Novel Parallelization Techniques for Computer Graphics Applications

Increasingly complex and data-intensive algorithms in computer graphics applications require software engineers to find ways of improving performance and scalability to satisfy the requirements of customers and users. Parallelizing and tailoring each algorithm of each specific application is a time-consuming task and its implementation is domain-specific because it can not be reused outside the specific problem in which the algorithm is defined. Identifying reusable parallelization patterns that can be extrapolated and applied to other different algorithms is an essential task needed in order to provide consistent parallelization improvements and reduce the development time of evolving a sequential algorithm into a parallel one. This thesis focuses on defining general and efficient parallelization techniques and approaches that can be followed in order to parallelize complex 3D graphic algorithms. These parallelization patterns can be easily applied in order to convert most kinds of sequential complex and data-intensive algorithms to parallel ones obtaining consistent optimization results. The main idea in the thesis is to use multi-threading techniques to improve the parallelization and core utilization of 3D algorithms. Most of the 3D algorithms apply similar repetitive independent operations on a vast amount of 3D data. These application characteristics bring the opportunity of applying multi-thread parallelization techniques on such applications. The efficiency of the proposed idea is tested on two common computer graphics algorithms: hidden-line removal and collision detection. Both algorithms are data-intensive algorithms, whose conversions from a sequential to a multithread implementation introduce challenges, due to their complexities and the fact that elements in their data have different sizes and complexities, producing work-load imbalances and asymmetries between processing elements. The results show that the proposed principles and patterns can be easily applied to both algorithms, transforming their sequential to multithread implementations, obtaining consistent optimization results proportional to the number of processing elements. From the work done in this thesis, it is concluded that the suggested parallelization warrants further study and development in order to extend its usage to heterogeneous platforms such as a Graphical Processing Unit (GPU). OpenCL is the most feasible framework to explore in the future due to its interoperability among different platforms

Comparing user experience between fuzzy logic and exact feedback systems in an e-learning environment

Neurophysiology students, including nursing students, must complete a course on electroencephalogram (EEG) sensor placement as part of their third-year studies. Currently, students attend and observe an EEG placement demonstration by experienced EEG professionals at the beginning of a semester and at the end of the semester they receive hands-on training. The lecturers have suggested building an e-learning environment that will help to bridge the gap between the observation and practical training sessions. This thesis presents the design, development, and implementation of such an e-learning environment that provides feedback to the students about the accuracy of EEG electrode placement. The learning environment contains two different feedback systems. One that provides fuzzy (more human) guidance to the students and another giving exact value error feedback. The purpose of this thesis was to determine which of the two systems the students enjoyed more and which one they thought would provide the best learning. The learning environment bases its evaluation of the virtual EEG placement on the 10-20 system—an international standard for the placement of EEG electrodes. Students were asked to spend two weeks with the system after their observation training. After their experience with the learning environment, students were invited to fill in a questionnaire and have a group discussion about their experiences with the virtual EEG placement system. The questionnaire measured student perceptions over three error categories, namely: short, medium and long distances between virtual placement and ideal positioning. The results showed that the students preferred the fuzzy logic over the exact feedback system. Although the students noted that the exact feedback system provided overall a more precise error feedback, the fuzzy logic was generally better-received for short and medium errors. For long errors, the exact and fuzzy feedback systems received similar results. Group discussions also indicated that the students welcomed the additional learning opportunity between their observation and practical training sessions and felt it would be beneficial to their learning. From this user experience test, in conclusion, the system warrants further development and possibly future formal integration into the lesson plan for neurophysiology students