| Apache spark: a unified engine for big data processing M Zaharia, RS Xin, P Wendell, T Das, M Armbrust, A Dave, X Meng, ... Communications of the ACM 59 (11), 56-65, 2016 | 2620 | 2016 |
| Mllib: Machine learning in apache spark X Meng, J Bradley, B Yavuz, E Sparks, S Venkataraman, D Liu, ... The Journal of Machine Learning Research 17 (1), 1235-1241, 2016 | 2195 | 2016 |
| Ernest: Efficient performance prediction for large-scale advanced analytics S Venkataraman, Z Yang, M Franklin, B Recht, I Stoica 13th {USENIX} symposium on networked systems design and implementation …, 2016 | 531 | 2016 |
| Consistent and durable data structures for non-volatile byte-addressable memory S Venkataraman, N Tolia, P Ranganathan, RH Campbell Proceedings of the 9th USENIX Conference on File and Storage Technologies …, 2011 | 519 | 2011 |
| CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. O Alipourfard, HH Liu, J Chen, S Venkataraman, M Yu, M Zhang NSDI 2, 4-2, 2017 | 518 | 2017 |
| Occupy the cloud: Distributed computing for the 99% E Jonas, Q Pu, S Venkataraman, I Stoica, B Recht Proceedings of the 2017 symposium on cloud computing, 445-451, 2017 | 495 | 2017 |
| Focus: Querying large video datasets with low latency and low cost K Hsieh, G Ananthanarayanan, P Bodik, S Venkataraman, P Bahl, ... 13th {USENIX} Symposium on Operating Systems Design and Implementation …, 2018 | 258 | 2018 |
| Probabilistically Bounded Staleness for Practical Partial Quorums P Bailis, S Venkataraman, JM Hellerstein, M Franklin, I Stoica | 257 | 2012 |
| Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. M Jeon, S Venkataraman, A Phanishayee, J Qian, W Xiao, F Yang USENIX Annual Technical Conference, 947-960, 2019 | 235 | 2019 |
| Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure. Q Pu, S Venkataraman, I Stoica NSDI 19, 193-206, 2019 | 213 | 2019 |
| Drizzle: Fast and adaptable stream processing at scale S Venkataraman, A Panda, K Ousterhout, M Armbrust, A Ghodsi, ... Proceedings of the 26th Symposium on Operating Systems Principles, 374-389, 2017 | 180 | 2017 |
| Keystoneml: Optimizing pipelines for large-scale advanced analytics ER Sparks, S Venkataraman, T Kaftan, MJ Franklin, B Recht 2017 IEEE 33rd international conference on data engineering (ICDE), 535-546, 2017 | 155 | 2017 |
| Presto: distributed machine learning and graph processing with sparse matrices S Venkataraman, E Bodzsar, I Roy, A AuYoung, RS Schreiber Proceedings of the 8th ACM European Conference on Computer Systems, 197-210, 2013 | 138 | 2013 |
| Themis: Fair and efficient GPU cluster scheduling K Mahajan, A Balasubramanian, A Singhvi, S Venkataraman, A Akella, ... 17th USENIX Symposium on Networked Systems Design and Implementation, 2020 | 134 | 2020 |
| Cake: enabling high-level SLOs on shared storage systems A Wang, S Venkataraman, S Alspaugh, R Katz, I Stoica Proceedings of the Third ACM Symposium on Cloud Computing, 1-14, 2012 | 129 | 2012 |
| The power of choice in data-aware cluster scheduling S Venkataraman, A Panda, G Ananthanarayanan, MJ Franklin, I Stoica 11th {USENIX} Symposium on Operating Systems Design and Implementation …, 2014 | 127 | 2014 |
| Matrix computations and optimization in apache spark R Bosagh Zadeh, X Meng, A Ulanov, B Yavuz, L Pu, S Venkataraman, ... Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 125 | 2016 |
| The Case for Tiny Tasks in Compute Clusters. K Ousterhout, A Panda, J Rosen, S Venkataraman, R Xin, S Ratnasamy, ... HotOS 13, 14-14, 2013 | 117 | 2013 |
| Sparkr: Scaling r programs with spark S Venkataraman, Z Yang, D Liu, E Liang, H Falaki, X Meng, R Xin, ... Proceedings of the 2016 international conference on management of data, 1099 …, 2016 | 92 | 2016 |
| Blink: Fast and generic collectives for distributed ml G Wang, S Venkataraman, A Phanishayee, N Devanur, J Thelin, I Stoica Proceedings of Machine Learning and Systems 2, 172-186, 2020 | 85 | 2020 |
