| Quickr: Lazily approximating complex adhoc queries in bigdata clusters S Kandula, A Shanbhag, A Vitorovic, M Olma, R Grandl, S Chaudhuri, ... Proceedings of the 2016 international conference on management of data, 631-646, 2016 | 162 | 2016 |
| Weld: A common runtime for high performance data analytics S Palkar, JJ Thomas, A Shanbhag, D Narayanan, H Pirk, M Schwarzkopf, ... | 161 | 2017 |
| Evaluating end-to-end optimization for data analytics applications in weld S Palkar, J Thomas, D Narayanan, P Thaker, R Palamuttam, P Negi, ... Proceedings of the VLDB Endowment 11 (9), 1002-1015, 2018 | 83 | 2018 |
| AdaptDB: adaptive partitioning for distributed joins Y Lu Massachusetts Institute of Technology, 2017 | 66 | 2017 |
| A study of the fundamental performance characteristics of GPUs and CPUs for database analytics A Shanbhag, S Madden, X Yu Proceedings of the 2020 ACM SIGMOD international conference on Management of …, 2020 | 60 | 2020 |
| Efficient top-k query processing on massively parallel hardware A Shanbhag, H Pirk, S Madden Proceedings of the 2018 International Conference on Management of Data, 1557 …, 2018 | 53 | 2018 |
| A robust partitioning scheme for ad-hoc query workloads A Shanbhag, A Jindal, S Madden, J Quiane, AJ Elmore Proceedings of the 2017 Symposium on Cloud Computing, 229-241, 2017 | 39 | 2017 |
| Large-scale in-memory analytics on Intel® Optane™ DC persistent memory A Shanbhag, N Tatbul, D Cohen, S Madden Proceedings of the 16th International Workshop on Data Management on New …, 2020 | 36 | 2020 |
| Controlling approximations of queries S Kandula, S Chaudhuri, B Ding, AA Shanbhag, A Vitorović, M Olma, ... US Patent 10,496,643, 2019 | 27 | 2019 |
| Weld: Rethinking the interface between data-intensive applications S Palkar, J Thomas, D Narayanan, A Shanbhag, R Palamuttam, H Pirk, ... arXiv preprint arXiv:1709.06416, 2017 | 23 | 2017 |
| Amoeba: a shape changing storage system for big data A Shanbhag, A Jindal, Y Lu, S Madden Proceedings of the VLDB Endowment 9 (13), 1569-1572, 2016 | 19 | 2016 |
| Optimizing join enumeration in transformation-based query optimizers A Shanbhag, S Sudarshan Proceedings of the VLDB Endowment 7 (12), 1243-1254, 2014 | 11 | 2014 |
| Tile-based lightweight integer compression in GPU A Shanbhag, BW Yogatama, X Yu, S Madden Proceedings of the 2022 International Conference on Management of Data, 1390 …, 2022 | 9 | 2022 |
| Weld: Rethinking the Interface Between Data-Intensive Applications. CoRR abs/1709.06416 (2017) S Palkar, JJ Thomas, D Narayanan, A Shanbhag, R Palamuttam, H Pirk, ... arXiv preprint arXiv:1709.06416, 2017 | 6 | 2017 |
| Locality-adaptive parallel hash joins using hardware transactional memory A Shanbhag, H Pirk, S Madden Data Management on New Hardware: 7th International Workshop on Accelerating …, 2017 | 2 | 2017 |
| An adaptive partitioning scheme for ad-hoc and time-varying database analytics AAA Shanbhag Massachusetts Institute of Technology, 2016 | 1 | 2016 |
| Interactive data analytics using GPUs AAA Shanbhag Massachusetts Institute of Technology, 2020 | | 2020 |
| Algorithms and Data Structures in the noisy comparison model and applications N Dikkala, A Shanbhag, E Zampetakis | | |
Samuel MaddenMITGeverifieerd e-mailadres voor csail.mit.edu
