Shivaram Venkataraman

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 79

Top Venues

University of California, Berkeley, USA (PHD)

Education History

University of California, Berkeley, USA

PHD

Publications

Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman
Does Compressing Activations Help Model Parallel Training? MLSys (2024)
Dong Liu, Roger Waleffe, Meng Jiang, Shivaram Venkataraman
GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval. CoRR (2024)
Konstantinos Kanellis, Johannes Freischuetz, Shivaram Venkataraman
Nautilus: A Benchmarking Platform for DBMS Knob Tuning. DEEM@SIGMOD (2024)
Minghao Yan, Saurabh Agarwal, Shivaram Venkataraman
Decoding Speculative Decoding. CoRR (2024)
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 (2024)
Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman
Blox: A Modular Toolkit for Deep Learning Schedulers. EuroSys (2024)
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. EuroSys (2023)
Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang
Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning. SC (2023)
Konstantinos Kanellis, Badrish Chandramouli, Shivaram Venkataraman
F2: Designing a Key-Value Store for Large Skewed Workloads. CoRR (2023)
Saurabh Agarwal, Chengpo Yan, Ziyi Zhang, Shivaram Venkataraman
Bagpipe: Accelerating Deep Recommendation Model Training. SOSP (2023)
Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman
Blox: A Modular Toolkit for Deep Learning Schedulers. CoRR (2023)
Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang
Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning. CoRR (2023)
Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman
Does compressing activations help model parallel training? CoRR (2023)
Minghao Yan, Hongyi Wang, Shivaram Venkataraman
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices. CoRR (2023)
Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella
Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning. NSDI (2023)
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)
Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella
Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning. CoRR (2022)
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 (2022)
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. CoRR (2022)
Saurabh Agarwal, Ziyi Zhang, Shivaram Venkataraman
BagPipe: Accelerating Deep Recommendation Model Training. CoRR (2022)
Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman
LlamaTune: Sample-Efficient DBMS Configuration Tuning. Proc. VLDB Endow. 15 (11) (2022)
Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos
On the Utility of Gradient Compression in Distributed Training Systems. MLSys (2022)
Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman
LlamaTune: Sample-Efficient DBMS Configuration Tuning. CoRR (2022)
Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella
Doing more by doing less: how structured partial backpropagation improves deep learning clusters. DistributedML@CoNEXT (2021)
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 (2021)
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos
Adaptive Gradient Communication via Critical Learning Regime Identification. MLSys (2021)
Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos
On the Utility of Gradient Compression in Distributed Training Systems. CoRR (2021)
Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
Learning Massive Graph Embeddings on a Single Machine. CoRR (2021)
Arjun Singhvi, Arjun Balasubramanian, Kevin Houck, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella
Atoll: A Scalable Low-Latency Serverless Platform. SoCC (2021)
Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella
Accelerating Deep Learning Inference via Learned Caches. CoRR (2021)
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) (2021)
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)
Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella
Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters. CoRR (2021)
Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman
Marius: Learning Massive Graph Embeddings on a Single Machine. OSDI (2021)
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) (2021)
Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman
AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning. CoRR (2021)
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)
Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. CoRR (2020)
Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla
Themis: Fair and Efficient GPU Cluster Scheduling. NSDI (2020)
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification. CoRR (2020)
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica
Blink: Fast and Generic Collectives for Distributed ML. MLSys (2020)
Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella
Accelerating Deep Learning Inference via Freezing. CoRR (2020)
Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Benjamin Recht, Ion Stoica, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman
Serverless linear algebra. SoCC (2020)
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Learning-Based Coded Computation. IEEE J. Sel. Areas Inf. Theory 1 (1) (2020)
Konstantinos Kanellis, Ramnatthan Alagappan, Shivaram Venkataraman
Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs. HotStorage (2020)
Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman
The Case for Unifying Data Loading in Machine Learning Clusters. HotCloud (2019)
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Parity Models: A General Framework for Coding-Based Resilience in ML Inference. CoRR (2019)
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)
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 (2019)
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 Annual Technical Conference (2019)
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 (2019)
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica
Blink: Fast and Generic Collectives for Distributed ML. CoRR (2019)
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Parity models: erasure-coded resilience for prediction serving systems. SOSP (2019)
Philip A. Bernstein, Todd Porter, Rahul Potharaju, Alejandro Z. Tomsic, Shivaram Venkataraman, Wentao Wu
Serverless Event-Stream Processing over Virtual Actors. CIDR (2019)
Qifan Pu, Shivaram Venkataraman, Ion Stoica
Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure. NSDI (2019)
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 (2019)
Arjun Singhvi, Kevin Houck, Arjun Balasubramanian, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella
Archipelago: A Scalable Low-Latency Serverless Platform. CoRR (2019)
Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella
Accelerating Deep Learning Inference via Freezing. HotCloud (2019)
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. CoRR (2018)
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) (2018)
Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
ASAP: Fast, Approximate Graph Pattern Mining at Scale. OSDI (2018)
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)
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)
Vaishaal Shankar, Karl Krauth, Qifan Pu, Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley
numpywren: serverless linear algebra. CoRR (2018)
Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
Towards Fast and Scalable Graph Pattern Mining. HotCloud (2018)
Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht
Occupy the Cloud: Distributed Computing for the 99%. CoRR (2017)
Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. ICDE (2017)
Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez
Hemingway: Modeling Distributed Optimization Algorithms. CoRR (2017)
Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht
Occupy the cloud: distributed computing for the 99%. SoCC (2017)
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)
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)
Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht
Breaking Locality Accelerates Block Gauss-Seidel. ICML (2017)
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) (2016)
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 (2016)
Shivaram Venkataraman, Zongheng Yang, Michael J. Franklin, Benjamin Recht, Ion Stoica
Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. NSDI (2016)
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht
Large Scale Kernel Learning using Block Coordinate Descent. CoRR (2016)
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)
Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. CoRR (2016)
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)
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 (2015)
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 (2015)
Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
Quantifying eventual consistency with PBS. Commun. ACM 57 (8) (2014)
Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael J. Franklin, Ion Stoica
The Power of Choice in Data-Aware Cluster Scheduling. OSDI (2014)
Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
Quantifying eventual consistency with PBS. VLDB J. 23 (2) (2014)
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)
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)
Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber
Presto: distributed machine learning and graph processing with sparse matrices. EuroSys (2013)
Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
PBS at work: advancing data management with consistency metrics. SIGMOD Conference (2013)
Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
Probabilistically Bounded Staleness for Practical Partial Quorums CoRR (2012)
Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
Probabilistically Bounded Staleness for Practical Partial Quorums. Proc. VLDB Endow. 5 (8) (2012)
Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber
Using R for Iterative and Incremental Processing. HotCloud (2012)
Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Randy H. Katz, Ion Stoica
Cake: enabling high-level SLOs on shared storage systems. SoCC (2012)
Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, Randy H. Katz
Sweet Storage SLOs with Frosting. HotCloud (2012)
Ellick Chan, Shivaram Venkataraman, Nadia Tkach, Kevin Larson, Alejandro Gutierrez, Roy H. Campbell
Characterizing Data Structures for Volatile Forensics. SADFE (2011)
Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy H. Campbell
Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory. FAST (2011)
Ellick Chan, Shivaram Venkataraman, Francis M. David, Amey Chaugule, Roy H. Campbell
Forenscope: a framework for live forensics. ACSAC (2010)
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)

Shivaram Venkataraman.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Shivaram Venkataraman