC) to accelerate visibility culling for time-varying volume rendering. In our algorithm, the opacity of volume blocks is encoded by means of plenoptic opacity functions (POFs). A coherence-based block fusion technique is employed to coalesce time-coherent data blocks over a span of time steps into a single, representative block. Then POFs need only be computed for these representative blocks. To quickly determine the subvolumes that do not require updates in their visibility status for each subsequent time step, a hierarchical "TOC tree" data structure is constructed to store the spans of coherent time steps. To achieve maximal culling potential, while remaining conservative, we have extended our previous POP into an optimized POP (OPOP) encoding scheme for this specific scenario. To test our general TOC and OPOF approach, we have designed a parallel time-varying volume rendering algorithm accelerated by visibility culling. Results from experimental runs on a 32-processor cluster confirm both the effectiveness and scalability of our approach.

Visibility culling has the potential to accelerate large data visualization in significant ways. Unfortunately, existing algorithms do not scale well when parallelized, and require full re-computation whenever the opacity transfer function is modified. To address these issues, we have designed a Plenoptic Opacity Function (POF) scheme to encode the view-dependent opacity of a volume block. POFs are computed off-line during a pre-processing stage, only once for each block. We show that using POFs is (i) an efficient, conservative and effective way to encode the opacity variations of a volume block for a range of views, (ii) flexible for re-use by a family of opacity transfer functions without the need for additional off-line processing, and (iii) highly scalable for use in massively parallel implementations. Our results confirm the efficacy of POFs for visibility culling in large-scale parallel volume rendering; we can interactively render the Visible Woman dataset using software ray-casting on 32 processors, with interactive modification of the opacity transfer function on-the-fly.