mSwap: A Large-Scale Image-Compositing Method with Optimal m-ary Tree
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.
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Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Posted 22 Oct, 2020
On 27 Jan, 2021
On 15 Oct, 2020
On 13 Oct, 2020
On 12 Oct, 2020
On 12 Oct, 2020
On 22 Sep, 2020
Received 21 Sep, 2020
Received 18 Sep, 2020
Received 18 Sep, 2020
On 10 Sep, 2020
Received 10 Sep, 2020
On 09 Sep, 2020
On 09 Sep, 2020
On 27 Aug, 2020
Invitations sent on 25 Aug, 2020
On 20 Aug, 2020
On 19 Aug, 2020
On 19 Aug, 2020
On 19 Aug, 2020
mSwap: A Large-Scale Image-Compositing Method with Optimal m-ary Tree
Posted 22 Oct, 2020
On 27 Jan, 2021
On 15 Oct, 2020
On 13 Oct, 2020
On 12 Oct, 2020
On 12 Oct, 2020
On 22 Sep, 2020
Received 21 Sep, 2020
Received 18 Sep, 2020
Received 18 Sep, 2020
On 10 Sep, 2020
Received 10 Sep, 2020
On 09 Sep, 2020
On 09 Sep, 2020
On 27 Aug, 2020
Invitations sent on 25 Aug, 2020
On 20 Aug, 2020
On 19 Aug, 2020
On 19 Aug, 2020
On 19 Aug, 2020
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.