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Daniel Crankshaw
Daniel Crankshaw
Graduate Student, UC Berkeley
Verified email at cs.berkeley.edu
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Cited by
Cited by
Year
{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
3170*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
6102017
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
1242014
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
1202014
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
1052017
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
872020
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
442018
Inferline: Ml inference pipeline composition framework
D Crankshaw, GE Sela, C Zumar, X Mo, JE Gonzalez, I Stoica, ...
arXiv preprint arXiv:1812.01776, 2018
402018
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
182017
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
122022
Scalable training and serving of personalized models
D Crankshaw, X Wang, JE Gonzalez, MJ Franklin
NIPS 2015 Workshop on Machine Learning Systems (LearningSys), 2015
102015
The design and implementation of low-latency prediction serving systems
D Crankshaw
University of California, Berkeley, 2019
72019
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
62013
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
42014
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
22018
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs
P Namyar, B Arzani, S Kandula, S Segarra, D Crankshaw, ...
arXiv preprint arXiv:2310.09699, 2023
12023
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
12011
Impact-aware mitigation for computer networks
B Arzani, P Namyar, DS Crankshaw, DS Berger, T Hsieh, S Kandula
US Patent App. 18/182,348, 2023
2023
Mitigating the Performance Impact of Network Failures in Public Clouds
P Namyar, B Arzani, D Crankshaw, DS Berger, K Hsieh, S Kandula, ...
arXiv preprint arXiv:2305.13792, 2023
2023
Impact-aware mitigation for computer networks
B Arzani, P Namyar, DS Crankshaw, DS Berger, T Hsieh, S Kandula
US Patent 11,611,466, 2023
2023
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