{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:14:48Z","timestamp":1776122088967,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":96,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,10,27]],"date-time":"2019-10-27T00:00:00Z","timestamp":1572134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin"},{"DOI":"10.13039\/100001395","name":"Wisconsin Alumni Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001395","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE-1745016,DGE-1252522,CNS-1850483,CNS-1838733"],"award-info":[{"award-number":["DGE-1745016,DGE-1252522,CNS-1850483,CNS-1838733"]}],"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":[[2019,10,27]]},"DOI":"10.1145\/3341301.3359654","type":"proceedings-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T13:34:22Z","timestamp":1571664862000},"page":"30-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Parity models"],"prefix":"10.1145","author":[{"given":"Jack","family":"Kosaian","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"K. V.","family":"Rashmi","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Shivaram","family":"Venkataraman","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}]}],"member":"320","published-online":{"date-parts":[[2019,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"https:\/\/developer.amazon.com\/alexa. Last accessed","author":"Amazon Alexa","year":"2019","unstructured":"Amazon Alexa . https:\/\/developer.amazon.com\/alexa. Last accessed 01 September 2019 . Amazon Alexa. https:\/\/developer.amazon.com\/alexa. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_2_1","volume-title":"https:\/\/aws.amazon.com\/ec2\/instance-types\/c5\/. Last accessed","author":"Amazon","year":"2019","unstructured":"Amazon EC2 C5 Instances. https:\/\/aws.amazon.com\/ec2\/instance-types\/c5\/. Last accessed 01 September 2019 . Amazon EC2 C5 Instances. https:\/\/aws.amazon.com\/ec2\/instance-types\/c5\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_3_1","volume-title":"https:\/\/azure.microsoft.com\/en-us\/services\/machine-learning-studio\/. Last accessed","author":"Azure Machine Learning Studio","year":"2019","unstructured":"Azure Machine Learning Studio . https:\/\/azure.microsoft.com\/en-us\/services\/machine-learning-studio\/. Last accessed 01 September 2019 . Azure Machine Learning Studio. https:\/\/azure.microsoft.com\/en-us\/services\/machine-learning-studio\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_4_1","volume-title":"https:\/\/cloud.google.com\/products\/machine-learning\/. Last accessed","author":"Google Cloud AI.","year":"2019","unstructured":"Google Cloud AI. https:\/\/cloud.google.com\/products\/machine-learning\/. Last accessed 01 September 2019 . Google Cloud AI. https:\/\/cloud.google.com\/products\/machine-learning\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_5_1","volume-title":"real-time answers to questions about the world around you. https:\/\/bit.ly\/2MHAOLq. Last accessed","author":"Google","year":"2019","unstructured":"Google lens : real-time answers to questions about the world around you. https:\/\/bit.ly\/2MHAOLq. Last accessed 01 September 2019 . Google lens: real-time answers to questions about the world around you. https:\/\/bit.ly\/2MHAOLq. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_6_1","volume-title":"http:\/\/www.slideshare.net\/ydn\/hdfs-raid-facebook. Last accessed","author":"RAID.","year":"2019","unstructured":"HDFS RAID. http:\/\/www.slideshare.net\/ydn\/hdfs-raid-facebook. Last accessed 01 September 2019 . HDFS RAID. http:\/\/www.slideshare.net\/ydn\/hdfs-raid-facebook. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_7_1","volume-title":"https:\/\/www.apple.com\/ios\/siri\/. Last accessed","author":"Siri","year":"2019","unstructured":"iOS Siri . https:\/\/www.apple.com\/ios\/siri\/. Last accessed 01 September 2019 . iOS Siri. https:\/\/www.apple.com\/ios\/siri\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_8_1","volume-title":"https:\/\/aws.amazon.com\/machine-learning\/. Last accessed","author":"Machine Learning AWS","year":"2019","unstructured":"Machine Learning on AWS . https:\/\/aws.amazon.com\/machine-learning\/. Last accessed 01 September 2019 . Machine Learning on AWS. https:\/\/aws.amazon.com\/machine-learning\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_9_1","volume-title":"https:\/\/github.com\/awslabs\/mxnet-model-server. Last accessed","author":"Model Server","year":"2019","unstructured":"Model Server for Apache MXNet. https:\/\/github.com\/awslabs\/mxnet-model-server. Last accessed 01 September 2019 . Model Server for Apache MXNet. https:\/\/github.com\/awslabs\/mxnet-model-server. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_10_1","volume-title":"https:\/\/developer.nvidia.com\/tensorrt. Last accessed","author":"Tensor RT.","year":"2019","unstructured":"NVIDIA Tensor RT. https:\/\/developer.nvidia.com\/tensorrt. Last accessed 01 September 2019 . NVIDIA TensorRT. https:\/\/developer.nvidia.com\/tensorrt. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_11_1","volume-title":"https:\/\/opencv.org\/. Last accessed","author":"Open CV.","year":"2019","unstructured":"Open CV. https:\/\/opencv.org\/. Last accessed 01 September 2019 . OpenCV. https:\/\/opencv.org\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_12_1","volume-title":"https:\/\/pytorch.org\/. Last accessed","author":"PyTorch","year":"2019","unstructured":"PyTorch . https:\/\/pytorch.org\/. Last accessed 01 September 2019 . PyTorch. https:\/\/pytorch.org\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_13_1","volume-title":"https:\/\/data-flair.training\/blogs\/speculative-execution-in-hadoop-mapreduce\/. Last accessed","author":"Speculative Execution","year":"2019","unstructured":"Speculative Execution in Hadoop MapReduce. https:\/\/data-flair.training\/blogs\/speculative-execution-in-hadoop-mapreduce\/. Last accessed 01 September 2019 . Speculative Execution in Hadoop MapReduce. https:\/\/data-flair.training\/blogs\/speculative-execution-in-hadoop-mapreduce\/. Last accessed 01 September 2019."},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the 14th ACM Conference on Computer and Communications Security (CCS 07)","author":"Asirra","year":"2007","unstructured":"Asirra : A CAPTCHA That Exploits Interest-aligned Manual Image Categorization . In Proceedings of the 14th ACM Conference on Computer and Communications Security (CCS 07) ( 2007 ). Asirra: A CAPTCHA That Exploits Interest-aligned Manual Image Categorization. In Proceedings of the 14th ACM Conference on Computer and Communications Security (CCS 07) (2007)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556252"},{"key":"e_1_3_2_1_16_1","unstructured":"Alex Krizhevsky and Vinod Nair and Geoffrey Hinton. The CIFAR-10 and CIFAR-100 Datasets. https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html.  Alex Krizhevsky and Vinod Nair and Geoffrey Hinton. The CIFAR-10 and CIFAR-100 Datasets. https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html."},{"key":"e_1_3_2_1_17_1","volume-title":"CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17)","author":"Alipourfard O.","year":"2017","unstructured":"Alipourfard , O. , Liu , H. H. , Chen , J. , Venkataraman , S. , Yu , M. , and Zhang , M . CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17) ( 2017 ). Alipourfard, O., Liu, H. H., Chen, J., Venkataraman, S., Yu, M., and Zhang, M. CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17) (2017)."},{"key":"e_1_3_2_1_18_1","volume-title":"Effective Straggler Mitigation: Attack of the Clones. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13)","author":"Ananthanarayanan G.","year":"2013","unstructured":"Ananthanarayanan , G. , Ghodsi , A. , Shenker , S. , and Stoica , I . Effective Straggler Mitigation: Attack of the Clones. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13) ( 2013 ). Ananthanarayanan, G., Ghodsi, A., Shenker, S., and Stoica, I. Effective Straggler Mitigation: Attack of the Clones. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13) (2013)."},{"key":"e_1_3_2_1_19_1","volume-title":"9th USENIX Symposium on Operating Systems Design and Implementation (OSDI 10)","author":"Ananthanarayanan G.","year":"2010","unstructured":"Ananthanarayanan , G. , Kandula , S. , Greenberg , A. G. , Stoica , I. , Lu , Y. , Saha , B. , and Harris , E . Reining in the Outliers in Map-Reduce Clusters using Mantri . In 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI 10) ( 2010 ). Ananthanarayanan, G., Kandula, S., Greenberg, A. G., Stoica, I., Lu, Y., Saha, B., and Harris, E. Reining in the Outliers in Map-Reduce Clusters using Mantri. In 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI 10) (2010)."},{"key":"e_1_3_2_1_20_1","volume-title":"End-to-End Learning of Communications Systems Without a Channel Model. arXiv preprint arXiv:1804.02276","author":"Aoudia F. A.","year":"2018","unstructured":"Aoudia , F. A. , and Hoydis , J . End-to-End Learning of Communications Systems Without a Channel Model. arXiv preprint arXiv:1804.02276 ( 2018 ). Aoudia, F. A., and Hoydis, J. End-to-End Learning of Communications Systems Without a Channel Model. arXiv preprint arXiv:1804.02276 (2018)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098021"},{"key":"e_1_3_2_1_22_1","volume-title":"TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Chen T.","unstructured":"Chen , T. , Moreau , T. , Jiang , Z. , Zheng , L. , Yan , E. , Shen , H. , Cowan , M. , Wang , L. , Hu , Y. , Ceze , L. , Guestrin , C. , and Krishnamurthy , A . TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) . Chen, T., Moreau, T., Jiang, Z., Zheng, L., Yan, E., Shen, H., Cowan, M., Wang, L., Hu, Y., Ceze, L., Guestrin, C., and Krishnamurthy, A. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)."},{"key":"e_1_3_2_1_23_1","first-page":"2","volume":"38","author":"Chung E.","year":"2018","unstructured":"Chung , E. , Fowers , J. , Ovtcharov , K. , Papamichael , M. , Caulfield , A. , Massengill , T. , Liu , M. , Lo , D. , Alkalay , S. , Haselman , M. , Serving DNNs in Real Time at Datacenter Scale with Project Brainwave. IEEE Micro 38 , 2 ( 2018 ), 8--20. Chung, E., Fowers, J., Ovtcharov, K., Papamichael, M., Caulfield, A., Massengill, T., Liu, M., Lo, D., Alkalay, S., Haselman, M., et al. Serving DNNs in Real Time at Datacenter Scale with Project Brainwave. IEEE Micro 38, 2 (2018), 8--20.","journal-title":"Datacenter Scale with Project Brainwave. IEEE Micro"},{"key":"e_1_3_2_1_24_1","volume-title":"InferLine: ML Inference Pipeline Composition Framework. arXiv preprint arXiv:1812.01776","author":"Crankshaw D.","year":"2018","unstructured":"Crankshaw , D. , Sela , G.-E. , Zumar , C. , Mo , X. , Gonzalez , J. E. , Stoica , I. , and Tumanov , A . InferLine: ML Inference Pipeline Composition Framework. arXiv preprint arXiv:1812.01776 ( 2018 ). Crankshaw, D., Sela, G.-E., Zumar, C., Mo, X., Gonzalez, J. E., Stoica, I., and Tumanov, A. InferLine: ML Inference Pipeline Composition Framework. arXiv preprint arXiv:1812.01776 (2018)."},{"key":"e_1_3_2_1_25_1","volume-title":"Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17)","author":"Crankshaw D.","year":"2017","unstructured":"Crankshaw , D. , Wang , X. , Zhou , G. , Franklin , M. J. , Gonzalez , J. E. , and Stoica , I . Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17) ( 2017 ). Crankshaw, D., Wang, X., Zhou, G., Franklin, M. J., Gonzalez, J. E., and Stoica, I. Clipper: A Low-Latency Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17) (2017)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2408776.2408794"},{"key":"e_1_3_2_1_27_1","volume-title":"Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR 15)","author":"Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR 15) ( 2015 ). Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR 15) (2015)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2018.8437852"},{"key":"e_1_3_2_1_29_1","volume-title":"Advances In Neural Information Processing Systems (NIPS 16)","author":"Dutta S.","year":"2016","unstructured":"Dutta , S. , Cadambe , V. , and Grover , P . Short-dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products . In Advances In Neural Information Processing Systems (NIPS 16) ( 2016 ). Dutta, S., Cadambe, V., and Grover, P. Short-dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products. In Advances In Neural Information Processing Systems (NIPS 16) (2016)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2017.8006960"},{"key":"e_1_3_2_1_31_1","first-page":"1","volume":"43","author":"Gardner K.","year":"2015","unstructured":"Gardner , K. , Zbarsky , S. , Doroudi , S. , Harchol-Balter , M. , and Hyytia , E. Reducing Latency via Redundant Requests: Exact Analysis. ACM SIGMETRICS Performance Evaluation Review 43 , 1 ( 2015 ), 347--360. Gardner, K., Zbarsky, S., Doroudi, S., Harchol-Balter, M., and Hyytia, E. Reducing Latency via Redundant Requests: Exact Analysis. ACM SIGMETRICS Performance Evaluation Review 43, 1 (2015), 347--360.","journal-title":"Redundant Requests: Exact Analysis. ACM SIGMETRICS Performance Evaluation Review"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 10)","author":"Glorot X.","year":"2010","unstructured":"Glorot , X. , and Bengio , Y . Understanding the Difficulty of Training Deep Feedforward Neural Networks . In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 10) ( 2010 ). Glorot, X., and Bengio, Y. Understanding the Difficulty of Training Deep Feedforward Neural Networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 10) (2010)."},{"key":"e_1_3_2_1_33_1","volume-title":"Queues Don't Matter When You Can JUMP Them! In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15)","author":"Grosvenor M. P.","year":"2015","unstructured":"Grosvenor , M. P. , Schwarzkopf , M. , Gog , I. , Watson , R. N. M. , Moore , A. W. , Hand , S. , and Crowcroft , J . Queues Don't Matter When You Can JUMP Them! In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15) ( 2015 ). Grosvenor, M. P., Schwarzkopf, M., Gog, I., Watson, R. N. M., Moore, A. W., Hand, S., and Crowcroft, J. Queues Don't Matter When You Can JUMP Them! In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15) (2015)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3135974.3135993"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132774"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781139226424","volume-title":"Performance Modeling and Design of Computer Systems: Queueing Theory in Action","author":"Harchol-Balter M.","year":"2013","unstructured":"Harchol-Balter , M. Performance Modeling and Design of Computer Systems: Queueing Theory in Action . Cambridge University Press , 2013 . Harchol-Balter, M. Performance Modeling and Design of Computer Systems: Queueing Theory in Action. Cambridge University Press, 2013."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987554"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2749472"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00059"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_41_1","volume-title":"Advances in Neural Information Processing Systems (NIPS 13)","author":"Ho Q.","year":"2013","unstructured":"Ho , Q. , Cipar , J. , Cui , H. , Lee , S. , Kim , J. K. , Gibbons , P. B. , Gibson , G. A. , Ganger , G. , and Xing , E. P . More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server . In Advances in Neural Information Processing Systems (NIPS 13) ( 2013 ). Ho, Q., Cipar, J., Cui, H., Lee, S., Kim, J. K., Gibbons, P. B., Gibson, G. A., Ganger, G., and Xing, E. P. More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server. In Advances in Neural Information Processing Systems (NIPS 13) (2013)."},{"key":"e_1_3_2_1_42_1","volume-title":"Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing. arXiv preprint arXiv:1708.06832","author":"Hu H.","year":"2018","unstructured":"Hu , H. , Dey , D. , Bagnell , J. A. , and Hebert , M . Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing. arXiv preprint arXiv:1708.06832 ( 2018 ). Hu, H., Dey, D., Bagnell, J. A., and Hebert, M. Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing. arXiv preprint arXiv:1708.06832 (2018)."},{"key":"e_1_3_2_1_43_1","volume-title":"Erasure Coding in Windows Azure Storage. In 2012 USENIX Annual Technical Conference (USENIX ATC 12)","author":"Huang C.","year":"2012","unstructured":"Huang , C. , Simitci , H. , Xu , Y. , Ogus , A. , Calder , B. , Gopalan , P. , Li , J. , and Yekhanin , S . Erasure Coding in Windows Azure Storage. In 2012 USENIX Annual Technical Conference (USENIX ATC 12) ( 2012 ). Huang, C., Simitci, H., Xu, Y., Ogus, A., Calder, B., Gopalan, P., Li, J., and Yekhanin, S. Erasure Coding in Windows Azure Storage. In 2012 USENIX Annual Technical Conference (USENIX ATC 12) (2012)."},{"key":"e_1_3_2_1_44_1","volume-title":"PerfIso: Performance Isolation for Commercial Latency-Sensitive Services. In 2018 USENIX Annual Technical Conference (USENIX ATC 18)","author":"Iorgulescu C.","year":"2018","unstructured":"Iorgulescu , C. , Azimi , R. , Kwon , Y. , Elnikety , S. , Syamala , M. , Narasayya , V. , Herodotou , H. , Tomita , P. , Chen , A. , Zhang , J. , and Wang , J . PerfIso: Performance Isolation for Commercial Latency-Sensitive Services. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) ( 2018 ). Iorgulescu, C., Azimi, R., Kwon, Y., Elnikety, S., Syamala, M., Narasayya, V., Herodotou, H., Tomita, P., Chen, A., Zhang, J., and Wang, J. PerfIso: Performance Isolation for Commercial Latency-Sensitive Services. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) (2018)."},{"key":"e_1_3_2_1_45_1","volume-title":"Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. arXiv preprint arXiv:1712.05877","author":"Jacob B.","year":"2017","unstructured":"Jacob , B. , Kligys , S. , Chen , B. , Zhu , M. , Tang , M. , Howard , A. , Adam , H. , and Kalenichenko , D . Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. arXiv preprint arXiv:1712.05877 ( 2017 ). Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. arXiv preprint arXiv:1712.05877 (2017)."},{"key":"e_1_3_2_1_46_1","volume-title":"Mainstream: Dynamic Stem-Sharing for Multi-Tenant Video Processing. In 2018 USENIX Annual Technical Conference (USENIX ATC 18)","author":"Jiang A. H.","year":"2018","unstructured":"Jiang , A. H. , Wong , D. L.-K. , Canel , C. , Tang , L. , Misra , I. , Kaminsky , M. , Kozuch , M. A. , Pillai , P. , Andersen , D. G. , and Ganger , G. R . Mainstream: Dynamic Stem-Sharing for Multi-Tenant Video Processing. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) ( 2018 ). Jiang, A. H., Wong, D. L.-K., Canel, C., Tang, L., Misra, I., Kaminsky, M., Kozuch, M. A., Pillai, P., Andersen, D. G., and Ganger, G. R. Mainstream: Dynamic Stem-Sharing for Multi-Tenant Video Processing. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) (2018)."},{"key":"e_1_3_2_1_47_1","first-page":"989","volume":"5","author":"Joshi G.","year":"2014","unstructured":"Joshi , G. , Liu , Y. , and Soljanin , E. On the Delay-Storage Trade-Off in Content Download From Coded Distributed Storage Systems. IEEE JSAC , 5 ( 2014 ), 989 -- 997 . Joshi, G., Liu, Y., and Soljanin, E. On the Delay-Storage Trade-Off in Content Download From Coded Distributed Storage Systems. IEEE JSAC, 5 (2014), 989--997.","journal-title":"On the Delay-Storage Trade-Off in Content Download From Coded Distributed Storage Systems. IEEE JSAC"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Learning Representations (ICLR 18)","author":"Kim H.","year":"2018","unstructured":"Kim , H. , Jiang , Y. , Rana , R. , Kannan , S. , Oh , S. , and Viswanath , P . Communication Algorithms via Deep Learning . In International Conference on Learning Representations (ICLR 18) ( 2018 ). Kim, H., Jiang, Y., Rana, R., Kannan, S., Oh, S., and Viswanath, P. Communication Algorithms via Deep Learning. In International Conference on Learning Representations (ICLR 18) (2018)."},{"key":"e_1_3_2_1_50_1","volume-title":"Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. arXiv preprint arXiv:1806.01259","author":"Kosaian J.","year":"2018","unstructured":"Kosaian , J. , Rashmi , K. V. , and Venkataraman , S . Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. arXiv preprint arXiv:1806.01259 ( 2018 ). Kosaian, J., Rashmi, K. V., and Venkataraman, S. Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. arXiv preprint arXiv:1806.01259 (2018)."},{"key":"e_1_3_2_1_51_1","volume-title":"Advances in Neural Information Processing Systems (NIPS 12)","author":"Krizhevsky A.","year":"2012","unstructured":"Krizhevsky , A. , Sutskever , I. , and Hinton , G. E . Imagenet Classification with Deep Convolutional Neural Networks . In Advances in Neural Information Processing Systems (NIPS 12) ( 2012 ). Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (NIPS 12) (2012)."},{"key":"e_1_3_2_1_52_1","unstructured":"LeCun Y. The MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/.  LeCun Y. The MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"e_1_3_2_1_53_1","first-page":"11","volume":"86","author":"LeCun Y.","year":"1998","unstructured":"LeCun , Y. , Bottou , L. , Bengio , Y. , and Haffner , P. Gradient -based Learning Applied to Document Recognition. Proceedings of the IEEE 86 , 11 ( 1998 ), 2278--2324. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based Learning Applied to Document Recognition. Proceedings of the IEEE 86, 11 (1998), 2278--2324.","journal-title":"Document Recognition. Proceedings of the IEEE"},{"key":"e_1_3_2_1_54_1","volume-title":"Speeding Up Distributed Machine Learning Using Codes","author":"Lee K.","year":"2018","unstructured":"Lee , K. , Lam , M. , Pedarsani , R. , Papailiopoulos , D. , and Ramchandran , K . Speeding Up Distributed Machine Learning Using Codes . IEEE Transactions on Information Theory (July 2018 ). Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., and Ramchandran, K. Speeding Up Distributed Machine Learning Using Codes. IEEE Transactions on Information Theory (July 2018)."},{"key":"e_1_3_2_1_55_1","volume-title":"PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Lee Y.","year":"2018","unstructured":"Lee , Y. , Scolari , A. , Chun , B.-G. , Santambrogio , M. D. , Weimer , M. , and Interlandi , M . PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) ( 2018 ). Lee, Y., Scolari, A., Chun, B.-G., Santambrogio, M. D., Weimer, M., and Interlandi, M. PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) (2018)."},{"key":"e_1_3_2_1_56_1","volume-title":"Towards High-Performance Prediction Serving Systems. NIPS ML Systems Workshop","author":"Lee Y.","year":"2017","unstructured":"Lee , Y. , Scolari , A. , Interlandi , M. , Weimer , M. , and Chun , B . -G . Towards High-Performance Prediction Serving Systems. NIPS ML Systems Workshop ( 2017 ). Lee, Y., Scolari, A., Interlandi, M., Weimer, M., and Chun, B.-G. Towards High-Performance Prediction Serving Systems. NIPS ML Systems Workshop (2017)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOMW.2016.7848828"},{"key":"e_1_3_2_1_58_1","volume-title":"Metis: Robustly Tuning Tail Latencies of Cloud Systems. In 2018 USENIX Annual Technical Conference (USENIX ATC 18)","author":"Li Z. L.","year":"2018","unstructured":"Li , Z. L. , Liang , C.-J. M. , He , W. , Zhu , L. , Dai , W. , Jiang , J. , and Sun , G . Metis: Robustly Tuning Tail Latencies of Cloud Systems. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) ( 2018 ). Li, Z. L., Liang, C.-J. M., He, W., Zhu, L., Dai, W., Jiang, J., and Sun, G. Metis: Robustly Tuning Tail Latencies of Cloud Systems. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) (2018)."},{"key":"e_1_3_2_1_59_1","volume-title":"FAST CLOUD: Pushing the Envelope on Delay Performance of Cloud Storage with Coding. arXiv:1301.1294 (Jan","author":"Liang G.","year":"2013","unstructured":"Liang , G. , and Kozat , U. C . FAST CLOUD: Pushing the Envelope on Delay Performance of Cloud Storage with Coding. arXiv:1301.1294 (Jan . 2013 ). Liang, G., and Kozat, U. C. FAST CLOUD: Pushing the Envelope on Delay Performance of Cloud Storage with Coding. arXiv:1301.1294 (Jan. 2013)."},{"key":"e_1_3_2_1_60_1","volume-title":"Optimizing CNN Model Inference on CPUs. In 2019 USENIX Annual Technical Conference (USENIX ATC 19)","author":"Liu Y.","year":"2019","unstructured":"Liu , Y. , Wang , Y. , Yu , R. , Li , M. , Sharma , V. , and Wang , Y . Optimizing CNN Model Inference on CPUs. In 2019 USENIX Annual Technical Conference (USENIX ATC 19) ( 2019 ). Liu, Y., Wang, Y., Yu, R., Li, M., Sharma, V., and Wang, Y. Optimizing CNN Model Inference on CPUs. In 2019 USENIX Annual Technical Conference (USENIX ATC 19) (2019)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934878"},{"key":"e_1_3_2_1_62_1","volume-title":"Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication. arXiv preprint arXiv:1804.10331","author":"Mallick A.","year":"2018","unstructured":"Mallick , A. , Chaudhari , M. , and Joshi , G . Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication. arXiv preprint arXiv:1804.10331 ( 2018 ). Mallick, A., Chaudhari, M., and Joshi, G. Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication. arXiv preprint arXiv:1804.10331 (2018)."},{"key":"e_1_3_2_1_63_1","first-page":"1","volume":"12","author":"Nachmani E.","year":"2018","unstructured":"Nachmani , E. , Marciano , E. , Lugosch , L. , Gross , W. J. , Burshtein , D. , and Be'ery , Y. Deep Learning Methods for Improved Decoding of Linear Codes. IEEE Journal of Selected Topics in Signal Processing 12 , 1 ( 2018 ), 119--131. Nachmani, E., Marciano, E., Lugosch, L., Gross, W. J., Burshtein, D., and Be'ery, Y. Deep Learning Methods for Improved Decoding of Linear Codes. IEEE Journal of Selected Topics in Signal Processing 12, 1 (2018), 119--131.","journal-title":"Deep Learning Methods for Improved Decoding of Linear Codes. IEEE Journal of Selected Topics in Signal Processing"},{"key":"e_1_3_2_1_64_1","volume-title":"High-Performance ML Serving. NIPS ML Systems Workshop","author":"Olston C.","year":"2017","unstructured":"Olston , C. , Fiedel , N. , Gorovoy , K. , Harmsen , J. , Lao , L. , Li , F. , Rajashekhar , V. , Ramesh , S. , and Soyke , J . TensorFlow-Serving: Flexible , High-Performance ML Serving. NIPS ML Systems Workshop ( 2017 ). Olston, C., Fiedel, N., Gorovoy, K., Harmsen, J., Lao, L., Li, F., Rajashekhar, V., Ramesh, S., and Soyke, J. TensorFlow-Serving: Flexible, High-Performance ML Serving. NIPS ML Systems Workshop (2017)."},{"key":"e_1_3_2_1_65_1","volume-title":"Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. arXiv preprint arXiv:1811.09886","author":"Park J.","year":"2018","unstructured":"Park , J. , Naumov , M. , Basu , P. , Deng , S. , Kalaiah , A. , Khudia , D. , Law , J. , Malani , P. , Malevich , A. , Nadathur , S. , Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. arXiv preprint arXiv:1811.09886 ( 2018 ). Park, J., Naumov, M., Basu, P., Deng, S., Kalaiah, A., Khudia, D., Law, J., Malani, P., Malevich, A., Nadathur, S., et al. Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications. arXiv preprint arXiv:1811.09886 (2018)."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/50202.50214"},{"key":"e_1_3_2_1_67_1","volume-title":"Low-Latency Cluster Caching with Online Erasure Coding. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Rashmi K. V.","year":"2016","unstructured":"Rashmi , K. V. , Chowdhury , M. , Kosaian , J. , Stoica , I. , and Ramchandran , K . EC-Cache: Load-Balanced , Low-Latency Cluster Caching with Online Erasure Coding. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) ( 2016 ). Rashmi, K. V., Chowdhury, M., Kosaian, J., Stoica, I., and Ramchandran, K. EC-Cache: Load-Balanced, Low-Latency Cluster Caching with Online Erasure Coding. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) (2016)."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626325"},{"key":"e_1_3_2_1_69_1","volume-title":"Advances in Neural Information Processing Systems (NIPS 11)","author":"Recht B.","year":"2011","unstructured":"Recht , B. , Re , C. , Wright , S. , and Niu , F . Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent . In Advances in Neural Information Processing Systems (NIPS 11) ( 2011 ). Recht, B., Re, C., Wright, S., and Niu, F. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In Advances in Neural Information Processing Systems (NIPS 11) (2011)."},{"key":"e_1_3_2_1_70_1","volume-title":"Polynomial Codes Over Certain Finite Fields. Journal of the society for industrial and applied mathematics 8, 2","author":"Reed I. S.","year":"1960","unstructured":"Reed , I. S. , and Solomon , G . Polynomial Codes Over Certain Finite Fields. Journal of the society for industrial and applied mathematics 8, 2 ( 1960 ), 300--304. Reed, I. S., and Solomon, G. Polynomial Codes Over Certain Finite Fields. Journal of the society for industrial and applied mathematics 8, 2 (1960), 300--304."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2017.8006961"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.5555\/1795974"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/263876.263881"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"crossref","unstructured":"Russakovsky O. Deng J. Su H. Krause J. Satheesh S. Ma S. Huang Z. Karpathy A. Khosla A. Bernstein M. Berg A. C. and Fei-Fei L. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115 3 (2015) 211--252.  Russakovsky O. Deng J. Su H. Krause J. Satheesh S. Ma S. Huang Z. Karpathy A. Khosla A. Bernstein M. Berg A. C. and Fei-Fei L. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115 3 (2015) 211--252.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2015.2506161"},{"key":"e_1_3_2_1_77_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations (ICLR 15)","author":"Simonyan K.","year":"2015","unstructured":"Simonyan , K. , and Zisserman , A . Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations (ICLR 15) ( 2015 ). Simonyan, K., and Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations (ICLR 15) (2015)."},{"key":"e_1_3_2_1_78_1","volume-title":"CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning. arXiv preprint arXiv:1902.00641","author":"So J.","year":"2019","unstructured":"So , J. , Guler , B. , Avestimehr , A. S. , and Mohassel , P . CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning. arXiv preprint arXiv:1902.00641 ( 2019 ). So, J., Guler, B., Avestimehr, A. S., and Mohassel, P. CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning. arXiv preprint arXiv:1902.00641 (2019)."},{"key":"e_1_3_2_1_79_1","volume-title":"12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15)","author":"Suresh L.","year":"2015","unstructured":"Suresh , L. , Canini , M. , Schmid , S. , and Feldmann , A . C3: Cutting Tail Latency in Cloud Data Stores via Adaptive Replica Selection . In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15) ( 2015 ). Suresh, L., Canini, M., Schmid, S., and Feldmann, A. C3: Cutting Tail Latency in Cloud Data Stores via Adaptive Replica Selection. In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15) (2015)."},{"key":"e_1_3_2_1_80_1","volume-title":"Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16)","author":"Venkataraman S.","year":"2016","unstructured":"Venkataraman , S. , Yang , Z. , Franklin , M. , Recht , B. , and Stoica , I . Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16) ( 2016 ). Venkataraman, S., Yang, Z., Franklin, M., Recht, B., and Stoica, I. Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16) (2016)."},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"e_1_3_2_1_82_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML 18)","author":"Wang S.","year":"2018","unstructured":"Wang , S. , Liu , J. , and Shroff , N . Coded Sparse Matrix Multiplication . In Proceedings of the International Conference on Machine Learning (ICML 18) ( 2018 ). Wang, S., Liu, J., and Shroff, N. Coded Sparse Matrix Multiplication. In Proceedings of the International Conference on Machine Learning (ICML 18) (2018)."},{"key":"e_1_3_2_1_83_1","first-page":"2","volume":"12","author":"Wang W.","year":"2018","unstructured":"Wang , W. , Gao , J. , Zhang , M. , Wang , S. , Chen , G. , Ng , T. K. , Ooi , B. C. , Shao , J. , and Reyad , M. Rafiki : Machine Learning as an Analytics Service System. Proceedings of the VLDB Endowment 12 , 2 ( 2018 ), 128--140. Wang, W., Gao, J., Zhang, M., Wang, S., Chen, G., Ng, T. K., Ooi, B. C., Shao, J., and Reyad, M. Rafiki: Machine Learning as an Analytics Service System. Proceedings of the VLDB Endowment 12, 2 (2018), 128--140.","journal-title":"Analytics Service System. Proceedings of the VLDB Endowment"},{"key":"e_1_3_2_1_84_1","volume-title":"Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 18)","author":"Wang X.","year":"2018","unstructured":"Wang , X. , Luo , Y. , Crankshaw , D. , Tumanov , A. , Yu , F. , and Gonzalez , J. E . IDK Cascades: Fast Deep Learning by Learning not to Overthink . In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 18) ( 2018 ). Wang, X., Luo, Y., Crankshaw, D., Tumanov, A., Yu, F., and Gonzalez, J. E. IDK Cascades: Fast Deep Learning by Learning not to Overthink. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 18) (2018)."},{"key":"e_1_3_2_1_85_1","volume-title":"Speech commands: A Dataset for Limited-Vocabulary Speech Recognition. arXiv preprint arXiv:1804.03209","author":"Warden P.","year":"2018","unstructured":"Warden , P. Speech commands: A Dataset for Limited-Vocabulary Speech Recognition. arXiv preprint arXiv:1804.03209 ( 2018 ). Warden, P. Speech commands: A Dataset for Limited-Vocabulary Speech Recognition. arXiv preprint arXiv:1804.03209 (2018)."},{"key":"e_1_3_2_1_87_1","volume-title":"Fashion-Mnist: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:1708.07747","author":"Xiao H.","year":"2017","unstructured":"Xiao , H. , Rasul , K. , and Vollgraf , R . Fashion-Mnist: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:1708.07747 ( 2017 ). Xiao, H., Rasul, K., and Vollgraf, R. Fashion-Mnist: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:1708.07747 (2017)."},{"key":"e_1_3_2_1_88_1","volume-title":"Bobtail: Avoiding Long Tails in the Cloud. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13)","author":"Xu Y.","year":"2013","unstructured":"Xu , Y. , Musgrave , Z. , Noble , B. , and Bailey , M . Bobtail: Avoiding Long Tails in the Cloud. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13) ( 2013 ). Xu, Y., Musgrave, Z., Noble, B., and Bailey, M. Bobtail: Avoiding Long Tails in the Cloud. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13) (2013)."},{"key":"e_1_3_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1145\/2670979.2671005"},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3131614"},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.5555\/3129633.3129636"},{"key":"e_1_3_2_1_92_1","volume-title":"Advances in Neural Information Processing Systems (NIPS 17)","author":"Yu Q.","year":"2017","unstructured":"Yu , Q. , Maddah-Ali , M. , and Avestimehr , S . Polynomial Codes: An Optimal Design for High-Dimensional Coded Matrix Multiplication . In Advances in Neural Information Processing Systems (NIPS 17) ( 2017 ). Yu, Q., Maddah-Ali, M., and Avestimehr, S. Polynomial Codes: An Optimal Design for High-Dimensional Coded Matrix Multiplication. In Advances in Neural Information Processing Systems (NIPS 17) (2017)."},{"key":"e_1_3_2_1_93_1","volume-title":"Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 19)","author":"Yu Q.","year":"2019","unstructured":"Yu , Q. , Raviv , N. , So , J. , and Avestimehr , A. S . Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy . In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 19) ( 2019 ). Yu, Q., Raviv, N., So, J., and Avestimehr, A. S. Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 19) (2019)."},{"key":"e_1_3_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.5555\/1855741.1855744"},{"key":"e_1_3_2_1_95_1","volume-title":"14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17)","author":"Zhang H.","year":"2017","unstructured":"Zhang , H. , Ananthanarayanan , G. , Bodik , P. , Philipose , M. , Bahl , P. , and Freedman , M. J . Live Video Analytics at Scale with Approximation and Delay-Tolerance . In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17) ( 2017 ). Zhang, H., Ananthanarayanan, G., Bodik, P., Philipose, M., Bahl, P., and Freedman, M. J. Live Video Analytics at Scale with Approximation and Delay-Tolerance. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17) (2017)."},{"key":"e_1_3_2_1_96_1","volume-title":"2018 USENIX Annual Technical Conference (USENIX ATC 18)","author":"Zhang M.","year":"2018","unstructured":"Zhang , M. , Rajbhandari , S. , Wang , W. , and He , Y . DeepCPU: Serving RNN-based Deep Learning Models 10x Faster . In 2018 USENIX Annual Technical Conference (USENIX ATC 18) ( 2018 ). Zhang, M., Rajbhandari, S., Wang, W., and He, Y. DeepCPU: Serving RNN-based Deep Learning Models 10x Faster. In 2018 USENIX Annual Technical Conference (USENIX ATC 18) (2018)."},{"key":"e_1_3_2_1_97_1","volume-title":"Neural Architecture Search with Reinforcement Learning. arXiv preprint arXiv:1611.01578","author":"Zoph B.","year":"2016","unstructured":"Zoph , B. , and Le , Q. V . Neural Architecture Search with Reinforcement Learning. arXiv preprint arXiv:1611.01578 ( 2016 ). Zoph, B., and Le, Q. V. Neural Architecture Search with Reinforcement Learning. arXiv preprint arXiv:1611.01578 (2016)."}],"event":{"name":"SOSP '19: ACM SIGOPS 27th Symposium on Operating Systems Principles","location":"Huntsville Ontario Canada","acronym":"SOSP '19","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems","USENIX Assoc USENIX Assoc"]},"container-title":["Proceedings of the 27th ACM Symposium on Operating Systems Principles"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3341301.3359654","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3341301.3359654","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3341301.3359654","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:12:56Z","timestamp":1750201976000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3341301.3359654"}},"subtitle":["erasure-coded resilience for prediction serving systems"],"short-title":[],"issued":{"date-parts":[[2019,10,27]]},"references-count":96,"alternative-id":["10.1145\/3341301.3359654","10.1145\/3341301"],"URL":"https:\/\/doi.org\/10.1145\/3341301.3359654","relation":{},"subject":[],"published":{"date-parts":[[2019,10,27]]},"assertion":[{"value":"2019-10-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}