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@inproceedings{li_connecting_2019,
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  title = {Machine Learning Based Online Performance Prediction for Runtime Parallelization and Task Scheduling},
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  keywords = {Application software,Artificial Neural Networks,authoring languages,automatic performance prediction,Automatic Task Scheduling,Costs,Hardware,learning (artificial intelligence),Load management,machine learning,Machine learning,next-generation software,online performance prediction,online task partitioning,parallel programming,Parallel programming,Performance Prediction,pR framework,Predictive models,program execution,R language,Runtime,runtime parallelization,scheduling,Scheduling algorithm,scripting language,Scripting Languages,task analysis,task cost estimates,task scheduling,Testing}
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@inproceedings{li_metis_2018,
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  month = jul,
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  publisher = {{USENIX Association}},
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  journal = {Journal of the American Statistical Association},
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@article{li_performance_2015,
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@inproceedings{li_pipesgd_2018,
  title = {Pipe-{{SGD}}: {{A Decentralized Pipelined SGD Framework}} for {{Distributed Deep Net Training}}},
  booktitle = {Proceedings of the {{32Nd International Conference}} on {{Neural Information Processing Systems}}},
  author = {Li, Youjie and Yu, Mingchao and Li, Songze and Avestimehr, Salman and Kim, Nam Sung and Schwing, Alexander},
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  pages = {8056--8067},
  publisher = {{Curran Associates Inc.}},
  address = {{USA}},
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license: MIT

license_type: permissive

branch_name: refs/heads/master

gha_license_id: MIT

extension: bib