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2020 – today
- 2024
[c8]Pooria Namyar, Behnaz Arzani, Srikanth Kandula, Santiago Segarra, Daniel Crankshaw, Umesh Krishnaswamy, Ramesh Govindan, Himanshu Raj:
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs. NSDI 2024- 2023
- [i9]Pooria Namyar, Behnaz Arzani, Daniel Crankshaw, Daniel S. Berger, Kevin Hsieh, Srikanth Kandula, Ramesh Govindan:
Mitigating the Performance Impact of Network Failures in Public Clouds. CoRR abs/2305.13792 (2023) - [i8]Pooria Namyar, Behnaz Arzani, Srikanth Kandula, Santiago Segarra, Daniel Crankshaw, Umesh Krishnaswamy, Ramesh Govindan, Himanshu Raj:
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs. CoRR abs/2310.09699 (2023) - 2022
- [c7]Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar, Daniel S. Berger, Christos Kozyrakis, Ricardo Bianchini:
SOL: safe on-node learning in cloud platforms. ASPLOS 2022: 622-634 - [i7]Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar, Daniel S. Berger, Christos Kozyrakis, Ricardo Bianchini:
SOL: Safe On-Node Learning in Cloud Platforms. CoRR abs/2201.10477 (2022) - 2020
- [c6]Daniel Crankshaw, Gur-Eyal Sela, Xiangxi Mo, Corey Zumar, Ion Stoica, Joseph Gonzalez
, Alexey Tumanov:
InferLine: latency-aware provisioning and scaling for prediction serving pipelines. SoCC 2020: 477-491
2010 – 2019
- 2019
- [b1]Daniel Crankshaw:
The Design and Implementation of Low-Latency Prediction Serving Systems. University of California, Berkeley, USA, 2019 - 2018
- [j1]Daniel Crankshaw, Joseph Gonzalez, Peter Bailis:
Research for practice: prediction-serving systems. Commun. ACM 61(8): 45-49 (2018) - [c5]Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez:
IDK Cascades: Fast Deep Learning by Learning not to Overthink. UAI 2018: 580-590 - [i6]Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar, Xiangxi Mo, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov:
InferLine: ML Inference Pipeline Composition Framework. CoRR abs/1812.01776 (2018) - 2017
- [c4]Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, Ion Stoica:
Clipper: A Low-Latency Online Prediction Serving System. NSDI 2017: 613-627 - [i5]Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Joseph E. Gonzalez:
IDK Cascades: Fast Deep Learning by Learning not to Overthink. CoRR abs/1706.00885 (2017) - [i4]Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg:
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning. CoRR abs/1711.01503 (2017) - 2016
- [i3]Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, Ion Stoica:
Clipper: A Low-Latency Online Prediction Serving System. CoRR abs/1612.03079 (2016) - 2015
- [c3]Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan:
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CIDR 2015 - 2014
- [c2]Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, Ion Stoica:
GraphX: Graph Processing in a Distributed Dataflow Framework. OSDI 2014: 599-613 - [i2]Reynold S. Xin, Daniel Crankshaw, Ankur Dave, Joseph E. Gonzalez, Michael J. Franklin, Ion Stoica:
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics. CoRR abs/1402.2394 (2014) - [i1]Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan:
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CoRR abs/1409.3809 (2014) - 2013
- [c1]Daniel Crankshaw, Randal C. Burns, Bridget Falck, Tamas Budavari, Alexander S. Szalay, Jie Wang:
Inverted indices for particle tracking in petascale cosmological simulations. SSDBM 2013: 25:1-25:10
Coauthor Index
aka: Joseph E. Gonzalez
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