Utilizing ubiquitous commodity graphics hardware for scientific computing

Current GPUs have many times the memory bandwidth and computing power compared to CPUs. The difference in performance is getting bigger as the evolution speed of the GPUs is higher than of the CPUs. This make it interesting to use the GPU for general purpose computing (GPGPU). I begin by looking at the architecture of the GPU, and some different techniques for programming on a GPU, including some of the available high-level languages. I have implemented the Mandelbrot computation on a cluster of GPUs (the HPDC display wall), and compared it against two different CPU implementations on the cluster. I have also implemented the Mandelbrot computation in both Cg and Brook, and compared the performance of the two languages. My experimental study shows that the GPU implementation of the Mandelbrot application is up to twice as fast as the load-balanced CPU implementation on the cluster of 28 computers, and up to 6 times faster on one computer

Integrating analytics with relational databases

In order to uncover insights and trends, it is an increasingly common practice for companies of all shapes and sizes to gather large quantities of data and to then analyze that data. This data can come from a multitude of different sources, ranging from data gathered about consumer behavior to data gathered from sensors. The most prevalent way of storing and managing data has traditionally been a relational database management system (RDBMS). However, there is currently a disconnect between the tools used for analysis of data and the tools used for storing that data. Instead of working directly with RDBMSes, these tools are build to work in a stand-alone fashion, and offer integration with RDBMSes as an afterthought. The focus of my PhD research is on investigating different methods of combining popular analytical tools (such as R or Python) with database management systems in an efficient and user-friendly fashion

Zifazah: A Scientific Visualization Language for Tensor Field Visualizations

This thesis presents the design and prototype implementation of a scientific visualization language called Zifazah for composing and exploring 3D visualizations of diffusion tensor magnetic resonance imaging (DT-MRI or DTI) data. Unlike existing tools allowing flexible customization of data visualizations that are programmer-oriented, Zifazah focuses on domain scientists as end users in order to enable them to freely compose visualizations of their scientific data set. Verbal descriptions of end users about how they would build and explore DTI visualizations are analyzed to collect syntax, semantics, and control structures of the language. Zifazah makes use of the initial set of lexical terms and semantical patterns to provide a declarative language in the spirit of intuitive syntax and usage. Along with sample scripts representative of the main language design features, some new DTI visualizations created by end users using the novel language have also been presented