Browsing by Subject "Image-space parallelization"

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Open Access
Exploiting replicated data for communication load balancing in image-space parallel direct volume rendering of unstructured grids
(2009) Okuyan, Erkan
The focus of this work is on parallel volume rendering applications in which renderings with different parameters are successively repeated over the same dataset. The only reason for inter-task interaction is the existence of data primitives that are inputs to several tasks. Both computational structure and expected task execution times may change during successive rendering instances. Change in computational structure means change in the data primitive requirements of tasks. Since the individual processors of a parallel system have a limited storage capacity, we can reserve a limited amount of storage for holding replicas at each processor. For the parallelization of a particular rendering instance, the remapping model should utilize the replication pattern of the previous rendering instance(s) for reducing the communication overhead due to the data replication requirement of the current rendering instance. We propose a two-phase model for solving this problem. The hypergraphpartitioning-based model proposed for the first phase aims to minimize the total message volume that will be incurred due to the replication/migration of input data while maintaining balance on computational and receive-volume loads of processors. The network-flow-based model proposed for the second phase aims to minimize the maximum message volume handled by processors via utilizing the flexibility in assigning send-communication tasks to processors, which is introduced by data replication. The validity of our proposed model is verified on image-space parallelization of a direct volume rendering algorithm.
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.