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ORCIDShivaram Venkataraman
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- affiliation: University of Wisconsin-Madison, WI, USA
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2020 – today
- 2024
[c51]Konstantinos Kanellis
, Johannes Freischuetz, Shivaram Venkataraman:
Nautilus: A Benchmarking Platform for DBMS Knob Tuning. DEEM@SIGMOD 2024: 72-76- [c50]Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman:
Blox: A Modular Toolkit for Deep Learning Schedulers. EuroSys 2024: 1093-1109 - [c49]Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu:
CHAI: Clustered Head Attention for Efficient LLM Inference. ICML 2024 - [c48]Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman:
Does Compressing Activations Help Model Parallel Training? MLSys 2024 - [i38]Minghao Yan, Saurabh Agarwal, Shivaram Venkataraman:
Decoding Speculative Decoding. CoRR abs/2402.01528 (2024) - [i37]Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu:
CHAI: Clustered Head Attention for Efficient LLM Inference. CoRR abs/2403.08058 (2024) - [i36]Dong Liu, Roger Waleffe, Meng Jiang, Shivaram Venkataraman:
GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval. CoRR abs/2406.17918 (2024) - [i35]Rutwik Jain, Brandon Tran, Keting Chen, Matthew D. Sinclair, Shivaram Venkataraman:
PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters. CoRR abs/2408.11919 (2024) - 2023
- [c47]Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman:
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. EuroSys 2023: 144-161 - [c46]Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella:
Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning. NSDI 2023: 703-723 - [c45]Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang:
Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning. SC 2023: 25:1-25:13 - [c44]Saurabh Agarwal, Chengpo Yan, Ziyi Zhang, Shivaram Venkataraman:
Bagpipe: Accelerating Deep Recommendation Model Training. SOSP 2023: 348-363 - [i34]Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman:
Does compressing activations help model parallel training? CoRR abs/2301.02654 (2023) - [i33]Konstantinos Kanellis, Badrish Chandramouli, Shivaram Venkataraman:
F2: Designing a Key-Value Store for Large Skewed Workloads. CoRR abs/2305.01516 (2023) - [i32]Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang:
Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning. CoRR abs/2306.14086 (2023) - [i31]Minghao Yan, Hongyi Wang, Shivaram Venkataraman:
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices. CoRR abs/2310.19991 (2023) - [i30]Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman:
Blox: A Modular Toolkit for Deep Learning Schedulers. CoRR abs/2312.12621 (2023) - 2022
- [j10]Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman:
LlamaTune: Sample-Efficient DBMS Configuration Tuning. Proc. VLDB Endow. 15(11): 2953-2965 (2022) - [c43]Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. MLSys 2022 - [c42]Prasoon Sinha, Akhil Guliani, Rutwik Jain, Brandon Tran, Matthew D. Sinclair, Shivaram Venkataraman:
Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems. SC 2022: 65:1-65:15 - [i29]Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman:
Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. CoRR abs/2202.02365 (2022) - [i28]Saurabh Agarwal, Ziyi Zhang, Shivaram Venkataraman:
BagPipe: Accelerating Deep Recommendation Model Training. CoRR abs/2202.12429 (2022) - [i27]Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman:
LlamaTune: Sample-Efficient DBMS Configuration Tuning. CoRR abs/2203.05128 (2022) - [i26]Prasoon Sinha, Akhil Guliani, Rutwik Jain, Brandon Tran, Matthew D. Sinclair, Shivaram Venkataraman:
Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems. CoRR abs/2208.11035 (2022) - [i25]Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella:
Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning. CoRR abs/2210.00093 (2022) - 2021
- [j9]Anders Carlsson, Anze Xie, Jason Mohoney, Roger Waleffe, Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman:
Demonstration of Marius: Graph Embeddings with a Single Machine. Proc. VLDB Endow. 14(12): 2759-2762 (2021) - [j8]Suman Banerjee, Remzi H. Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, Shivaram Venkataraman:
The Roaming Edge and its Applications. GetMobile Mob. Comput. Commun. 25(4): 5-11 (2021) - [c41]Arjun Singhvi, Arjun Balasubramanian, Kevin Houck, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella:
Atoll: A Scalable Low-Latency Serverless Platform. SoCC 2021: 138-152 - [c40]Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella:
Doing more by doing less: how structured partial backpropagation improves deep learning clusters. DistributedML@CoNEXT 2021: 15-21 - [c39]Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
Adaptive Gradient Communication via Critical Learning Regime Identification. MLSys 2021 - [c38]Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju:
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. NSDI 2021: 389-405 - [c37]Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman:
Marius: Learning Massive Graph Embeddings on a Single Machine. OSDI 2021: 533-549 - [c36]J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian T. Foster, Zhao Zhang:
KAISA: an adaptive second-order optimizer framework for deep neural networks. SC 2021: 13 - [i24]Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella:
Accelerating Deep Learning Inference via Learned Caches. CoRR abs/2101.07344 (2021) - [i23]Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman:
Learning Massive Graph Embeddings on a Single Machine. CoRR abs/2101.08358 (2021) - [i22]Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman:
AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning. CoRR abs/2102.01386 (2021) - [i21]Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. CoRR abs/2103.00543 (2021) - [i20]J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian T. Foster, Zhao Zhang:
KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks. CoRR abs/2107.01739 (2021) - [i19]Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella:
Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters. CoRR abs/2111.10672 (2021) - 2020
- [j7]Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman:
Learning-Based Coded Computation. IEEE J. Sel. Areas Inf. Theory 1(1): 227-236 (2020) - [c35]Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Benjamin Recht, Ion Stoica, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman:
Serverless linear algebra. SoCC 2020: 281-295 - [c34]Konstantinos Kanellis, Ramnatthan Alagappan, Shivaram Venkataraman:
Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs. HotStorage 2020 - [c33]Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica:
Blink: Fast and Generic Collectives for Distributed ML. MLSys 2020 - [c32]Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla:
Themis: Fair and Efficient GPU Cluster Scheduling. NSDI 2020: 289-304 - [i18]Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella:
Accelerating Deep Learning Inference via Freezing. CoRR abs/2002.02645 (2020) - [i17]Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju:
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. CoRR abs/2010.03035 (2020) - [i16]Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification. CoRR abs/2010.16248 (2020)
2010 – 2019
- 2019
- [c31]Philip A. Bernstein, Todd Porter, Rahul Potharaju, Alejandro Z. Tomsic, Shivaram Venkataraman, Wentao Wu:
Serverless Event-Stream Processing over Virtual Actors. CIDR 2019 - [c30]Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman:
The Case for Unifying Data Loading in Machine Learning Clusters. HotCloud 2019 - [c29]Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella:
Accelerating Deep Learning Inference via Freezing. HotCloud 2019 - [c28]John Emmons, Sadjad Fouladi, Ganesh Ananthanarayanan, Shivaram Venkataraman, Silvio Savarese, Keith Winstein:
Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary. HotEdgeVideo@MobiCom 2019: 27-32 - [c27]Qifan Pu, Shivaram Venkataraman, Ion Stoica:
Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure. NSDI 2019: 193-206 - [c26]Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman:
Parity models: erasure-coded resilience for prediction serving systems. SOSP 2019: 30-46 - [c25]Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang:
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. USENIX ATC 2019: 947-960 - [i15]Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang:
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. CoRR abs/1901.05758 (2019) - [i14]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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 Randall 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) - [i13]Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman:
Parity Models: A General Framework for Coding-Based Resilience in ML Inference. CoRR abs/1905.00863 (2019) - [i12]Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla:
Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads. CoRR abs/1907.01484 (2019) - [i11]Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica:
Blink: Fast and Generic Collectives for Distributed ML. CoRR abs/1910.04940 (2019) - [i10]Arjun Singhvi, Kevin Houck, Arjun Balasubramanian, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella:
Archipelago: A Scalable Low-Latency Serverless Platform. CoRR abs/1911.09849 (2019) - 2018
- [j6]Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanam Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao:
Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. Proc. VLDB Endow. 11(10): 1303-1316 (2018) - [c24]Anand Padmanabha Iyer, Aurojit Panda, Shivaram Venkataraman, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica:
Bridging the GAP: towards approximate graph analytics. GRADES/NDA@SIGMOD/PODS 2018: 10:1-10:5 - [c23]Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica:
Towards Fast and Scalable Graph Pattern Mining. HotCloud 2018 - [c22]Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodík, Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, Onur Mutlu:
Focus: Querying Large Video Datasets with Low Latency and Low Cost. OSDI 2018: 269-286 - [c21]Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica:
ASAP: Fast, Approximate Graph Pattern Mining at Scale. OSDI 2018: 745-761 - [i9]Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman:
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. CoRR abs/1806.01259 (2018) - [i8]Vaishaal Shankar, Karl Krauth, Qifan Pu, Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley:
numpywren: serverless linear algebra. CoRR abs/1810.09679 (2018) - 2017
- [b1]Shivaram Venkataraman:
System Design for Large Scale Machine Learning. University of California, Berkeley, USA, 2017 - [c20]Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht:
Occupy the cloud: distributed computing for the 99%. SoCC 2017: 445-451 - [c19]Evan Randall Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht:
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. ICDE 2017: 535-546 - [c18]Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht:
Breaking Locality Accelerates Block Gauss-Seidel. ICML 2017: 3482-3491 - [c17]Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, Ming Zhang:
CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. NSDI 2017: 469-482 - [c16]Shivaram Venkataraman, Aurojit Panda, Kay Ousterhout, Michael Armbrust, Ali Ghodsi, Michael J. Franklin, Benjamin Recht, Ion Stoica:
Drizzle: Fast and Adaptable Stream Processing at Scale. SOSP 2017: 374-389 - [i7]Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht:
Occupy the Cloud: Distributed Computing for the 99%. CoRR abs/1702.04024 (2017) - [i6]Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez:
Hemingway: Modeling Distributed Optimization Algorithms. CoRR abs/1702.05865 (2017) - 2016
- [j5]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) - [j4]Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Randall 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) - [c15]Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Aaron Staple, Matei Zaharia:
Matrix Computations and Optimization in Apache Spark. KDD 2016: 31-38 - [c14]Shivaram Venkataraman, Zongheng Yang, Michael J. Franklin, Benjamin Recht, Ion Stoica:
Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. NSDI 2016: 363-378 - [c13]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 - [i5]Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht:
Large Scale Kernel Learning using Block Coordinate Descent. CoRR abs/1602.05310 (2016) - [i4]Evan Randall Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht:
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. CoRR abs/1610.09451 (2016) - 2015
- [i3]Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Randall 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) - [i2]Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Alexander Ulanov, Matei Zaharia:
linalg: Matrix Computations in Apache Spark. CoRR abs/1509.02256 (2015) - 2014
- [j3]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Quantifying eventual consistency with PBS. Commun. ACM 57(8): 93-102 (2014) - [j2]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Quantifying eventual consistency with PBS. VLDB J. 23(2): 279-302 (2014) - [c12]Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael J. Franklin, Ion Stoica:
The Power of Choice in Data-Aware Cluster Scheduling. OSDI 2014: 301-316 - [c11]Jun Suzuki, Shivaram Venkataraman, Sameer Agarwal, Michael J. Franklin, Ion Stoica:
Record Placement Based on Data Skew Using Solid State Drives. BPOE@ASPLOS/VLDB 2014: 181-193 - 2013
- [c10]Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber:
Presto: distributed machine learning and graph processing with sparse matrices. EuroSys 2013: 197-210 - [c9]Kay Ousterhout, Aurojit Panda, Josh Rosen, Shivaram Venkataraman, Reynold Xin, Sylvia Ratnasamy, Scott Shenker, Ion Stoica:
The Case for Tiny Tasks in Compute Clusters. HotOS 2013 - [c8]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
PBS at work: advancing data management with consistency metrics. SIGMOD Conference 2013: 1113-1116 - 2012
- [j1]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Probabilistically Bounded Staleness for Practical Partial Quorums. Proc. VLDB Endow. 5(8): 776-787 (2012) - [c7]Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Randy H. Katz, Ion Stoica:
Cake: enabling high-level SLOs on shared storage systems. SoCC 2012: 14 - [c6]Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber:
Using R for Iterative and Incremental Processing. HotCloud 2012 - [c5]Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, Randy H. Katz:
Sweet Storage SLOs with Frosting. HotCloud 2012 - [i1]Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica:
Probabilistically Bounded Staleness for Practical Partial Quorums. CoRR abs/1204.6082 (2012) - 2011
- [c4]Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy H. Campbell:
Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory. FAST 2011: 61-75 - [c3]Ellick Chan, Shivaram Venkataraman, Nadia Tkach, Kevin Larson, Alejandro Gutierrez, Roy H. Campbell:
Characterizing Data Structures for Volatile Forensics. SADFE 2011: 1-9 - 2010
- [c2]Ellick Chan, Shivaram Venkataraman, Francis M. David, Amey Chaugule, Roy H. Campbell:
Forenscope: a framework for live forensics. ACSAC 2010: 307-316 - [c1]Abhishek Verma, Xavier Llorà, Shivaram Venkataraman, David E. Goldberg, Roy H. Campbell:
Scaling eCGA model building via data-intensive computing. IEEE Congress on Evolutionary Computation 2010: 1-8
Coauthor Index
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