Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system

doi:10.1002/cpe.5547

Title: Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system

Authors:

^[1];

^[1]; Williams, Samuel ^[2]

National Energy Research Scientific Computing Center (NERSC) Lawrence Berkeley National Laboratory Berkeley California
Computational Research Division (CRD) Lawrence Berkeley National Laboratory Berkeley California

Publication Date:: 2019-11-12

Sponsoring Org.:: USDOE

OSTI Identifier:: 1574050

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Concurrency and Computation. Practice and Experience

Additional Journal Information:: Journal Name: Concurrency and Computation. Practice and Experience Journal Volume: 32 Journal Issue: 20; Journal ID: ISSN 1532-0626

Publisher:: Wiley Blackwell (John Wiley & Sons)

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Yang, Charlene, Kurth, Thorsten, and Williams, Samuel. Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system.  United Kingdom: N. p., 2019. 
        Web.  doi:10.1002/cpe.5547.

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                    Yang, Charlene, Kurth, Thorsten, & Williams, Samuel. Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system.  United Kingdom.  doi:10.1002/cpe.5547.

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                    Yang, Charlene, Kurth, Thorsten, and Williams, Samuel. Tue .  
        "Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system".  United Kingdom.  doi:10.1002/cpe.5547.

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@article{osti_1574050,

  title        = {Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system},

  author       = {Yang, Charlene and Kurth, Thorsten and Williams, Samuel},

  abstractNote = {},

  doi          = {10.1002/cpe.5547},

  journal      = {Concurrency and Computation. Practice and Experience},

  number       = 20,

  volume       = 32,

  place        = {United Kingdom},

  year         = {2019},

  month        = {11}

}

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DOI: 10.1002/cpe.5547

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Works referenced in this record:

An Empirical Roofline Methodology for Quantitatively Assessing Performance Portability
conference, November 2018

Yang, Charlene; Gayatri, Rahulkumar; Kurth, Thorsten
2018 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC)
DOI: 10.1109/P3HPC.2018.00005

Deep Residual Learning for Image Recognition
conference, June 2016

He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI: 10.1109/CVPR.2016.90

Roofline: an insightful visual performance model for multicore architectures
journal, April 2009

Williams, Samuel; Waterman, Andrew; Patterson, David
Communications of the ACM, Vol. 52, Issue 4
DOI: 10.1145/1498765.1498785

Electron self-energy calculation using a general multi-pole approximation
journal, April 2003

Soininen, J. A.; Rehr, J. J.; Shirley, Eric L.
Journal of Physics: Condensed Matter, Vol. 15, Issue 17
DOI: 10.1088/0953-8984/15/17/312

Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis
journal, August 2019

Ben-Nun, Tal; Hoefler, Torsten
ACM Computing Surveys, Vol. 52, Issue 4
DOI: 10.1145/3320060

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Similar Records

Title: Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system

Citation Formats

An Empirical Roofline Methodology for Quantitatively Assessing Performance Portability conference, November 2018

Deep Residual Learning for Image Recognition conference, June 2016

Roofline: an insightful visual performance model for multicore architectures journal, April 2009

Electron self-energy calculation using a general multi-pole approximation journal, April 2003

Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis journal, August 2019

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conference, November 2018

Deep Residual Learning for Image Recognition
conference, June 2016

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journal, April 2009

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journal, April 2003

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journal, August 2019