Matrix Product (GEMM) Performance Data from GPUs

Joubert, Wayne; Palmer, Eric

doi:10.13139/OLCF/1819195

Title: Matrix Product (GEMM) Performance Data from GPUs

Dataset
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Abstract

Timing data for mixed precision GEMM matrix product operations on several GPU models, including NVIDIA V100 and A100, AMD MI100 and Intel P580. Also data from machine learning model training on this data using Scikit-learn.

Authors:

Joubert, Wayne; Palmer, Eric

ORNL-OLCF

Publication Date:: 2021-09-09

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Office of Science (SC); Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

Subject:: 97 MATHEMATICS AND COMPUTING; GEMM matrix product operations; GPU Graphical Processing Units; computer system benchmarking

OSTI Identifier:: 1819195

DOI:: https://doi.org/10.13139/OLCF/1819195

Citation Formats


                    Joubert, Wayne, and Palmer, Eric. Matrix Product (GEMM) Performance Data from GPUs.  United States: N. p., 2021. 
        Web.  doi:10.13139/OLCF/1819195.

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                    Joubert, Wayne, & Palmer, Eric. Matrix Product (GEMM) Performance Data from GPUs.  United States.  doi:https://doi.org/10.13139/OLCF/1819195

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                    Joubert, Wayne, and Palmer, Eric. 2021.  
"Matrix Product (GEMM) Performance Data from GPUs".  United States.  doi:https://doi.org/10.13139/OLCF/1819195.  https://www.osti.gov/servlets/purl/1819195. Pub date:Thu Sep 09 00:00:00 EDT 2021

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@article{osti_1819195,

  title        = {Matrix Product (GEMM) Performance Data from GPUs},

  author       = {Joubert, Wayne and Palmer, Eric},

  abstractNote = {Timing data for mixed precision GEMM matrix product operations on several GPU models, including NVIDIA V100 and A100, AMD MI100 and Intel P580. Also data from machine learning model training on this data using Scikit-learn.},

  doi          = {10.13139/OLCF/1819195},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {2021},

  month        = {9}

}

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DOI: https://doi.org/10.13139/OLCF/1819195

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