Browsing by Subject "Remapping"

Now showing 1 - 4 of 4

Open Access
Adaptive decomposition and remapping algorithms for object-space-parallel direct volume rendering of unstructured grids
(Academic Press, 2007-01) Aykanat, Cevdet; Cambazoglu, B. B.; Findik, F.; Kurc, T.
Object space (OS) parallelization of an efficient direct volume rendering algorithm for unstructured grids on distributed-memory architectures is investigated. The adaptive OS decomposition problem is modeled as a graph partitioning (GP) problem using an efficient and highly accurate estimation scheme for view-dependent node and edge weighting. In the proposed model, minimizing the cutsize corresponds to minimizing the parallelization overhead due to the data communication and redundant computation/storage while maintaining the GP balance constraint corresponds to maintaining the computational load balance in parallel rendering. A GP-based, view-independent cell clustering scheme is introduced to induce more tractable view-dependent computational graphs for successive visualizations. As another contribution, a graph-theoretical remapping model is proposed as a solution to the general remapping problem and is used in minimization of the cell-data migration overhead. The remapping tool RM-MeTiS is developed by modifying the GP tool MeTiS and is used in partitioning the remapping graphs. Experiments are conducted using benchmark datasets on a 28-node PC cluster to evaluate the performance of the proposed models. © 2006 Elsevier Inc. All rights reserved.
Open Access
A hypergraph-partitioning based remapping model for image-space parallel volume rendering
(2000) Cambazoğlu, Berkant Barla
Ray-casting is a popular direct volume rendering technique, used to explore the content of 3D data. Although this technique is capable of producing high quality visualizations, its slowness prevents the interactive use. The major method to overcome this speed limitation is parallelization. In this work, we investigate the image-space parallelization of ray-casting for distributed memory architectures. The most important issues in image-space parallelization are load balancing and minimization of the data redistribution overhead introduced at successive visualization instances. Load balancing in volume rendering requires the estimation of screen work load correctly. For this purpose, we tested three different load assignment schemes. Since the data used in this work is made up of unstructured tetrahedral grids, clusters of data were used instead of cells, for efficiency purposes. Two different cluster-processor distribution schemes are employed to see the effects of initial data distribution. The major contribution of the thesis comes at the hypergraph partitioning model proposed as a solution to the remapping problem. For this purpose, existing hypergraph partitioning tool PaToH is modified and used as a one-phase remapping tool. The model is tested on a Parsytec CC system and satisfactory results are obtained. Compared to the two-phase jagged partitioning model, our work incurs less preprocessing overhead. At comparable load imbalance values, our hypergraph partitioning model requires 25% less total volume of communication than jagged partitioning on the average.
Open Access
Hypergraph-partitioning-based remapping models for image-space-parallel direct volume rendering of unstructured grids
(Institute of Electrical and Electronics Engineers, 2007-07) Cambazoglu, B. B.; Aykanat, Cevdet
In this work, image-space-parallel direct volume rendering (DVR) of unstructured grids is investigated for distributed-memory architectures. A hypergraph-partitioning-based model is proposed for the adaptive screen partitioning problem in this context. The proposed model aims to balance the rendering loads of processors while trying to minimize the amount of data replication. In the parallel DVR framework we adopted, each data primitive is statically owned by its home processor, which is responsible from replicating its primitives on other processors. Two appropriate remapping models are proposed by enhancing the above model for use within this framework. These two remapping models aim to minimize the total volume of communication in data replication while balancing the rendering loads of processors. Based on the proposed models, a parallel DVR algorithm is developed. The experiments conducted on a PC cluster show that the proposed remapping models achieve better speedup values compared to the remapping models previously suggested for image-space-parallel DVR. © 2007 IEEE.
Open Access
Parallel direct volume rendering of unstructured grids based on object-space decomposition
(1997-10) Fındık, Ferit
This work investigates object-space (OS) parallelization of an efficient ray-casting based direct volume rendering algorithm (DVR) for unstructured grids on distributed-memory architectures. The key point for a successful parallelization is to find an OS decomposition which maintains the OS coherency and computational load balance as much as possible. The OS decomposition problem is modeled as a graph partitioning (GP) problem with correct view-dependent node and edge weighting. As the parallel visualizations of the results of parallel engineering simulations are performed on the same machine, OS decomposition, which is necessary for each visualization instance because of the changes in the computational structures of the successive parallel steps, constitutes a typical case of the general remapping problem. A GP-based model is proposed for the solution of the general remapping problem by constructing an augmented remapping graph. The remapping tool RM-MeTiS, developed by modifying and enhancing the original MeTiS package for partitioning the remapping graph, is successfully used in the purposed parallel DVR algorithm. An effective view-dependent cell-clustering scheme is introduced to induce more tractable contracted view-independent remapping graphs for successive visualizations. An efficient estimation scheme with high accuracy is proposed for view-dependent node and edge weighting of the remapping graph. Speedup values as high as 22 are obtained on a Parsytec CC system with 24 processors in the visualization of benchmark volumetric datasets and the proposed DVR algorithm seems to be linearly scalable according to the experimental results.