{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:41:57Z","timestamp":1771260117537,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1730628"],"award-info":[{"award-number":["CCF-1730628"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,21]]},"DOI":"10.1145\/3447786.3456245","type":"proceedings-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T06:18:11Z","timestamp":1619072291000},"page":"327-342","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["RubberBand"],"prefix":"10.1145","author":[{"given":"Ujval","family":"Misra","sequence":"first","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Liaw","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisa","family":"Dunlap","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Romil","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kirthevasan","family":"Kandasamy","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph E.","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ion","family":"Stoica","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey","family":"Tumanov","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Volume 1 (Long and Short Papers), pages 4171--4186, Minneapolis, Minnesota , June 2019 . Association for Computational Linguistics. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171--4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics."},{"key":"e_1_3_2_1_2_1","first-page":"1877","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown , Benjamin Mann , Nick Ryder , Melanie Subbiah , Jared D Kaplan , Prafulla Dhariwal , Arvind Neelakantan , Pranav Shyam , Girish Sastry , Amanda Askell , Sandhini Agarwal , Ariel Herbert-Voss , Gretchen Krueger , Tom Henighan , Rewon Child , Aditya Ramesh , Daniel Ziegler , Jeffrey Wu , Clemens Winter , Chris Hesse , Mark Chen , Eric Sigler , Mateusz Litwin , Scott Gray , Benjamin Chess , Jack Clark , Christopher Berner , Sam McCandlish , Alec Radford , Ilya Sutskever , and Dario Amodei . Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors , Advances in Neural Information Processing Systems , volume 33 , pages 1877 -- 1901 . Curran Associates, Inc. , 2020 . Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 1877--1901. Curran Associates, Inc., 2020."},{"key":"e_1_3_2_1_3_1","volume-title":"The cost of training nlp models: A concise overview","author":"Sharir Or","year":"2020","unstructured":"Or Sharir , Barak Peleg , and Yoav Shoham . The cost of training nlp models: A concise overview , 2020 . Or Sharir, Barak Peleg, and Yoav Shoham. The cost of training nlp models: A concise overview, 2020."},{"key":"e_1_3_2_1_4_1","volume-title":"Energy and policy considerations for deep learning in NLP. CoRR, abs\/1906.02243","author":"Strubell Emma","year":"2019","unstructured":"Emma Strubell , Ananya Ganesh , and Andrew McCallum . Energy and policy considerations for deep learning in NLP. CoRR, abs\/1906.02243 , 2019 . Emma Strubell, Ananya Ganesh, and Andrew McCallum. Energy and policy considerations for deep learning in NLP. CoRR, abs\/1906.02243, 2019."},{"key":"e_1_3_2_1_5_1","volume-title":"Tunability: Importance of hyperparameters of machine learning algorithms","author":"Probst Philipp","year":"2018","unstructured":"Philipp Probst , Bernd Bischl , and Anne-Laure Boulesteix . Tunability: Importance of hyperparameters of machine learning algorithms , 2018 . Philipp Probst, Bernd Bischl, and Anne-Laure Boulesteix. Tunability: Importance of hyperparameters of machine learning algorithms, 2018."},{"key":"e_1_3_2_1_6_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. ArXiv, abs\/1907.11692","author":"Liu Y.","year":"2019","unstructured":"Y. Liu , Myle Ott , Naman Goyal , Jingfei Du , Mandar Joshi , Danqi Chen , Omer Levy , M. Lewis , Luke Zettlemoyer , and Veselin Stoyanov . Roberta: A robustly optimized bert pretraining approach. ArXiv, abs\/1907.11692 , 2019 . Y. Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, M. Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. ArXiv, abs\/1907.11692, 2019."},{"key":"e_1_3_2_1_7_1","first-page":"240","volume-title":"Artificial Intelligence and Statistics","author":"Jamieson Kevin","year":"2016","unstructured":"Kevin Jamieson and Ameet Talwalkar . Non-stochastic best arm identification and hyperparameter optimization . In Artificial Intelligence and Statistics , pages 240 -- 248 , 2016 . Kevin Jamieson and Ameet Talwalkar. Non-stochastic best arm identification and hyperparameter optimization. In Artificial Intelligence and Statistics, pages 240--248, 2016."},{"key":"e_1_3_2_1_8_1","first-page":"1","volume-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","author":"Li Lisha","year":"2018","unstructured":"Lisha Li , Kevin Jamieson , Giulia DeSalvo , Afshin Rostamizadeh , and Ameet Talwalkar . Hyperband: A novel bandit-based approach to hyperparameter optimization . pages 1 -- 52 , 2018 . Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. Hyperband: A novel bandit-based approach to hyperparameter optimization. pages 1--52, 2018."},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of Workshop on ML Systems in The Thirty-second Annual Conference on Neural Information Processing Systems (NIPS)","author":"Li Liam","year":"2018","unstructured":"Liam Li , Kevin Jamieson , Afshin Rostamizadeh , Ekaterina Gonina , Moritz Hardt , Benjamin Recht , and Ameet Talwalkar . Massively parallel hyperparameter tuning . In Proceedings of Workshop on ML Systems in The Thirty-second Annual Conference on Neural Information Processing Systems (NIPS) , 2018 . Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar. Massively parallel hyperparameter tuning. In Proceedings of Workshop on ML Systems in The Thirty-second Annual Conference on Neural Information Processing Systems (NIPS), 2018."},{"key":"e_1_3_2_1_10_1","volume-title":"BOHB: robust and efficient hyperparameter optimization at scale. CoRR, abs\/1807.01774","author":"Falkner Stefan","year":"2018","unstructured":"Stefan Falkner , Aaron Klein , and Frank Hutter . BOHB: robust and efficient hyperparameter optimization at scale. CoRR, abs\/1807.01774 , 2018 . Stefan Falkner, Aaron Klein, and Frank Hutter. BOHB: robust and efficient hyperparameter optimization at scale. CoRR, abs\/1807.01774, 2018."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362719"},{"key":"e_1_3_2_1_12_1","volume-title":"A Generalized Framework for Population Based Training, page 1791--1799","author":"Li Ang","year":"2019","unstructured":"Ang Li , Ola Spyra , Sagi Perel , Valentin Dalibard , Max Jaderberg , Chenjie Gu , David Budden , Tim Harley , and Pramod Gupta . A Generalized Framework for Population Based Training, page 1791--1799 . Association for Computing Machinery , New York, NY, USA , 2019 . Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, and Pramod Gupta. A Generalized Framework for Population Based Training, page 1791--1799. Association for Computing Machinery, New York, NY, USA, 2019."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352020.3352024"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359646"},{"key":"e_1_3_2_1_15_1","volume-title":"5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net","author":"Qi Hang","year":"2017","unstructured":"Hang Qi , Evan R. Sparks , and Ameet Talwalkar . Paleo : A performance model for deep neural networks . In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net , 2017 . Hang Qi, Evan R. Sparks, and Ameet Talwalkar. Paleo: A performance model for deep neural networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017."},{"key":"e_1_3_2_1_16_1","unstructured":"Amazon ec2 pricing - amazon web services. https:\/\/aws.amazon.com\/ec2\/pricing\/. (Accessed on 03\/11\/2021).  Amazon ec2 pricing - amazon web services. https:\/\/aws.amazon.com\/ec2\/pricing\/. (Accessed on 03\/11\/2021)."},{"key":"e_1_3_2_1_17_1","unstructured":"All pricing | compute engine documentation | google cloud. https:\/\/cloud.google.com\/compute\/all-pricing. (Accessed on 03\/11\/2021).  All pricing | compute engine documentation | google cloud. https:\/\/cloud.google.com\/compute\/all-pricing. (Accessed on 03\/11\/2021)."},{"key":"e_1_3_2_1_18_1","unstructured":"Pricing - windows virtual machine | microsoft azure. https:\/\/azure.microsoft.com\/en-us\/pricing\/details\/virtual-machines\/windows\/. (Accessed on 03\/11\/2021).  Pricing - windows virtual machine | microsoft azure. https:\/\/azure.microsoft.com\/en-us\/pricing\/details\/virtual-machines\/windows\/. (Accessed on 03\/11\/2021)."},{"key":"e_1_3_2_1_19_1","unstructured":"Aws lambda --- pricing. https:\/\/aws.amazon.com\/lambda\/pricing\/. (Accessed on 03\/11\/2021).  Aws lambda --- pricing. https:\/\/aws.amazon.com\/lambda\/pricing\/. (Accessed on 03\/11\/2021)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916550"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3190508.3190517"},{"key":"e_1_3_2_1_22_1","first-page":"14","volume-title":"2018 ACM SIGSAC Conference on Computer and Communications Security (CCS'18)","author":"Shen Yun","year":"2018","unstructured":"Yun Shen , Enrico Mariconti , Pierre-Antoine Vervier , and Gianluca Stringhini . Tiresias : Predicting security events through deep learning . In 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS'18) , page 14 , 2018 . Yun Shen, Enrico Mariconti, Pierre-Antoine Vervier, and Gianluca Stringhini. Tiresias: Predicting security events through deep learning. In 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS'18), page 14, 2018."},{"key":"e_1_3_2_1_23_1","first-page":"595","volume-title":"USENIX Symposium on Operating Systems Design and Implementation (OSDI)","author":"Xiao Wencong","year":"2018","unstructured":"Wencong Xiao , Romil Bhardwaj , Ramachandran Ramjee , Muthian Sivathanu , Nipun Kwatra , Zhenhua Han , Pratyush Patel , Xuan Peng , Hanyu Zhao , Quanlu Zhang , Fan Yang , and Lidong Zhou . Gandiva : Introspective cluster scheduling for deep learning . In USENIX Symposium on Operating Systems Design and Implementation (OSDI) , pages 595 -- 610 , 2018 . Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel, Xuan Peng, Hanyu Zhao, Quanlu Zhang, Fan Yang, and Lidong Zhou. Gandiva: Introspective cluster scheduling for deep learning. In USENIX Symposium on Operating Systems Design and Implementation (OSDI), pages 595--610, 2018."},{"key":"e_1_3_2_1_24_1","first-page":"947","volume-title":"2019 USENIX Annual Technical Conference (USENIX ATC 19)","author":"Jeon Myeongjae","year":"2019","unstructured":"Myeongjae Jeon , Shivaram Venkataraman , Amar Phanishayee , Junjie Qian , Wencong Xiao , and Fan Yang . Analysis of large-scale multi-tenant GPU clusters for DNN training workloads . In 2019 USENIX Annual Technical Conference (USENIX ATC 19) , pages 947 -- 960 , Renton, WA , July 2019 . USENIX Association. Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, and Fan Yang. Analysis of large-scale multi-tenant GPU clusters for DNN training workloads. In 2019 USENIX Annual Technical Conference (USENIX ATC 19), pages 947--960, Renton, WA, July 2019. USENIX Association."},{"key":"e_1_3_2_1_25_1","volume-title":"Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, and Shuchi Chawla. Themis: Fair and efficient GPU cluster scheduling for machine learning workloads.","author":"Mahajan Kshiteej","year":"2019","unstructured":"Kshiteej Mahajan , Arjun Singhvi , Arjun Balasubramanian , Varun Batra , Surya Teja Chavali , Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, and Shuchi Chawla. Themis: Fair and efficient GPU cluster scheduling for machine learning workloads. volume abs\/ 1907 .01484, 2019 . Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, and Shuchi Chawla. Themis: Fair and efficient GPU cluster scheduling for machine learning workloads. volume abs\/1907.01484, 2019."},{"key":"e_1_3_2_1_26_1","unstructured":"Amazon ec2 instance types - amazon web services. https:\/\/aws.amazon.com\/ec2\/instance-types\/. (Accessed on 03\/11\/2021).  Amazon ec2 instance types - amazon web services. https:\/\/aws.amazon.com\/ec2\/instance-types\/. (Accessed on 03\/11\/2021)."},{"key":"e_1_3_2_1_27_1","volume-title":"On large-batch training for deep learning: Generalization gap and sharp minima","author":"Keskar Nitish Shirish","year":"2017","unstructured":"Nitish Shirish Keskar , Dheevatsa Mudigere , Jorge Nocedal , Mikhail Smelyanskiy , and Ping Tak Peter Tang . On large-batch training for deep learning: Generalization gap and sharp minima , 2017 . Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima, 2017."},{"key":"e_1_3_2_1_28_1","volume-title":"VLDB DISPA Workshop","author":"Narayanan Deepak","year":"2020","unstructured":"Deepak Narayanan , Keshav Santhanam , Fiodar Kazhamiaka , Amar Phanishayee , and Matei Zaharia . Analysis and exploitation of dynamic pricing in the public cloud for ml training . VLDB DISPA Workshop 2020 . Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, and Matei Zaharia. Analysis and exploitation of dynamic pricing in the public cloud for ml training. VLDB DISPA Workshop 2020."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3132017"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"e_1_3_2_1_31_1","volume-title":"Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118","author":"Liaw Richard","year":"2018","unstructured":"Richard Liaw , Eric Liang , Robert Nishihara , Philipp Moritz , Joseph E Gonzalez , and Ion Stoica . Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118 , 2018 . Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica. Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118, 2018."},{"key":"e_1_3_2_1_32_1","first-page":"561","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Moritz Philipp","year":"2018","unstructured":"Philipp Moritz , Robert Nishihara , Stephanie Wang , Alexey Tumanov , Richard Liaw , Eric Liang , Melih Elibol , Zongheng Yang , William Paul , Michael I Jordan , : A distributed framework for emerging ai applications . In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) , pages 561 -- 577 , 2018 . Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I Jordan, et al. Ray: A distributed framework for emerging ai applications. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pages 561--577, 2018."},{"key":"e_1_3_2_1_33_1","unstructured":"Distributeddataparallel --- pytorch 1.6.0 documentation. https:\/\/pytorch.org\/docs\/stable\/generated\/torch.nn.parallel.DistributedDataParallel.html. (Accessed on 10\/09\/2020).  Distributeddataparallel --- pytorch 1.6.0 documentation. https:\/\/pytorch.org\/docs\/stable\/generated\/torch.nn.parallel.DistributedDataParallel.html. (Accessed on 10\/09\/2020)."},{"key":"e_1_3_2_1_34_1","unstructured":"Boto3 documentation --- boto3 docs 1.15.15 documentation. https:\/\/boto3.amazonaws.com\/v1\/documentation\/api\/latest\/index.html. (Accessed on 10\/09\/2020).  Boto3 documentation --- boto3 docs 1.15.15 documentation. https:\/\/boto3.amazonaws.com\/v1\/documentation\/api\/latest\/index.html. (Accessed on 10\/09\/2020)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_36_1","unstructured":"Amazon s3 simple storage service pricing - amazon web services. https:\/\/aws.amazon.com\/s3\/pricing\/. (Accessed on 03\/11\/2021).  Amazon s3 simple storage service pricing - amazon web services. https:\/\/aws.amazon.com\/s3\/pricing\/. (Accessed on 03\/11\/2021)."},{"key":"e_1_3_2_1_37_1","first-page":"3735","volume-title":"Journal of Machine Learning Research","author":"Martinez-Cantin Ruben","year":"2014","unstructured":"Ruben Martinez-Cantin . Bayesopt : A bayesian optimization library for nonlinear optimization, experimental design and bandits . In Journal of Machine Learning Research , pages 3735 -- 3739 , 2014 . Ruben Martinez-Cantin. Bayesopt: A bayesian optimization library for nonlinear optimization, experimental design and bandits. In Journal of Machine Learning Research, pages 3735--3739, 2014."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_1_39_1","unstructured":"Distributed deep learning and hyperparameter tuning platform | determined ai. https:\/\/determined.ai\/. (Accessed on 10\/08\/2020).  Distributed deep learning and hyperparameter tuning platform | determined ai. https:\/\/determined.ai\/. (Accessed on 10\/08\/2020)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3342195.3387555"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3419111.3421287"},{"key":"e_1_3_2_1_42_1","volume-title":"Elastic deep learning in multi-tenant gpu cluster. arXiv preprint arXiv:1909.11985","author":"Wu Yidi","year":"2019","unstructured":"Yidi Wu , Kaihao Ma , Xiao Yan , Zhi Liu , and James Cheng . Elastic deep learning in multi-tenant gpu cluster. arXiv preprint arXiv:1909.11985 , 2019 . Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, and James Cheng. Elastic deep learning in multi-tenant gpu cluster. arXiv preprint arXiv:1909.11985, 2019."},{"key":"e_1_3_2_1_43_1","unstructured":"Torchelastic --- pytorch\/elastic master documentation. https:\/\/pytorch.org\/elastic\/0.2.1\/index.html. (Accessed on 10\/08\/2020).  Torchelastic --- pytorch\/elastic master documentation. https:\/\/pytorch.org\/elastic\/0.2.1\/index.html. (Accessed on 10\/08\/2020)."},{"key":"e_1_3_2_1_44_1","unstructured":"Andrew Or Haoyu Zhang and Michael J Freedman. Resource elasticity in distributed deep learning.  Andrew Or Haoyu Zhang and Michael J Freedman. Resource elasticity in distributed deep learning."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362711"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132772"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2038916.2038921"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2644865.2541941"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3267809.3267819"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063384.2063449"},{"key":"e_1_3_2_1_51_1","first-page":"363","volume-title":"Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation, NSDI'16","author":"Venkataraman Shivaram","year":"2016","unstructured":"Shivaram Venkataraman , Zongheng Yang , Michael Franklin , Benjamin Recht , and Ion Stoica . Ernest : Efficient performance prediction for large-scale advanced analytics . In Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation, NSDI'16 , page 363 -- 378 , USA, 2016 . USENIX Association. Shivaram Venkataraman, Zongheng Yang, Michael Franklin, Benjamin Recht, and Ion Stoica. Ernest: Efficient performance prediction for large-scale advanced analytics. In Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation, NSDI'16, page 363--378, USA, 2016. USENIX Association."},{"key":"e_1_3_2_1_52_1","first-page":"469","volume-title":"Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation, NSDI'17","author":"Alipourfard Omid","year":"2017","unstructured":"Omid Alipourfard , Hongqiang Harry Liu , Jianshu Chen , Shivaram Venkataraman , Minlan Yu , and Ming Zhang . Cherrypick : Adaptively unearthing the best cloud configurations for big data analytics . In Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation, NSDI'17 , page 469 -- 482 , USA, 2017 . USENIX Association. Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang. Cherrypick: Adaptively unearthing the best cloud configurations for big data analytics. In Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation, NSDI'17, page 469--482, USA, 2017. USENIX Association."}],"event":{"name":"EuroSys '21: Sixteenth European Conference on Computer Systems","location":"Online Event United Kingdom","acronym":"EuroSys '21","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the Sixteenth European Conference on Computer Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447786.3456245","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447786.3456245","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447786.3456245","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:10Z","timestamp":1750200070000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447786.3456245"}},"subtitle":["cloud-based hyperparameter tuning"],"short-title":[],"issued":{"date-parts":[[2021,4,21]]},"references-count":52,"alternative-id":["10.1145\/3447786.3456245","10.1145\/3447786"],"URL":"https:\/\/doi.org\/10.1145\/3447786.3456245","relation":{},"subject":[],"published":{"date-parts":[[2021,4,21]]},"assertion":[{"value":"2021-04-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}