high performance computing on graphics processing units: hgpu.org

As the speed of computers continues to increase at a very fast rate, the size of data generated from scientific simulations has now reached petabytes ($10^{12}$ bytes) and beyond. Under such circumstances, no existing techniques can be used to perform effective data analysis at a full precision. To analyze large scale data sets, visual analytics techniques with effective summarization and flexible interface are crucial in assisting the exploration of data at different levels of detail. To improve data access efficiency, summarization and triage are important components for categorizing data items according to their saliency. This will allow the user to focus only on the relevant portion of data. In this dissertation, several visualization and analysis techniques are presented to facilitate the analysis of multivariate time-varying data and flow fields. For multivariate time-varying data sets, data items are categorized based on the values over time to provide an effective overview of the time-varying phenomena. From the similarity to the user-specified feature, dynamic phenomena across multiple variables in different spatial and temporal domains can be explored. To visualize flow fields, information theory is used to model the local flow complexity quantitatively. Based on the model, an information-aware visualization framework is designed to create images with different levels of visual focus according to the local flow complexity. By extending the measurement from object space to image space, visualization primitives can be further rearranged, leading to more effective visualization of salient flow features with less occlusion.