Remote visualization has emerged as a necessary tool in the analysis of big data. High-performance computing clusters can provide several benefits in scaling to larger data sizes, from parallel file systems to larger RAM profiles to parallel computation among many CPUs and GPUs. For scalable data visualization, remote visualization tools and infrastructure is critical where only pixels and interaction events are sent over the network instead of the data. In this paper, we present our pipeline using VirtualGL, TurboVNC, and ParaView to render over 40 million points using remote HPC clusters and project over 26 million pixels in a CAVE-style system. We benchmark the system by varying the video stream compression parameters supported by TurboVNC and establish some best practices for typical usage scenarios. This work will help research scientists and academicians in scaling their big data visualizations for real time interaction. / Master of Science

Many simulation data sets are so massive that they must be distributed among disk farms attached to different computing nodes. The data is partitioned into spatially disjoint sets that are not easily transferable among nodes due to bandwidth limitations. Conventional machine learning methods are not designed for this type of data distribution. Experts mark a training data set with different levels of saliency emphasizing speed rather than accuracy due to the size of the task. The challenge is to develop machine learning methods that learn how the expert has marked the training data so that similar test data sets can be marked more efficiently. Ensembles of machine learning classifiers are typically more accurate than individual classifiers. An ensemble of machine learning classifiers requires substantially less memory than the corresponding partition of the data set. This allows the transfer of ensembles among partitions. If all the ensembles are sent to each partition, they can vote for a level of saliency for each example in the partition. Different partitions of the data set may not have any salient points, especially if the data set has a time step dimension. This means the learned classifier for such partitions can not vote for saliency since they have not been trained to recognize it. In this work, we investigate the performance of different ensembles of classifiers on spatially partitioned data sets. Success is measured by the correct recognition of unknown and salient regions of data points.