GPU Sharing with Triples Mode

There is a tremendous amount of interest in AI/ML technologies due to the proliferation of generative AI applications such as ChatGPT. This trend has significantly increased demand on GPUs, which are the workhorses for training AI models. Due to the high costs of GPUs and lacking supply, it has become of interest to optimize GPU usage in HPC centers. MIT Lincoln Laboratory Supercomputing Center (LLSC) has developed an easy-to-use GPU sharing feature supported by LLSC-developed tools including LLsub and LLMapReduce. This approach overcomes some of the limitations with the existing methods for GPU sharing. This allows users to apply GPU sharing whenever possible while they are developing their AI/ML models and/or doing parametric study on their AI models or executing other GPU applications. Based on our initial experimental results with GPU sharing, GPU sharing with triples mode is easy to use and achieved significant improvement in GPU usage and throughput performance for certain types of AI applications.

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Publication Details

Published
2024-09-23
DOI
https://doi.org/10.1109/hpec62836.2024.10938521
Citations
1
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.35
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article

GPU Sharing with Triples Mode

Albert Reuther, Michael E. Houle, William Arcand, Bill Bergeron et al.
1 citations
Distributed and Parallel Computing Systems
0.35
article

GPU Sharing with Triples Mode

Albert Reuther, Michael E. Houle, William Arcand, Bill Bergeron, Vijay Gadepally, Lauren Milechin, Michael T. Jones, Julie Mullen, Hayden Jananthan, Jeremy Kepner, David Bestor, Piotr Łuszczek, Matthew Hubbell, Chansup Byun, Guillermo Morales, Daniel Burrill, Andrew Prout, Antonio Rosa, Larry W. Anderson, Charles Yee, Peter Michaleas, Alexander Bonn
article en
1 citations

Abstract

There is a tremendous amount of interest in AI/ML technologies due to the proliferation of generative AI applications such as ChatGPT. This trend has significantly increased demand on GPUs, which are the workhorses for training AI models. Due to the high costs of GPUs and lacking supply, it has become of interest to optimize GPU usage in HPC centers. MIT Lincoln Laboratory Supercomputing Center (LLSC) has developed an easy-to-use GPU sharing feature supported by LLSC-developed tools including LLsub and LLMapReduce. This approach overcomes some of the limitations with the existing methods for GPU sharing. This allows users to apply GPU sharing whenever possible while they are developing their AI/ML models and/or doing parametric study on their AI models or executing other GPU applications. Based on our initial experimental results with GPU sharing, GPU sharing with triples mode is easy to use and achieved significant improvement in GPU usage and throughput performance for certain types of AI applications.

Massachusetts Institute of Technology (US)
Affordable and clean energy
Openalex Percentile: Top 33%
Distributed and Parallel Computing Systems
0.35
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