The dataset generated by a large-scale numerical simulation may include thousands of timesteps and hundreds of variables describing different aspects of the modeled physical phenomena. In order to analyze and understand such data, scientists need the capability to explore simultaneously in the temporal, spatial, and variable domains of the data. Such capability, however, is not generally provided by conventional visualization tools. This paper presents a new visualization interface addressing this problem. The interface consists of three components which abstracts the complexity of exploring in temporal, variable, and spatial domain, respectively. The first component displays time histograms of the data, helps the user identify timesteps of interest, and also helps specify time-varying features. The second component displays correlations between variables in parallel coordinates and enables the user to verify those correlations and possibly identity unanticipated ones. The third component allows the user to more closely explore and validate the data in spatial domain while rendering multiple variables into a single visualization in a user controllable fashion. Each of these three components is not only an interface but is also the visualization itself, thus enabling efficient screen-space usage. The three components are tightly linked to facilitate tri-space data exploration, which offers scientists new power to study their time-varying, multivariate volume data.

Existing commercial finite element analysis (FEA) codes do not exhibit the performance necessary for large scale analysis on parallel computer systems. In this paper, we demonstrate the performance characteristics of a commercial parallel structural analysis code, ADVC, on Blue Gene/L (BG/L). The numerical algorithm of ADVC is described, tuned, and optimized on BG/L, and then a large scale drop impact analysis of a mobile phone is performed. The model of the mobile phone is a nearly-full assembly that includes inner structures. The size of the model we have analyzed has 47 million nodal points and 142 million DOFs. This does not seem exceptionally large, but the dynamic impact analysis of a product model, with the contact condition on the entire surface of the outer case under this size, cannot be handled by other CAE systems. Our analysis is an unprecedented attempt in the electronics industry. It took only half a day, 12.1 hours, for the analysis of about 2.4 milliseconds. The floating point operation performance obtained has been 538 GFLOPS on 4096 node of BG/L.

An important challenge in the application of direct volume rendering to time-varying data is the specification of transfer functions for all time steps. Very little research has been devoted to this problem, however. To address this issue we propose an approach which allows simultaneous classification of the entire time series. We explore options for transfer function specification that are based, either directly or indirectly, on the time histogram. Furthermore, we consider how to effectively provide feedback for interactive classification by exploring options for simultaneous rendering of the time series, again based on the time histogram. Finally, we apply this approach to several large time-varying data sets where we show that the important features at all times are captured with about the same effort it takes to classify one time step using conventional classification.

Rendering directly from packed or compressed volume data formats using graphics hardware has advantages in terms of memory consumption and bandwidth, but results in lower-quality images due to the prohibitive cost of performing interpolation and gradient-based shading on the reconstructed data. The problem with the existing method lies in its close coupling of decompression and interpolation. We demonstrate that deferred filtering overcomes this problem by using a two-pass decompression and rendering strategy. With this method interpolation and gradient calculations are very efficient, allowing high quality rendering directly from packed or compressed volume data. We evaluate the cost of creating interpolated, gradient-shaded renderings using traditional on-the-fly decompression and deferred filtering, and show that deferred filtering can provide up to twenty times speed-up for high quality rendering.