Shivaram Venkataraman
Shivaram Venkataraman
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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
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
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
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
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
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
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
Probabilistically Bounded Staleness for Practical Partial Quorums
P Bailis, S Venkataraman, JM Hellerstein, M Franklin, I Stoica
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
Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure.
Q Pu, S Venkataraman, I Stoica
NSDI 19, 193-206, 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
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
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
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
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
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
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
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
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
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
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