mSwap: A Large-Scale Image-Compositing Method with Optimal m-ary Tree

Abstract

With the increasing of computing ability, large-scale science simulations have been generating massive amounts of data in aerodynamics. Sort-last parallel rendering is a proven approach for large-scale science visualization. However, in the stage of image compositing, the sort-last method may suffer from scalability problem on large-scale processors. Existing image compositing algorithms tend to perform well in certain situations. For instance, Direct Send is well on small and medium scale; Radix-k gets well performance only when the k -value is appropriate and so on. In this paper, we propose a novel method named mSwap for science visualization in aerodynamics, which uses the best scale of processors to make sure its performance at the best. mSwap groups the processors that we can use with a ( m, k ) table, which records the best combination of m (the number of processors in subgroup of each group) and k (the number of processors in each group). Then in each group, using a m-ary tree to composite the image for reducing the communication of processors. Finally, the image is composited between different groups to generate the final image. The performance and scalability of our mSwap method is demonstrated through experiments with thousands of processors.