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.

中文翻译：

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mSwap：具有最佳m元树的大规模图像合成方法

随着计算能力的提高，大规模仿真已经在空气动力学中生成了大量数据。最后排序并行渲染是用于大规模科学可视化的最经典的图像合成方法。但是，在图像合成阶段，后排序方法可能会在大型处理器上遇到可伸缩性问题。现有的图像合成算法在某些情况下往往表现良好。例如，直接发送适用于中小型规模。仅当k值合适时，Radix-k才能获得良好的性能。在本文中，我们提出了一种名为mSwap的新颖方法，用于空气动力学的科学可视化，该方法使用最佳处理器规模来确保其最佳性能。mSwap将可与（m，k）表一起使用的处理器分组，其中记录了m（每组子组中的处理器数量）和k（每组中的处理器数量）的最佳组合。然后，在每个组中，使用m元树对图像进行合成，以减少处理器之间的通信。最后，在不同组之间合成图像以生成最终图像。我们的mSwap方法的性能和可伸缩性通过数千个处理器的实验得以证明。