Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction

Xiao Wang; Venkatesh Sridhar; Zahra Ronaghi; Rollin Thomas; Jack Deslippe; Dilworth Parkinson; Gregery T. Buzzard; Samuel P. Midkiff; Charles A. Bouman; Simon K. Warfield

doi:10.1145/3295500.3356142

Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction

Xiao Wang
, Venkatesh Sridhar
, Zahra Ronaghi
, Rollin Thomas
, Jack Deslippe
, Dilworth Parkinson
, Gregery T. Buzzard
, Samuel P. Midkiff
, Charles A. Bouman
, Simon K. Warfield

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Computed tomography (CT) image reconstruction is a crucial technique for many imaging applications. Among various reconstruction methods, Model-Based Iterative Reconstruction (MBIR) enables super-resolution with superior image quality. MBIR, however, has a high memory requirement that limits the achievable image resolution, and the parallelization for MBIR suffers from limited scalability. In this paper, we propose Asynchronous Consensus MBIR (AC-MBIR) that uses Consensus Equilibrium (CE) to provide a super-resolution algorithm with a small memory footprint, low communication overhead and a high scalability. Super-resolution experiments show that AC-MBIR has a 6.8 times smaller memory footprint and 16 times more scalability, compared with the state-of-the-art MBIR implementation, and maintains a 100% strong scaling efficiency at 146880 cores. In addition, AC-MBIR achieves an average bandwidth of 3.5 petabytes per second at 587520 cores.

Original language	English
Title of host publication	Proceedings of SC 2019
Subtitle of host publication	The International Conference for High Performance Computing, Networking, Storage and Analysis
Publisher	IEEE Computer Society
ISBN (Electronic)	9781450362290
DOIs	https://doi.org/10.1145/3295500.3356142
State	Published - Nov 17 2019
Externally published	Yes
Event	2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019 - Denver, United States Duration: Nov 17 2019 → Nov 22 2019

Publication series

Name	International Conference for High Performance Computing, Networking, Storage and Analysis, SC
ISSN (Print)	2167-4329
ISSN (Electronic)	2167-4337

Conference

Conference	2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019
Country/Territory	United States
City	Denver
Period	11/17/19 → 11/22/19

Funding

The authors would like to thank the National Energy Research Scientific Computing Center (NERSC) for providing supercomputing resources under contract No. DE-AC02-05CH11231. This research was supported by the National Science Foundation (NSF) under Award CCF-1763896. Additional support was provided by the DHS ALERT Center for Excellence under Grant Award 2013-ST-061-ED0001.

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10.1145/3295500.3356142

Cite this

Wang, X., Sridhar, V., Ronaghi, Z., Thomas, R., Deslippe, J., Parkinson, D., Buzzard, G. T., Midkiff, S. P., Bouman, C. A., & Warfield, S. K. (2019). Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction. In Proceedings of SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis Article a86 (International Conference for High Performance Computing, Networking, Storage and Analysis, SC). IEEE Computer Society. https://doi.org/10.1145/3295500.3356142

Wang, Xiao ; Sridhar, Venkatesh ; Ronaghi, Zahra et al. / Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction. Proceedings of SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society, 2019. (International Conference for High Performance Computing, Networking, Storage and Analysis, SC).

@inproceedings{fdb30546ccc748b8bfade8947297d967,

title = "Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction",

abstract = "Computed tomography (CT) image reconstruction is a crucial technique for many imaging applications. Among various reconstruction methods, Model-Based Iterative Reconstruction (MBIR) enables super-resolution with superior image quality. MBIR, however, has a high memory requirement that limits the achievable image resolution, and the parallelization for MBIR suffers from limited scalability. In this paper, we propose Asynchronous Consensus MBIR (AC-MBIR) that uses Consensus Equilibrium (CE) to provide a super-resolution algorithm with a small memory footprint, low communication overhead and a high scalability. Super-resolution experiments show that AC-MBIR has a 6.8 times smaller memory footprint and 16 times more scalability, compared with the state-of-the-art MBIR implementation, and maintains a 100\% strong scaling efficiency at 146880 cores. In addition, AC-MBIR achieves an average bandwidth of 3.5 petabytes per second at 587520 cores.",

author = "Xiao Wang and Venkatesh Sridhar and Zahra Ronaghi and Rollin Thomas and Jack Deslippe and Dilworth Parkinson and Buzzard, \{Gregery T.\} and Midkiff, \{Samuel P.\} and Bouman, \{Charles A.\} and Warfield, \{Simon K.\}",

note = "Publisher Copyright: {\textcopyright} 2019 ACM.; 2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019 ; Conference date: 17-11-2019 Through 22-11-2019",

year = "2019",

month = nov,

day = "17",

doi = "10.1145/3295500.3356142",

language = "English",

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Wang, X, Sridhar, V, Ronaghi, Z, Thomas, R, Deslippe, J, Parkinson, D, Buzzard, GT, Midkiff, SP, Bouman, CA & Warfield, SK 2019, Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction. in Proceedings of SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis., a86, International Conference for High Performance Computing, Networking, Storage and Analysis, SC, IEEE Computer Society, 2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019, Denver, United States, 11/17/19. https://doi.org/10.1145/3295500.3356142

Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction. / Wang, Xiao; Sridhar, Venkatesh; Ronaghi, Zahra et al.
Proceedings of SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society, 2019. a86 (International Conference for High Performance Computing, Networking, Storage and Analysis, SC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction

AU - Wang, Xiao

AU - Sridhar, Venkatesh

AU - Ronaghi, Zahra

AU - Thomas, Rollin

AU - Deslippe, Jack

AU - Parkinson, Dilworth

AU - Buzzard, Gregery T.

AU - Midkiff, Samuel P.

AU - Bouman, Charles A.

AU - Warfield, Simon K.

PY - 2019/11/17

Y1 - 2019/11/17

N2 - Computed tomography (CT) image reconstruction is a crucial technique for many imaging applications. Among various reconstruction methods, Model-Based Iterative Reconstruction (MBIR) enables super-resolution with superior image quality. MBIR, however, has a high memory requirement that limits the achievable image resolution, and the parallelization for MBIR suffers from limited scalability. In this paper, we propose Asynchronous Consensus MBIR (AC-MBIR) that uses Consensus Equilibrium (CE) to provide a super-resolution algorithm with a small memory footprint, low communication overhead and a high scalability. Super-resolution experiments show that AC-MBIR has a 6.8 times smaller memory footprint and 16 times more scalability, compared with the state-of-the-art MBIR implementation, and maintains a 100% strong scaling efficiency at 146880 cores. In addition, AC-MBIR achieves an average bandwidth of 3.5 petabytes per second at 587520 cores.

AB - Computed tomography (CT) image reconstruction is a crucial technique for many imaging applications. Among various reconstruction methods, Model-Based Iterative Reconstruction (MBIR) enables super-resolution with superior image quality. MBIR, however, has a high memory requirement that limits the achievable image resolution, and the parallelization for MBIR suffers from limited scalability. In this paper, we propose Asynchronous Consensus MBIR (AC-MBIR) that uses Consensus Equilibrium (CE) to provide a super-resolution algorithm with a small memory footprint, low communication overhead and a high scalability. Super-resolution experiments show that AC-MBIR has a 6.8 times smaller memory footprint and 16 times more scalability, compared with the state-of-the-art MBIR implementation, and maintains a 100% strong scaling efficiency at 146880 cores. In addition, AC-MBIR achieves an average bandwidth of 3.5 petabytes per second at 587520 cores.

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M3 - Conference contribution

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Wang X, Sridhar V, Ronaghi Z, Thomas R, Deslippe J, Parkinson D et al. Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction. In Proceedings of SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society. 2019. a86. (International Conference for High Performance Computing, Networking, Storage and Analysis, SC). doi: 10.1145/3295500.3356142

Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction

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