Uintah is a computational framework for fluid-structure interaction problems using a combination of the ICE fluid flow algorithm, adaptive mesh refinement (AMR) and MPM particle methods. Uintah uses domain decomposition with a task-graph approach for asynchronous communication and automatic message generation. The Uintah software has been used for a decade with its original task scheduler that ran computational tasks in a predefined static order. In order to improve the performance of Uintah for petascale architecture, a new dynamic task scheduler allowing better overlapping of the communication and computation is designed and evaluated in this study. The new scheduler supports asynchronous, out-of-order scheduling of computational tasks by putting them in a distributed directed acyclic graph (DAG) and by isolating task memory and keeping multiple copies of task variables in a data warehouse when necessary. A new runtime system has been implemented with a two-stage priority queuing architecture to improve the scheduling efficiency. The effectiveness of this new approach is shown through an analysis of the performance of the software on large scale fluid-structure examples.

Cells in an organism share the same genetic information in their DNA, but have very different forms and behavior because of the selective expression of subsets of their genes. The widely used approach of measuring gene expression over time from a tissue sample using techniques such as microarrays or sequencing do not provide information about the spatial position within the tissue where these genes are expressed. In contrast, we are working with biologists who use techniques that measure gene expression in every individual cell of entire fruitfly embryos over an hour of their development, and do so for multiple closely-related subspecies of Drosophila. These scientists are faced with the challenge of integrating temporal gene expression data with the spatial location of cells and, moreover, comparing this data across multiple related species. We have worked with these biologists over the past two years to develop MulteeSum, a visualization system that supports inspection and curation of data sets showing gene expression over time, in conjunction with the spatial location of the cells where the genes are expressed — it is the first tool to support comparisons across multiple such data sets. MulteeSum is part of a general and flexible framework we developed with our collaborators that is built around multiple summaries for each cell, allowing the biologists to explore the results of computations that mix spatial information, gene expression measurements over time, and data from multiple related species or organisms. We justify our design decisions based on specific descriptions of the analysis needs of our collaborators, and provide anecdotal evidence of the efficacy of MulteeSum through a series of case studies.

Biologists pioneering the new field of comparative functional genomics attempt to infer the mechanisms of gene regulation by looking for similarities and differences of gene activity over time across multiple species. They use three kinds of data: functional data such as gene activity measurements, pathway data that represent a series of reactions within a cellular process, and phylogenetic relationship data that describe the relatedness of species. No existing visualization tool can visually encode the biologically interesting relationships between multiple pathways, multiple genes, and multiple species. We tackle the challenge of visualizing all aspects of this comparative functional genomics dataset with a new interactive tool called Pathline. In addition to the overall characterization of the problem and design of Pathline, our contributions include two new visual encoding techniques. One is a new method for linearizing metabolic pathways that provides appropriate topological information and supports the comparison of quantitative data along the pathway. The second is the curvemap view, a depiction of time series data for comparison of gene activity and metabolite levels across multiple species. Pathline was developed in close collaboration with a team of genomic scientists. We validate our approach with case studies of the biologists' use of Pathline and report on how they use the tool to confirm existing findings and to discover new scientific insights.

3D iterative CT reconstruction is an active research area in medical imaging. Compared with analytic reconstruction methods such as FDK, iterative methods may provide better reconstruction results for incomplete and noisy projection data. The simultaneous algebraic reconstruction technique (SART), one of the most popular iterative reconstruction methods, is applied in the cone-beam geometry for highresolution reconstruction, with the help of graphics hardware (GPU) and total variation (TV) regularization. GPU greatly improves the efficiency of SART, which is computationally intense for CPU, and thus makes it suitable for clinical applications. TV regularization reduces the effects of noise and helps the convergence of SART for noisy data. Experimental results for both synthetic and real data are provided to evaluate the accuracy and efficiency of the proposed framework.

Keywords: Cone-beam CT, iterative reconstruction, SART, GPU, TV regularization

Most multidimensional projection techniques rely on distance (dissimilarity) information between data instances to embed high-dimensional data into a visual space. When data are endowed with Cartesian coordinates, an extra computational effort is necessary to compute the needed distances, making multidimensional projection prohibitive in applications dealing with interactivity and massive data. The novel multidimensional projection technique proposed in this work, called Part-Linear Multidimensional Projection (PLMP), has been tailored to handle multivariate data represented in Cartesian high-dimensional spaces, requiring only distance information between pairs of representative samples. This characteristic renders PLMP faster than previous methods when processing large data sets while still being competitive in terms of precision. Moreover, knowing the range of variation for data instances in the high-dimensional space, we can make PLMP a truly streaming data projection technique, a trait absent in previous methods.