2-3-4 Decomposition Method for Large-Scale Parallel Image Composition with Arbitrary Number of Nodes
Jorji Nonaka, Chongke Bi, Kenji Ono, Masahiro Fujita · International Conference on Systems · 2014
Visual data exploration helps users to get better insight into their data and has been considered an indispensable tool for computational scientists. Sort-last parallel rendering is a proven approach for large-scale scientific visualization however it requires a costly parallel image composition at the final stage. Since it requires interprocess communication among the entire nodes, it usually dominates the total cost of a parallel rendering process. Efficient image composition algorithms for power-of-two number of nodes have already been proposed so far, however when handling non power-of-two number of nodes, an additional processing is required causing performance penalty. The simplest way is to execute this additional processing in the initial stage, or in parts, during the entire parallel image composition process. The latter approach causes less performance penalty, however since it adds performance penalty at every stage of parallel image composition, thus it can suffer in a large-scale image composition where tens, or even hundreds, of thousands of nodes can be involved. In this paper, we propose a decomposition approach, for non-power-of-two number of nodes, named 2-3-4 Decomposition. It works by generating exactly power-of-two number of groups of 2, 3 or 4 nodes. Therefore, by compositing independently each of these groups, at the end, we will obtain a power-of-two number of nodes making it easy to combine with any of the existing image composition algorithms for power-of-two number of nodes. It works as a pre-processing and the performance penalty is limited to the overhead of compositing three or four images. This performance penalty can be further reduced depending on the image compositing algorithm to be applied in the next stage. Our experimental results have shown promising results making this method a potential candidate for large-scale image composition with arbitrary number of nodes.