See the published version of this article at Advances in Aerodynamics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
mSwap: A Large-Scale Image-Compositing Method with Optimal m-ary Tree
Chongke Bi
Tianjin University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4324-8028
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Advances in Aerodynamics.
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.
Keywords
Parallel rendering, Sort-last, Image compositing, mSwap, m-ary Tree
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.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 22 Oct, 2020
Published
On 27 Jan, 2021
No community comments so far
Editorial decision: Accept
On 15 Oct, 2020
Editor assigned
On 13 Oct, 2020
Submission checks complete
On 12 Oct, 2020
Editor invited
On 12 Oct, 2020
No comments provided
Editorial decision: Minor revision
On 22 Sep, 2020
Review #4 received
Received 21 Sep, 2020
Review #3 received
Received 18 Sep, 2020
Review #2 received
Received 18 Sep, 2020
Reviewer #4 agreed
On 10 Sep, 2020
Review #1 received
Received 10 Sep, 2020
Reviewer #2 agreed
On 09 Sep, 2020
Reviewer #3 agreed
On 09 Sep, 2020
Reviewer #1 agreed
On 27 Aug, 2020
Reviewers invited
Invitations sent on 25 Aug, 2020
Editor assigned
On 20 Aug, 2020
First submitted
On 19 Aug, 2020
Submission checks complete
On 19 Aug, 2020
Editor invited
On 19 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Aeronautics and Astronautics
Learn more about our company.
This preprint is under consideration at Advances in Aerodynamics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
mSwap: A Large-Scale Image-Compositing Method with Optimal m-ary Tree
Chongke Bi
Tianjin University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4324-8028
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 22 Oct, 2020
Published
On 27 Jan, 2021
No community comments so far
Editorial decision: Accept
On 15 Oct, 2020
Editor assigned
On 13 Oct, 2020
Submission checks complete
On 12 Oct, 2020
Editor invited
On 12 Oct, 2020
No comments provided
Editorial decision: Minor revision
On 22 Sep, 2020
Review #4 received
Received 21 Sep, 2020
Review #3 received
Received 18 Sep, 2020
Review #2 received
Received 18 Sep, 2020
Reviewer #4 agreed
On 10 Sep, 2020
Review #1 received
Received 10 Sep, 2020
Reviewer #2 agreed
On 09 Sep, 2020
Reviewer #3 agreed
On 09 Sep, 2020
Reviewer #1 agreed
On 27 Aug, 2020
Reviewers invited
Invitations sent on 25 Aug, 2020
Editor assigned
On 20 Aug, 2020
First submitted
On 19 Aug, 2020
Submission checks complete
On 19 Aug, 2020
Editor invited
On 19 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Aeronautics and Astronautics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Advances in Aerodynamics.
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.