Deepak Narayanan
Deepak Narayanan
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
DAWNBench: An End-to-End Deep Learning Benchmark and Competition
C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ...
NeurIPS Workshop on Systems for Machine Learning, 2017
1362017
Macrobase: Prioritizing Attention in Fast Data
P Bailis, E Gan, S Madden, D Narayanan, K Rong, S Suri
Proceedings of the 2017 ACM International Conference on Management of Data …, 2017
1282017
Weld: A Common Runtime for High Performance Data Analytics
S Palkar, JJ Thomas, A Shanbhag, D Narayanan, H Pirk, M Schwarzkopf, ...
Conference on Innovative Data Systems Research (CIDR), 2017
962017
PipeDream: Fast and Efficient Pipeline Parallel DNN Training
A Harlap, D Narayanan, A Phanishayee, V Seshadri, N Devanur, ...
arXiv preprint arXiv:1806.03377, 2018
632018
MLPerf Training Benchmark
P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ...
Third Conference on Machine Learning and Systems, 2020
452020
MLPerf Training Benchmark
P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ...
arXiv preprint arXiv:1910.01500, 2019
452019
PipeDream: Generalized Pipeline Parallelism for DNN Training
D Narayanan, A Harlap, A Phanishayee, V Seshadri, NR Devanur, ...
27th ACM Symposium on Operating Systems Principles, 1-15, 2019
432019
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
C Coleman, D Kang, D Narayanan, L Nardi, T Zhao, J Zhang, P Bailis, ...
ACM SIGOPS Operating Systems Review 53 (1), 14-25, 2019
362019
Evaluating End-to-End Optimization for Data Analytics Applications in Weld
S Palkar, J Thomas, D Narayanan, P Thaker, R Palamuttam, P Negi, ...
Proceedings of the VLDB Endowment 11 (9), 1002-1015, 2018
332018
Weld: Rethinking the Interface between Data-Intensive Applications
S Palkar, J Thomas, D Narayanan, A Shanbhag, R Palamuttam, H Pirk, ...
arXiv preprint arXiv:1709.06416, 2017
172017
Accelerating Deep Learning Workloads through Efficient Multi-Model Execution
D Narayanan, K Santhanam, A Phanishayee, M Zaharia
NeurIPS Workshop on Systems for Machine Learning, 2018
152018
Macrobase: Analytic Monitoring for the Internet of Things
P Bailis, E Gan, S Madden, D Narayanan, K Rong, S Suri
arXiv preprint arXiv:1603.00567 7, 2016
92016
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
P Kraft, D Kang, D Narayanan, S Palkar, P Bailis, M Zaharia
Third Conference on Machine Learning and Systems, 2020
42020
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
P Kraft, D Kang, D Narayanan, S Palkar, P Bailis, M Zaharia
arXiv preprint arXiv:1906.01974, 2019
42019
Weld: Rethinking the Interface Between Data-Intensive Applications. CoRR abs/1709.06416 (2017)
S Palkar, JJ Thomas, D Narayanan, A Shanbhag, R Palamuttam, H Pirk, ...
arXiv preprint arXiv:1709.06416, 2017
42017
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads
D Narayanan, K Santhanam, F Kazhamiaka, A Phanishayee, M Zaharia
arXiv preprint arXiv:2008.09213, 2020
22020
Memory-Efficient Pipeline-Parallel DNN Training
D Narayanan, A Phanishayee, K Shi, X Chen, M Zaharia
arXiv preprint arXiv:2006.09503, 2020
22020
MacroBase: Prioritizing Attention in Fast Data
F Abuzaid, P Bailis, J Ding, E Gan, S Madden, D Narayanan, K Rong, ...
ACM Transactions on Database Systems (TODS) 43 (4), 1-45, 2018
22018
Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads
G Yuan, S Palkar, D Narayanan, M Zaharia
2020 USENIX Annual Technical Conference (USENIX ATC 20), 293-306, 2020
12020
A Demonstration of Willump: A Statistically-Aware End-to-End Optimizer for Machine Learning Inference
P Kraft, D Kang, D Narayanan, S Palkar, P Bailis, M Zaharia
Proceedings of the VLDB Endowment 13 (12), 2833-2836, 2020
2020
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