Scaling Deep Learning for Scientific Workloads on the #1 Summit Supercomputer. Brueckner, R. April, 2019.
Scaling Deep Learning for Scientific Workloads on the #1 Summit Supercomputer [link]Paper  abstract   bibtex   
Jack Wells from ORNL gave this talk at the GPU Technology Conference. "HPC centers have been traditionally configured for simulation workloads, but deep learning has been increasingly applied alongside simulation on scientific datasets. These frameworks do not always fit well with job schedulers, large parallel file systems, and MPI backends. We'll share benchmarks between native compiled versus containers on Power systems, like Summit, as well as best practices for deploying learning and models on HPC resources on scientific workflows."
@misc{brueckner_scaling_2019,
	title = {Scaling {Deep} {Learning} for {Scientific} {Workloads} on the \#1 {Summit} {Supercomputer}},
	url = {https://insidehpc.com/2019/04/scaling-deep-learning-for-scientific-workloads-on-the-1-summit-supercomputer/},
	abstract = {Jack Wells from ORNL gave this talk at the GPU Technology Conference. "HPC centers have been traditionally configured for simulation workloads, but deep learning has been increasingly applied alongside simulation on scientific datasets. These frameworks do not always fit well with job schedulers, large parallel file systems, and MPI backends. We'll share benchmarks between native compiled versus containers on Power systems, like Summit, as well as best practices for deploying learning and models on HPC resources on scientific workflows."},
	language = {en-US},
	urldate = {2019-11-28},
	journal = {insideHPC},
	author = {Brueckner, Rich},
	month = apr,
	year = {2019}
}

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