research-article

An image compositing solution at scale

Authors:

Kenneth Moreland,

Wesley Kendall,

Jian HuangAuthors Info & Claims

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 25, Pages 1 - 10

https://doi.org/10.1145/2063384.2063417

Published: 12 November 2011 Publication History

Abstract

The only proven method for performing distributed-memory parallel rendering at large scales, tens of thousands of nodes, is a class of algorithms called sort last. The fundamental operation of sort-last parallel rendering is an image composite, which combines a collection of images generated independently on each node into a single blended image. Over the years numerous image compositing algorithms have been proposed as well as several enhancements and rendering modes to these core algorithms. However, the testing of these image compositing algorithms has been with an arbitrary set of enhancements, if any are applied at all. In this paper we take a leading production-quality image-compositing framework, IceT, and use it as a testing framework for the leading image compositing algorithms of today. As we scale IceT to ever increasing job sizes, we consider the image compositing systems holistically, incorporate numerous optimizations, and discover several improvements to the process never considered before. We conclude by demonstrating our solution on 64K cores of the Intrepid Blue-Gene/P at Argonne National Laboratories.

References

[1]

VisIt user's manual. Technical Report UCRL-SM-220449, Lawrence Livermore National Laboratory, October 2005.

[2]

J. Ahrens and J. Painter. Efficient sort-last rendering using compression-based image compositing. In Second Eurographics Workshop on Parallel Graphics and Visualization, September 1998.

[3]

A. Cedilnik, B. Geveci, K. Moreland, J. Ahrens, and J. Farve. Remote large data visualization in the ParaView framework. In Eurographics Parallel Graphics and Visualization 2006, pages 163--170, May 2006.

Digital Library

[4]

A. Chan, W. Gropp, and E. Lusk. An efficient format for nearly constant-time access to arbitrary time intervals in large trace files. Scientific Programming, 16(2--3):155--165, 2008.

Digital Library

[5]

H. Childs. Architectural challenges and solutions for petascale postprocessing. Journal of Physics: Conference Series, 78(012012), 2007. DOI=10.1088/1742-6596/78/1/012012.

[6]

H. Childs, D. Pugmire, S. Ahern, B. Whitlock, M. Howison, Prabhat, G. H. Weber, and E. W. Bethel. Extreme scaling of production visualization software on diverse architectures. IEEE Computer Graphics and Applications, 30(3):22--31, May/June 2010. DOI=10.1109/MCG.2010.51.

Digital Library

[7]

M. Howison, E. Bethel, and H. Childs. MPI-hybrid parallelism for volume rendering on large, multi-core systems. In Eurographics Symposium on Parallel Graphics and Visualization, May 2010.

Digital Library

[8]

W. Kendall, T. Peterka, J. Huang, H.-W. Shen, and R. Ross. Accelerating and benchmarking radix-k image compositing at large scale. In Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), May 2010.

Digital Library

[9]

S. M. LaValle. Planning Algorithms. Cambridge University Press, 2006.

Digital Library

[10]

K.-L. Ma. In situ visualization at extreme scale: Challenges and opportunities. IEEE Computer Graphics and Applications, 29(6):14--19, November/December 2009. DOI=10.1109/MCG.2009.120.

Digital Library

[11]

K.-L. Ma, J. S. Painter, C. D. Hansen, and M. F. Krogh. A data distributed, parallel algorithm for ray-traced volume rendering. In Proceedings of the 1993 Symposium on Parallel Rendering, pages 15--22, 1993. DOI=10.1145/166181.166183.

Digital Library

[12]

K.-L. Ma, J. S. Painter, C. D. Hansen, and M. F. Krogh. Parallel volume rendering using binary-swap compositing. IEEE Computer Graphics and Applications, 14(4):59--68, July/August 1994. DOI=10.1109/38.291532.

Digital Library

[13]

K.-L. Ma, C. Wang, H. Yu, K. Moreland, J. Huang, and R. Ross. Next-generation visualization technologies: Enabling discoveries at extreme scale. SciDAC Review, (12):12--21, Spring 2009.

[14]

S. Molnar, M. Cox, D. Ellsworth, and H. Fuchs. A sorting classification of parallel rendering. IEEE Computer Graphics and Applications, pages 23--32, July 1994.

Digital Library

[15]

K. Moreland. IceT users' guide and reference, version 2.0. Technical Report SAND2010-7451, Sandia National Laboratories, January 2011.

[16]

K. Moreland, B. Wylie, and C. Pavlakos. Sort-last parallel rendering for viewing extremely large data sets on tile displays. In Proceedings of the IEEE 2001 Symposium on Parallel and Large-Data Visualization and Graphics, pages 85--92, October 2001.

Digital Library

[17]

U. Neumann. Parallel volume-rendering algorithm performance on mesh-connected multicomputers. In Proceedings of the 1993 Symposium on Parallel Rendering, pages 97--104, 1993. DOI=10.1145/166181.166196.

Digital Library

[18]

U. Neumann. Communication costs for parallel volume-rendering algorithms. IEEE Computer Graphics and Applications, 14(4):49--58, July 1994. DOI=10.1109/38.291531.

Digital Library

[19]

B. Nouanesengsy, J. Ahrens, J. Woodring, and H.-W. Shen. Revisiting parallel rendering for shared memory machines. In Eurographics Symposium on Parallel Graphics and Visualization 2011, April 2011.

Digital Library

[20]

T. Peterka, D. Goodell, R. Ross, H.-W. Shen, and R. Thakur. A configurable algorithm for parallel image-compositing applications. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (SC '09), November 2009. DOI=10.1145/1654059.1654064.

Digital Library

[21]

T. Peterka, R. Ross, H. Yu, K.-L. Ma, W. Kendall, and J. Huang. Assessing improvements in the parallel volume rendering pipeline at large scale. In Proceedings of SC 08 Ultrascale Visualization Workshop, 2008.

[22]

T. Peterka, H. Yu, R. Ross, K.-L. Ma, and R. Latham. End-to-end study of parallel volume rendering on the IBM Blue Gene/P. In International Conference on Parallel Processing (ICPP '09), pages 566--573, September 2009. DOI=10.1109/ICPP.2009.27.

Digital Library

[23]

E. Reinhard and C. Hansen. A comparison of parallel compositing techniques on shared memory architectures. In Proceedings of the Third Eurographics Workshop on Parallel Graphics and Visualization, pages 115--123, September 2000.

[24]

R. Samanta, T. Funkhouser, and K. Li. Parallel rendering with k-way replication. In 2001 Symposium on Parallel and Large-Data Visualization and Graphics, pages 75--84, October 2001.

Digital Library

[25]

R. Samanta, T. Funkhouser, K. Li, and J. P. Singh. Hybrid sort-first and sort-last parallel rendering with a cluster of PCs. In Proceedings of the ACM SIGGRAPH/Eurographics Workshop on Graphics Hardware, pages 97--108, 2000.

Digital Library

[26]

C. Sosa and B. Knudson. IBM System Blue Gene Solution: Blue Gene/P Application Development. IBM Redbooks, fourth edition, August 2009. ISBN 0738433330.

[27]

A. H. Squillacote. The ParaView Guide: A Parallel Visualization Application. Kitware Inc., 2007. ISBN 1-930934-21-1.

[28]

A. Stompel, K.-L. Ma, E. B. Lum, J. Ahrens, and J. Patchett. SLIC: Scheduled linear image compositing for parallel volume rendering. In Proceedings IEEE Symposium on Parallel and Large-Data Visualization and Graphics (PVG 2003), pages 33--40, October 2003.

Digital Library

[29]

A. Takeuchi, F. Ino, and K. Hagihara. An improvement on binary-swap compositing for sort-last parallel rendering. In Proceedings of the 2003 ACM Symposium on Applied Computing, pages 996--1002, 2003. DOI=10.1145/952532.952728.

Digital Library

[30]

T. Tu, H. Yu, L. Ramirez-Guzman, J. Bielak, O. Ghattas, K.-L. Ma, and D. R. O'Hallaron. From mesh generation to scientific visualization: An end-to-end approach to parallel supercomputing. In Proceedings of the 2006 ACM/IEEE conference on Supercomputing, 2006.

Digital Library

[31]

B. Wylie, C. Pavlakos, V. Lewis, and K. Moreland. Scalable rendering on PC clusters. IEEE Computer Graphics and Applications, 21(4):62--70, July/August 2001.

Digital Library

[32]

D.-L. Yang, J.-C. Yu, and Y.-C. Chung. Efficient compositing methods for the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. In 1999 International Conference on Parallel Processing, pages 200--207, 1999.

Digital Library

[33]

H. Yu, C. Wang, R. W. Grout, J. H. Chen, and K.-L. Ma. In situ visualization for large-scale combustion simulations. IEEE Computer Graphics and Applications, 30(3):45--57, May/June 2010. DOI=10.1109/MCG.2010.55.

Digital Library

[34]

H. Yu, C. Wang, and K.-L. Ma. Massively parallel volume rendering using 2-3 swap image compositing. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, November 2008. DOI=10.1145/1413370.1413419.

Digital Library

Cited By

Pugmire DMoreland KAthawale THammer JHuang J(2024)Top Research Challenges and Opportunities for Near Real-Time Extreme-Scale Visualization of Scientific Data2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678727(1-6)Online publication date: 16-Sep-2024
https://doi.org/10.1109/e-Science62913.2024.10678727
Wald IZellmann SAmstutz JWu QGriffin KJaros MWesner S(2024)Standardized Data-Parallel Rendering Using ANARI2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)10.1109/LDAV64567.2024.00013(23-32)Online publication date: 13-Oct-2024
https://doi.org/10.1109/LDAV64567.2024.00013
Bruder VLarsen MErtl TChilds HFrey S(2023)A Hybrid in Situ Approach for Cost Efficient Image Database GenerationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.316959029:9(3788-3798)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TVCG.2022.3169590
Show More Cited By

Index Terms

An image compositing solution at scale
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
  2. Parallel computing methodologies

Recommendations

An improvement on binary-swap compositing for sort-last parallel rendering
SAC '03: Proceedings of the 2003 ACM symposium on Applied computing

Sort-last parallel rendering is a good rendering scheme on distributed memory multiprocessors. This paper presents an improvement on the binary-swap (BS) method, which is an efficient image compositing algorithm for sort-last parallel rendering. Our ...
An improved binary-swap compositing for sort-last parallel rendering on distributed memory multiprocessors
Special issue: Parallel and distributed scientific and engineering computing

Sort-last parallel rendering is a good rendering scheme on distributed memory multiprocessors. This paper presents an improvement on the binary-swap (BS) method, which is an efficient image compositing algorithm for sort-last parallel rendering. Our ...
NUMA-aware image compositing on multi-GPU platform

Sort-last parallel rendering is widely used. Recent GPU developments mean that a PC equipped with multiple GPUs is a viable alternative to a high-cost supercomputer: the Fermi architecture of a single GPU supports uniform virtual addressing, providing a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis

November 2011

866 pages

ISBN:9781450307710

DOI:10.1145/2063384

Conference Chair:
Scott Lathrop
University of Chicago
,
Program Chairs:
Jim Costa
Sandia National Laboratories
,
William Kramer
National Center for Supercomputing Applications

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Argonne National Laboratory, Office of Science

Conference

SC '11

Sponsor:

SIGARCH
IEEE-CS

SC '11: International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 18, 2011

Washington, Seattle

Acceptance Rates

SC '11 Paper Acceptance Rate 74 of 352 submissions, 21%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

45
Total Citations
View Citations
426
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pugmire DMoreland KAthawale THammer JHuang J(2024)Top Research Challenges and Opportunities for Near Real-Time Extreme-Scale Visualization of Scientific Data2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678727(1-6)Online publication date: 16-Sep-2024
https://doi.org/10.1109/e-Science62913.2024.10678727
Wald IZellmann SAmstutz JWu QGriffin KJaros MWesner S(2024)Standardized Data-Parallel Rendering Using ANARI2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)10.1109/LDAV64567.2024.00013(23-32)Online publication date: 13-Oct-2024
https://doi.org/10.1109/LDAV64567.2024.00013
Bruder VLarsen MErtl TChilds HFrey S(2023)A Hybrid in Situ Approach for Cost Efficient Image Database GenerationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.316959029:9(3788-3798)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TVCG.2022.3169590
Zellmann SWald IBarbosa JDermic SSahistan AGudukbay U(2022)Hybrid Image-/Data-Parallel Rendering Using Island Parallelism2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV)10.1109/LDAV57265.2022.9966396(1-10)Online publication date: 16-Oct-2022
https://doi.org/10.1109/LDAV57265.2022.9966396
Frey SBruder VFrieß FGralka PRau TErtl TReina G(2022)Parameter Adaptation In Situ: Design Impacts and Trade-OffsIn Situ Visualization for Computational Science10.1007/978-3-030-81627-8_8(159-182)Online publication date: 5-May-2022
https://doi.org/10.1007/978-3-030-81627-8_8
Usher WAmstutz JGünther JKnoll AJohnson GBrownlee CHota ACherniak BRowley TJeffers JPascucci V(2022)Scalable CPU Ray Tracing for In Situ Visualization Using OSPRayIn Situ Visualization for Computational Science10.1007/978-3-030-81627-8_16(353-374)Online publication date: 5-May-2022
https://doi.org/10.1007/978-3-030-81627-8_16
Krämer AMaier BRau THuber FKlotz TErtl TGöddeke DMehl MReina GRöhrle O(2022)Multi-physics Multi-scale HPC Simulations of Skeletal MusclesHigh Performance Computing in Science and Engineering '2010.1007/978-3-030-80602-6_13(185-203)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-80602-6_13
Lipinksi RMoreland KPapka MMarrinan T(2021)GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV)10.1109/LDAV53230.2021.00012(43-52)Online publication date: Oct-2021
https://doi.org/10.1109/LDAV53230.2021.00012
Atzori MKöpp WChien SMassaro DMallor FPeplinski ARezaei MJansson NMarkidis SVinuesa RLaure ESchlatter PWeinkauf T(2021)In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView CatalystThe Journal of Supercomputing10.1007/s11227-021-03990-3Online publication date: 2-Aug-2021
https://doi.org/10.1007/s11227-021-03990-3
Han MWald IUsher WMorrical NKnoll APascucci VJohnson C(2020)A Virtual Frame Buffer Abstraction for Parallel Rendering of Large Tiled Display Walls2020 IEEE Visualization Conference (VIS)10.1109/VIS47514.2020.00009(11-15)Online publication date: Oct-2020
https://doi.org/10.1109/VIS47514.2020.00009
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents