Abstract
With the increasing of computing ability, large-scale simulations have been generating massive amounts of data in aerodynamics. Sort-last parallel rendering is the most classical image compositing method for large-scale scientific 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 scientific 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.
Highlights
With the development of HPC systems, simulations could generate larger amounts of data than before in aerodynamics, which means more powerful techniques should be adopted for data analyzing
6 Conclusions In this paper, we propose an image compositing algorithm called mSwap, which aims to find the best case of processors according to the performance curves
In the past two decades, various of image compositing algorithms have been proposed to improve the performance of parallel rendering by increasing the utilization of processors
Summary
With the development of HPC systems, simulations could generate larger amounts of data than before in aerodynamics, which means more powerful techniques should be adopted for data analyzing. Visualization aimed at aerodynamics has been playing an important role in scientific discovery, especially in massive data discovery [1,2,3,4,5,6]. The increase of data challenges the traditional methods of scientific visualization. As defined by Molnar et al [7], all parallel rendering approaches are classified into three categories: sort-first, sort-middle and sort-last based on where the sort from object-space to screen space occurs. Among these classifications, sort-first and sort-last are more appropriate for parallel systems than sort-middle, because of their entire rendering pipeline.
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