| {PowerGraph}: Distributed {Graph-Parallel} computation on natural graphs JE Gonzalez, Y Low, H Gu, D Bickson, C Guestrin 10th USENIX symposium on operating systems design and implementation (OSDI …, 2012 | 3292* | 2012 |
| Clipper: A {Low-Latency} online prediction serving system D Crankshaw, X Wang, G Zhou, MJ Franklin, JE Gonzalez, I Stoica 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2017 | 710 | 2017 |
| The missing piece in complex analytics: Low latency, scalable model management and serving with velox D Crankshaw, P Bailis, JE Gonzalez, H Li, Z Zhang, MJ Franklin, A Ghodsi, ... arXiv preprint arXiv:1409.3809, 2014 | 128 | 2014 |
| Idk cascades: Fast deep learning by learning not to overthink X Wang, Y Luo, D Crankshaw, A Tumanov, F Yu, JE Gonzalez arXiv preprint arXiv:1706.00885, 2017 | 126 | 2017 |
| Graphx: Unifying data-parallel and graph-parallel analytics RS Xin, D Crankshaw, A Dave, JE Gonzalez, MJ Franklin, I Stoica arXiv preprint arXiv:1402.2394, 2014 | 126 | 2014 |
| InferLine: latency-aware provisioning and scaling for prediction serving pipelines D Crankshaw, GE Sela, X Mo, C Zumar, I Stoica, J Gonzalez, A Tumanov Proceedings of the 11th ACM Symposium on Cloud Computing, 477-491, 2020 | 120 | 2020 |
| Context: The missing piece in the machine learning lifecycle R Garcia, V Sreekanti, N Yadwadkar, D Crankshaw, JE Gonzalez, ... KDD CMI Workshop 114, 1-4, 2018 | 48 | 2018 |
| Inferline: Ml inference pipeline composition framework D Crankshaw, GE Sela, C Zumar, X Mo, JE Gonzalez, I Stoica, ... arXiv preprint arXiv:1812.01776, 2018 | 38 | 2018 |
| Composing meta-policies for autonomous driving using hierarchical deep reinforcement learning R Liaw, S Krishnan, A Garg, D Crankshaw, JE Gonzalez, K Goldberg arXiv preprint arXiv:1711.01503, 2017 | 23 | 2017 |
| SOL: Safe on-node learning in cloud platforms Y Wang, D Crankshaw, NJ Yadwadkar, D Berger, C Kozyrakis, ... Proceedings of the 27th ACM International Conference on Architectural …, 2022 | 20 | 2022 |
| Scalable training and serving of personalized models D Crankshaw, X Wang, JE Gonzalez, MJ Franklin NIPS 2015 Workshop on Machine Learning Systems (LearningSys), 2015 | 10 | 2015 |
| The design and implementation of low-latency prediction serving systems D Crankshaw University of California, Berkeley, 2019 | 7 | 2019 |
| Inverted indices for particle tracking in petascale cosmological simulations D Crankshaw, R Burns, B Falck, T Budavári, AS Szalay, J Wang Proceedings of the 25th International Conference on Scientific and …, 2013 | 6 | 2013 |
| Solving {Max-Min} Fair Resource Allocations Quickly on Large Graphs P Namyar, B Arzani, S Kandula, S Segarra, D Crankshaw, ... 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2024 | 4 | 2024 |
| The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CoRR abs/1409.3809 (2014) D Crankshaw, P Bailis, JE Gonzalez, H Li, Z Zhang, MJ Franklin, A Ghodsi, ... arXiv preprint arXiv:1409.3809, 2014 | 4 | 2014 |
| InferLine: ML prediction pipeline provisioning and management for tight latency objectives D Crankshaw, GE Sela, C Zumar, X Mo, JE Gonzalez, I Stoica, ... arXiv preprint arXiv:1812.01776, 2018 | 2 | 2018 |
| Impact-aware mitigation for computer networks B Arzani, P Namyar, DS Crankshaw, DS Berger, T Hsieh, S Kandula US Patent 11,611,466, 2023 | 1 | 2023 |
| The Indra Simulation Database B Falck, T Budavari, S Cole, D Crankshaw, L Dobos, G Lemson, ... American Astronomical Society Meeting Abstracts# 218 218, 131.04, 2011 | 1 | 2011 |
| SOLVING MAX-MIN FAIR RESOURCE ALLOCATION AT LARGE SCALE B Arzani, P Namyar, S Kandula, U Krishnaswamy, H Raj, SM Segarra, ... US Patent App. 18/323,126, 2024 | | 2024 |
| Impact-aware mitigation for computer networks B Arzani, P Namyar, DS Crankshaw, DS Berger, T Hsieh, S Kandula US Patent 12,063,142, 2024 | | 2024 |
