Deepak Narayanan
Deepak Narayanan
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On the Opportunities and Risks of Foundation Models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ...
arXiv preprint arXiv:2211.05100, 2022
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
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
Holistic Evaluation of Language Models
P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ...
arXiv preprint arXiv:2211.09110, 2022
MLPerf Training Benchmark
P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ...
Third Conference on Machine Learning and Systems, 2020
Efficient Large-Scale Language Model Training on GPU Clusters using Megatron-LM
D Narayanan, M Shoeybi, J Casper, P LeGresley, M Patwary, ...
Proceedings of the International Conference for High Performance Computing …, 2021
PipeDream: Fast and Efficient Pipeline Parallel DNN Training
A Harlap, D Narayanan, A Phanishayee, V Seshadri, N Devanur, ...
arXiv preprint arXiv:1806.03377, 2018
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
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
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads
D Narayanan, K Santhanam, F Kazhamiaka, A Phanishayee, M Zaharia
14th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2020
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
Memory-Efficient Pipeline-Parallel DNN Training
D Narayanan, A Phanishayee, K Shi, X Chen, M Zaharia
International Conference on Machine Learning, 7937-7947, 2021
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
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
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP
D Narayanan, F Kazhamiaka, F Abuzaid, P Kraft, A Agrawal, S Kandula, ...
28th ACM Symposium on Operating Systems Principles, 2021
Weld: Rethinking the Interface Between Data-Intensive Applications
S Palkar, JJ Thomas, D Narayanan, A Shanbhag, R Palamuttam, H Pirk, ...
arXiv preprint arXiv:1709.06416, 2017
Analysis and Exploitation of Dynamic Pricing in the Public Cloud for ML Training
D Narayanan, K Santhanam, F Kazhamiaka, A Phanishayee, M Zaharia
Workshop on Distributed Infrastructure, Systems, Programming and AI (DISPA), 2020
Piper: Multidimensional Planner for DNN Parallelization
JM Tarnawski, D Narayanan, A Phanishayee
Advances in Neural Information Processing Systems 34, 24829-24840, 2021
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
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