Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant.

Abstract Most 3D vector field visualization techniques suffer from the problem of visual clutter, and it remains a challenging task to effectively convey both directional and structural information of 3D vector fields. In this paper, we present a novel visualization framework that combines the advantages of clustering methods and illustrative rendering techniques to generate a concise and informative depiction of complex flow structures. Given a 3D vector field, we first generate a number of streamlines covering the important regions based on an entropy measurement. Then we decompose the streamlines into different groups based on a categorization of vector information, wherein the streamline pattern in each group is ensured to be coherent or nearly coherent. For each group, we select a set of representative streamlines and render them in an illustrative fashion to enhance depth cues and succinctly show local flow characteristics. The results demonstrate that our approach can generate a visualization that is relatively free of visual clutter while facilitating perception of salient information of complex vector fields.

This paper presents visualization of field-measured, time-varying multidimensional earthquake accelerograph readings. Direct volume rendering is used to depict the space-time relationships of seismic readings collected from sensor stations in an intuitive way such that the progress of seismic wave propagation of an earthquake event can be directly observed. The resulting visualization reveals the sequence of seismic wave initiation, propagation, attenuation over time, and energy releasing events. We provide a case study on the magnitude scale Mw7.6 Chi-Chi earthquake in Taiwan, which is the most thoroughly recorded earthquake event ever in the history. More than 400 stations recorded this event, and the readings from this event increased global strong-motion records five folds. Each station measured east-west, north-south, and vertical component of acceleration for approximately 90 seconds. The sensor network released the initial raw data within minutes after the Chi-Chi mainshock. It is essential to have a visualization system for fast data exploring and analyzing, offering crucial visual analytical information for scientists to make quick judgments. Raw data requires preprocessing before it can be rendered. We generated a sequence of ground-motion wave-field maps of 350 × 200 regular grid covers the entire Taiwan island from the sensor network readings. The result is a total of 1000 ground-motion wave-field maps with 0.1 second interval, forming a 1000 × 350 × 200 volume data set. We show that visualizing the time-varying component of the data spatially uncovers the changing features hidden in the data.

The pervasive concept of cloud computing suggests that visualization, which is both data and computing intensive, is a perfect cloud computing application. This paper presents a sketch of an interface design for an online visualization service. To make such a service attractive to a wider audience, its user interface must be simple and easy to use for both casual and expert users. We envision an interface that supports visualization processes mainly directed by browsing and assessing existing visualizations in terms of images and videos will be very appealing to, in particular, casual users. That is, the aim is to maximize the utilization of the rich visualization data on the web. Without losing generality, we consider volume data visualization applications for our interface design. We also discuss issues in organizing online visualization data, and constructing and managing a rendering cloud.

In this paper we present a multi-GPU parallel volume rendering implemention built using the MapReduce programming model. We give implementation details of the library, including specific optimizations made for our rendering and compositing design. We analyze the theoretical peak performance and bottlenecks for all tasks required and show that our system significantly reduces computation as a bottleneck in the ray-casting phase. We demonstrate that our rendering speeds are adequate for interactive visualization (our system is capable of rendering a 10243 floating-point sampled volume in under one second using 8 GPUs), and that our system is capable of delivering both in-core and out-of-core visualizations. We argue that a multi-GPU MapReduce library is a good fit for parallel volume renderering because it is easy to program for, scales well, and eliminates the need to focus on I/O algorithms thus allowing the focus to be on visualization algorithms instead. We show that our system scales with respect to the size of the volume, and (given enough work) the number of GPUs.

This paper introduces a new streamline placement and selection algorithm for 3D vector fields. Instead of considering the problem as a simple feature search in data space, we base our work on the observation that most streamline fields generate a lot of self-occlusion which prevents proper visualization. In order to avoid this issue, we approach the problem in a view-dependent fashion and dynamically determine a set of streamlines which contributes to data understanding without cluttering the view. Since our technique couples flow characteristic criteria and view-dependent streamline selection we are able achieve the best of both worlds: relevant flow description and intelligible, uncluttered pictures. We detail an efficient GPU implementation of our algorithm, show comprehensive visual results on multiple datasets and compare our method with existing flow depiction techniques. Our results show that our technique greatly improves the readability of streamline visualizations on different datasets without requiring user intervention.

In this paper, an object-oriented unified optimization framework (UOF) for general problem optimization is proposed. Based on evolutionary algorithms, numerical deterministic methods, and C++ objective design, the UOF possesses real-world applications for various optimization problems. The UOF is with interface between defining a general problem and generic solver; hence the two areas are bridged. The UOF's components are categorized into problem and solver parts, and they work independently. Therefore, the reusable high level code allows the adaptation to new problem and solver quickly. Practical applications in microelectronics industry confirm the functionality of the developed unified optimization framework.

A new parallel semiconductor device simulation using the dynamic load balancing approach is presented. This semiconductor device simulation based on adaptive finite volume error estimation, and monotone iterative methods has been developed and implemented on a Linux-cluster with MPI library. Two different parallel versions of the algorithm to perform a complete device simulation are proposed. The first one is a dynamic parallel domain decomposition approach. The second version is a parallel current-voltage characteristic points simulation. The implementation shows that a well-designed load balancing simulation can reduce the execution time up to an order of magnitude. Compared with the measured data, numerical simulations on P-N diode, N-MOSFET, and DTMOS devices are presented to show the accuracy and efficiency of the method.