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ORCIDMatei Zaharia
Matei A. Zaharia
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- affiliation: Stanford University, CA, USA
- award (2019): Presidential Early Career Award for Scientists and Engineers
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2020 – today
- 2021
[j26]Firas Abuzaid
, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Ananthanarayan, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia:
DIFF: a relational interface for large-scale data explanation. VLDB J. 30(1): 45-70 (2021)- [c71]Fiodar Kazhamiaka, Matei Zaharia, Peter Bailis:
Challenges and Opportunities for Autonomous Vehicle Query Systems. CIDR 2021 - [c70]Matei Zaharia, Ali Ghodsi, Reynold Xin, Michael Armbrust:
Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. CIDR 2021 - [c69]Deepti Raghavan, Philip Levis, Matei Zaharia, Irene Zhang:
Breakfast of champions: towards zero-copy serialization with NIC scatter-gather. HotOS 2021: 199-205 - [c68]Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, Peter Bailis:
Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. NSDI 2021: 175-200 - [i43]Omar Khattab, Christopher Potts, Matei Zaharia:
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval. CoRR abs/2101.00436 (2021) - [i42]Lingjiao Chen, Matei Zaharia, James Zou:
FrugalMCT: Efficient Online ML API Selection for Multi-Label Classification Tasks. CoRR abs/2102.09127 (2021) - [i41]Akshay Agrawal, Stephen P. Boyd, Deepak Narayanan, Fiodar Kazhamiaka, Matei Zaharia:
Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method. CoRR abs/2104.00282 (2021) - [i40]Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia:
Efficient Large-Scale Language Model Training on GPU Clusters. CoRR abs/2104.04473 (2021) - [i39]Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Matei Zaharia:
Don't Give Up on Large Optimization Problems; POP Them! CoRR abs/2104.06513 (2021) - 2020
- [j25]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. Proc. VLDB Endow. 13(11): 1990-2003 (2020) - [j24]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. Proc. VLDB Endow. 13(12): 2833-2836 (2020) - [j23]Michael Armbrust, Tathagata Das, Sameer Paranjpye, Reynold Xin, Shixiong Zhu, Ali Ghodsi, Burak Yavuz, Mukul Murthy, Joseph Torres, Liwen Sun, Peter A. Boncz, Mostafa Mokhtar, Herman Van Hovell, Adrian Ionescu, Alicja Luszczak, Michal Switakowski, Takuya Ueshin, Xiao Li, Michal Szafranski, Pieter Senster, Matei Zaharia:
Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores. Proc. VLDB Endow. 13(12): 3411-3424 (2020) - [j22]Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia:
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. Proc. VLDB Endow. 14(2): 87-100 (2020) - [j21]Deepti Raghavan, Sadjad Fouladi, Philip Levis, Matei Zaharia:
Posh: A Data-Aware Shell. login Usenix Mag. 45(4) (2020) - [c67]James J. Thomas, Pat Hanrahan, Matei Zaharia:
Fleet: A Framework for Massively Parallel Streaming on FPGAs. ASPLOS 2020: 639-651 - [c66]Benyu Zhang, Matei Zaharia, Shouling Ji, Raluca Ada Popa, Guofei Gu:
PPMLP 2020: Workshop on Privacy-Preserving Machine Learning In Practice. CCS 2020: 2139-2140 - [c65]Michael J. Cafarella, David J. DeWitt, Vijay Gadepally, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Matei Zaharia:
A Polystore Based Database Operating System (DBOS). Poly/DMAH@VLDB 2020: 3-24 - [c64]Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia:
Selection via Proxy: Efficient Data Selection for Deep Learning. ICLR 2020 - [c63]Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, Alex Aiken:
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc. MLSys 2020 - [c62]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. MLSys 2020 - [c61]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. MLSys 2020 - [c60]Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. MLSys 2020 - [c59]Lingjiao Chen, Matei Zaharia, James Y. Zou:
FrugalML: How to use ML Prediction APIs more accurately and cheaply. NeurIPS 2020 - [c58]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. OSDI 2020: 481-498 - [c57]Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen:
Sparse GPU kernels for deep learning. SC 2020: 17 - [c56]Omar Khattab, Matei Zaharia:
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. SIGIR 2020: 39-48 - [c55]Andrew Chen, Andy Chow, Aaron Davidson, Arjun DCunha, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Clemens Mewald, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, Avesh Singh, Fen Xie, Matei Zaharia, Richard Zang, Juntai Zheng, Corey Zumar:
Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle. DEEM@SIGMOD 2020: 5:1-5:4 - [c54]Saachi Jain, Matei Zaharia:
Spectral Lower Bounds on the I/O Complexity of Computation Graphs. SPAA 2020: 329-338 - [c53]Gina Yuan, Shoumik Palkar, Deepak Narayanan, Matei Zaharia:
Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads. USENIX Annual Technical Conference 2020: 293-306 - [c52]Deepti Raghavan, Sadjad Fouladi, Philip Levis, Matei Zaharia:
POSH: A Data-Aware Shell. USENIX Annual Technical Conference 2020: 617-631 - [i38]Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia:
Model Assertions for Monitoring and Improving ML Models. CoRR abs/2003.01668 (2020) - [i37]Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Approximate Selection with Guarantees using Proxies. CoRR abs/2004.00827 (2020) - [i36]Omar Khattab, Matei Zaharia:
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. CoRR abs/2004.12832 (2020) - [i35]Lingjiao Chen, Matei Zaharia, James Zou:
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply. CoRR abs/2006.07512 (2020) - [i34]Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia:
Memory-Efficient Pipeline-Parallel DNN Training. CoRR abs/2006.09503 (2020) - [i33]Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen:
Sparse GPU Kernels for Deep Learning. CoRR abs/2006.10901 (2020) - [i32]Pratiksha Thaker, Mihai Budiu, Parikshit Gopalan, Udi Wieder, Matei Zaharia:
Overlook: Differentially Private Exploratory Visualization for Big Data. CoRR abs/2006.12018 (2020) - [i31]Cody Coleman, Edward Chou, Sean Culatana, Peter Bailis, Alexander C. Berg, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz:
Similarity Search for Efficient Active Learning and Search of Rare Concepts. CoRR abs/2007.00077 (2020) - [i30]Omar Khattab, Christopher Potts, Matei Zaharia:
Relevance-guided Supervision for OpenQA with ColBERT. CoRR abs/2007.00814 (2020) - [i29]Michael J. Cafarella, David J. DeWitt, Vijay Gadepally, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Matei Zaharia:
DBOS: A Proposal for a Data-Centric Operating System. CoRR abs/2007.11112 (2020) - [i28]Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, Matei Zaharia:
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. CoRR abs/2007.13005 (2020) - [i27]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. CoRR abs/2008.09213 (2020) - [i26]Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia:
Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. CoRR abs/2009.04540 (2020)
2010 – 2019
- 2019
- [j20]Saba Eskandarian, Matei Zaharia:
ObliDB: Oblivious Query Processing for Secure Databases. Proc. VLDB Endow. 13(2): 169-183 (2019) - [j19]Daniel Kang, Peter Bailis, Matei Zaharia:
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Proc. VLDB Endow. 13(4): 533-546 (2019) - [j18]Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. ACM SIGOPS Oper. Syst. Rev. 53(1): 14-25 (2019) - [j17]Sadjad Fouladi, Francisco Romero, Dan Iter, Qian Li, Alex Ozdemir, Shuvo Chatterjee, Matei Zaharia, Christos Kozyrakis, Keith Winstein:
Outsourcing Everyday Jobs to Thousands of Cloud Functions with gg. login Usenix Mag. 44(3) (2019) - [c51]Daniel Kang, Peter Bailis, Matei Zaharia:
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR 2019 - [c50]Matei Zaharia:
Lessons from Large-Scale Software as a Service at Databricks. SoCC 2019: 101 - [c49]Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia:
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search. ICDE 2019: 1250-1261 - [c48]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Learned Intermediate Representation Training for Model Compression. ICML 2019: 3509-3518 - [c47]Zhihao Jia, James J. Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, Alex Aiken:
Optimizing DNN Computation with Relaxed Graph Substitutions. MLSys 2019 - [c46]Zhihao Jia, Matei Zaharia, Alex Aiken:
Beyond Data and Model Parallelism for Deep Neural Networks. MLSys 2019 - [c45]Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia:
PipeDream: generalized pipeline parallelism for DNN training. SOSP 2019: 1-15 - [c44]Zhihao Jia, Oded Padon, James J. Thomas, Todd Warszawski, Matei Zaharia, Alex Aiken:
TASO: optimizing deep learning computation with automatic generation of graph substitutions. SOSP 2019: 47-62 - [c43]Shoumik Palkar, Matei Zaharia:
Optimizing data-intensive computations in existing libraries with split annotations. SOSP 2019: 291-305 - [c42]Sadjad Fouladi, Francisco Romero, Dan Iter, Qian Li, Shuvo Chatterjee, Christos Kozyrakis, Matei Zaharia, Keith Winstein:
From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers. USENIX Annual Technical Conference 2019: 475-488 - [e1]Ameet Talwalkar, Virginia Smith, Matei Zaharia:
Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019. mlsys.org 2019 [contents] - [i25]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i24]Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia:
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. CoRR abs/1906.01974 (2019) - [i23]Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia:
Selection Via Proxy: Efficient Data Selection For Deep Learning. CoRR abs/1906.11829 (2019) - [i22]Saachi Jain, Matei Zaharia:
Automated Lower Bounds on the I/O Complexity of Computation Graphs. CoRR abs/1909.09791 (2019) - [i21]Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia:
MLPerf Training Benchmark. CoRR abs/1910.01500 (2019) - [i20]Saba Eskandarian, Henry Corrigan-Gibbs, Matei Zaharia, Dan Boneh:
Express: Lowering the Cost of Metadata-hiding Communication with Cryptographic Privacy. CoRR abs/1911.09215 (2019) - 2018
- [j16]Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe, Fen Xie, Corey Zumar:
Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Eng. Bull. 41(4): 39-45 (2018) - [j15]Shoumik Palkar, James J. Thomas, Deepak Narayanan, Pratiksha Thaker, Rahul Palamuttam, Parimarjan Negi, Anil Shanbhag, Malte Schwarzkopf, Holger Pirk, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia:
Evaluating End-to-End Optimization for Data Analytics Applications in Weld. Proc. VLDB Endow. 11(9): 1002-1015 (2018) - [j14]Shoumik Palkar, Firas Abuzaid, Peter Bailis, Matei Zaharia:
Filter Before You Parse: Faster Analytics on Raw Data with Sparser. Proc. VLDB Endow. 11(11): 1576-1589 (2018) - [j13]Firas Abuzaid, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Anathanaraya, John Sheu, Erik Meijer, Xi Wu, Jeffrey F. Naughton, Peter Bailis, Matei Zaharia:
DIFF: A Relational Interface for Large-Scale Data Explanation. Proc. VLDB Endow. 12(4): 419-432 (2018) - [c41]Michael Armbrust, Tathagata Das, Joseph Torres, Burak Yavuz, Shixiong Zhu, Reynold Xin, Ali Ghodsi, Ion Stoica, Matei Zaharia:
Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark. SIGMOD Conference 2018: 601-613 - [c40]Manasi Vartak, Joana M. F. da Trindade, Samuel Madden, Matei Zaharia:
MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis. SIGMOD Conference 2018: 1285-1300 - [r1]Volker Markl, Vinayak R. Borkar, Matei Zaharia, Till Westmann, Alexander Alexandrov:
Big Data Platforms for Data Analytics. Encyclopedia of Database Systems (2nd ed.) 2018 - [i19]Daniel Kang, Peter Bailis, Matei Zaharia:
BlazeIt: Fast Exploratory Video Queries using Neural Networks. CoRR abs/1805.01046 (2018) - [i18]Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. CoRR abs/1806.01427 (2018) - [i17]Zhihao Jia, Matei Zaharia, Alex Aiken:
Beyond Data and Model Parallelism for Deep Neural Networks. CoRR abs/1807.05358 (2018) - [i16]Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Block-wise Intermediate Representation Training for Model Compression. CoRR abs/1810.01937 (2018) - [i15]Shoumik Palkar, Matei Zaharia:
Splitability Annotations: Optimizing Black-Box Function Composition in Existing Libraries. CoRR abs/1810.12297 (2018) - 2017
- [j12]Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia:
NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. Proc. VLDB Endow. 10(11): 1586-1597 (2017) - [c39]Yunming Zhang, Vladimir Kiriansky, Charith Mendis, Saman P. Amarasinghe, Matei Zaharia:
Making caches work for graph analytics. IEEE BigData 2017: 293-302 - [c38]Shoumik Palkar, James J. Thomas, Anil Shanbhag, Malte Schwarzkopf, Saman P. Amarasinghe, Matei Zaharia:
A Common Runtime for High Performance Data Analysis. CIDR 2017 - [c37]Shoumik Palkar, Matei Zaharia:
DIY Hosting for Online Privacy. HotNets 2017: 1-7 - [c36]Frank Wang, Catherine Yun, Shafi Goldwasser, Vinod Vaikuntanathan, Matei Zaharia:
Splinter: Practical Private Queries on Public Data. NSDI 2017: 299-313 - [c35]Nirvan Tyagi, Yossi Gilad, Derek Leung, Matei Zaharia, Nickolai Zeldovich:
Stadium: A Distributed Metadata-Private Messaging System. SOSP 2017: 423-440 - [i14]Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia:
Optimizing Deep CNN-Based Queries over Video Streams at Scale. CoRR abs/1703.02529 (2017) - [i13]Peter Bailis, Kunle Olukotun, Christopher Ré, Matei Zaharia:
Infrastructure for Usable Machine Learning: The Stanford DAWN Project. CoRR abs/1705.07538 (2017) - [i12]Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia:
SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations. CoRR abs/1706.01449 (2017) - [i11]Shoumik Palkar, James J. Thomas, Deepak Narayanan, Anil Shanbhag, Rahul Palamuttam, Holger Pirk, Malte Schwarzkopf, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia:
Weld: Rethinking the Interface Between Data-Intensive Applications. CoRR abs/1709.06416 (2017) - [i10]Saba Eskandarian, Matei Zaharia:
An Oblivious General-Purpose SQL Database for the Cloud. CoRR abs/1710.00458 (2017) - 2016
- [j11]Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, Ion Stoica:
Apache Spark: a unified engine for big data processing. Commun. ACM 59(11): 56-65 (2016) - [j10]Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan R. Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar:
MLlib: Machine Learning in Apache Spark. J. Mach. Learn. Res. 17: 34:1-34:7 (2016) - [j9]Holger Pirk, Oscar R. Moll, Matei Zaharia, Sam Madden:
Voodoo - A Vector Algebra for Portable Database Performance on Modern Hardware. Proc. VLDB Endow. 9(14): 1707-1718 (2016) - [c34]Ankur Dave, Alekh Jindal, Li Erran Li, Reynold Xin, Joseph Gonzalez, Matei Zaharia:
GraphFrames: an integrated API for mixing graph and relational queries. GRADES 2016: 2 - [c33]Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan R. Sparks, Aaron Staple, Matei Zaharia:
Matrix Computations and Optimization in Apache Spark. KDD 2016: 31-38 - [c32]Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar:
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. NIPS 2016: 3810-3818 - [c31]Qifan Pu, Haoyuan Li, Matei Zaharia, Ali Ghodsi, Ion Stoica:
FairRide: Near-Optimal, Fair Cache Sharing. NSDI 2016: 393-406 - [c30]Manasi Vartak, Harihar Subramanyam, Wei-En Lee, Srinidhi Viswanathan, Saadiyah Husnoo, Samuel Madden, Matei Zaharia:
ModelDB: a system for machine learning model management. HILDA@SIGMOD 2016: 14 - [c29]Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael J. Franklin, Ion Stoica, Matei Zaharia:
SparkR: Scaling R Programs with Spark. SIGMOD Conference 2016: 1099-1104 - [c28]Michael Armbrust, Doug Bateman, Reynold Xin, Matei Zaharia:
Introduction to Spark 2.0 for Database Researchers. SIGMOD Conference 2016: 2193-2194 - [i9]Yunming Zhang, Vladimir Kiriansky, Charith Mendis, Matei Zaharia, Saman P. Amarasinghe:
Optimizing Cache Performance for Graph Analytics. CoRR abs/1608.01362 (2016) - [i8]Nirvan Tyagi, Yossi Gilad, Matei Zaharia, Nickolai Zeldovich:
Stadium: A Distributed Metadata-Private Messaging System. IACR Cryptol. ePrint Arch. 2016: 943 (2016) - [i7]Frank Wang, Catherine Yun, Shafi Goldwasser, Vinod Vaikuntanathan, Matei Zaharia:
Splinter: Practical Private Queries on Public Data. IACR Cryptol. ePrint Arch. 2016: 1148 (2016) - 2015
- [j8]Michael Armbrust, Tathagata Das, Aaron Davidson, Ali Ghodsi, Andrew Or, Josh Rosen, Ion Stoica, Patrick Wendell, Reynold Xin, Matei Zaharia:
Scaling Spark in the Real World: Performance and Usability. Proc. VLDB Endow. 8(12): 1840-1843 (2015) - [c27]Michael Armbrust, Reynold S. Xin, Cheng Lian, Yin Huai, Davies Liu, Joseph K. Bradley, Xiangrui Meng, Tomer Kaftan, Michael J. Franklin, Ali Ghodsi, Matei Zaharia:
Spark SQL: Relational Data Processing in Spark. SIGMOD Conference 2015: 1383-1394 - [c26]Jelle van den Hooff, David Lazar, Matei Zaharia, Nickolai Zeldovich:
Vuvuzela: scalable private messaging resistant to traffic analysis. SOSP 2015: 137-152 - [i6]Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan R. Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar:
MLlib: Machine Learning in Apache Spark. CoRR abs/1505.06807 (2015) - [i5]Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan R. Sparks, Alexander Ulanov, Matei Zaharia:
linalg: Matrix Computations in Apache Spark. CoRR abs/1509.02256 (2015) - 2014
- [c25]Haoyuan Li, Ali Ghodsi, Matei Zaharia, Scott Shenker, Ion Stoica:
Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks. SoCC 2014: 6:1-6:15 - 2013
- [b1]Matei A. Zaharia:
An Architecture for and Fast and General Data Processing on Large Clusters. University of California, Berkeley, USA, 2013 - [j7]Timothy Hunter, Tathagata Das, Matei Zaharia, Pieter Abbeel, Alexandre M. Bayen:
Large-Scale Estimation in Cyberphysical Systems Using Streaming Data: A Case Study With Arterial Traffic Estimation. IEEE Trans Autom. Sci. Eng. 10(4): 884-898 (2013) - [c24]Ali Ghodsi, Matei Zaharia, Scott Shenker, Ion Stoica:
Choosy: max-min fair sharing for datacenter jobs with constraints. EuroSys 2013: 365-378 - [c23]Reynold S. Xin, Josh Rosen, Matei Zaharia, Michael J. Franklin, Scott Shenker, Ion Stoica:
Shark: SQL and rich analytics at scale. SIGMOD Conference 2013: 13-24 - [c22]Kay Ousterhout, Patrick Wendell, Matei Zaharia, Ion Stoica:
Sparrow: distributed, low latency scheduling. SOSP 2013: 69-84 - [c21]Matei Zaharia, Tathagata Das, Haoyuan Li, Timothy Hunter, Scott Shenker, Ion Stoica:
Discretized streams: fault-tolerant streaming computation at scale. SOSP 2013: 423-438 - 2012
- [j6]Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, James Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion Stoica:
Fast and Interactive Analytics over Hadoop Data with Spark. login Usenix Mag. 37(4) (2012) - [c20]Anna N. Rafferty, Matei Zaharia, Thomas L. Griffiths:
Optimally Designing Games for Cognitive Science Research. CogSci 2012 - [c19]Matei Zaharia, Tathagata Das, Haoyuan Li, Scott Shenker, Ion Stoica:
Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters. HotCloud 2012 - [c18]Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauly, Michael J. Franklin, Scott Shenker, Ion Stoica:
Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. NSDI 2012: 15-28 - [c17]