Browsing by Subject "parallel algorithms"
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Item Open Access Comparison of image space subdivision algorithms for parallel volume rendering(Bilkent University, 1995) Tanin, EgemenIn many scientific applications, results are presented as unstructured volumetric data sets. Direct Volume Rendering (DVR) is a powerful way of visualizing these volumetric data sets. However, it involves intensive computations. In addition, most of the volumetric data sets also require huge memories. Hence, DVR is a good candidate for parallelization on distributed memory multicomputers. Also most of the engineering simulations are done on multicomputers. Therefore, visualization of these results on the same architectures where simulations are done avoids the overhead of transporting large amount of data. In order to visualize unstructured volumetric data sets, the underlying algorithms should resolve the point location and the view sort problems of the 3D grid points. In this thesis, these problems are solved by using the well-known Scanline Z-Buffer algorithm. Three image space subdivision algorithms, namely horizontal, rectangular, and recursive subdivisions, are utilized to distribute the computations evenly among the processors in the rendering phase. The main parallel algorithm uses Raycasting approach of DVR to visualize the data sets, which is also an image space method. Therefore, the divisions are made in order to obtain a set of sub-images. Static task decomposition is used where each processor is assigned to a single sub-image. The load balance among the processors is achieved by defining the overall work load with in a sub-image by using the milestone operations done in the Scanline Z-Buffer algorithm. The algorithms are developed in a way that they can handle any kind of polygonal, volumetric, and etc. data set where the underlying architecture is also kept flexible in many aspects for the sake of generality and portability. The experimental performance evaluation of the horizontal, rectangular, and recursive subdivision algorithms on an IBM-SP2 system are presented and discussed in a comparative way.Item Open Access Parallel image restoration(Bilkent University, 2004) Malas, TahirIn this thesis, we are concerned with the image restoration problem which has been formulated in the literature as a system of linear inequalities. With this formulation, the resulting constraint matrix is an unstructured sparse-matrix and even with small size images we end up with huge matrices. So, to solve the restoration problem, we have used the surrogate constraint methods, that can work efficiently for large size problems and are amenable for parallel implementations. Among the surrogate constraint methods, the basic method considers all of the violated constraints in the system and performs a single block projection in each step. On the other hand, parallel method considers a subset of the constraints, and makes simultaneous block projections. Using several partitioning strategies and adopting different communication models we have realized several parallel implementations of the two methods. We have used the hypergraph partitioning based decomposition methods in order to minimize the communication costs while ensuring load balance among the processors. The implementations are evaluated based on the per iteration performance and on the overall performance. Besides, the effects of different partitioning strategies on the speed of convergence are investigated. The experimental results reveal that the proposed parallelization schemes have practical usage in the restoration problem and in many other real-world applications which can be modeled as a system of linear inequalities.