Shivaram Venkataraman

According to our database, Shivaram Venkataraman authored at least 83 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Incremental IVF Index Maintenance for Streaming Vector Search.

Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Umar Farooq Minhas, Jeffrey Pound, Cédric Renggli, Nima Reyhani, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman

CoRR, 2024

PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters.

Rutwik Jain, Brandon Tran, Keting Chen, Matthew D. Sinclair, Shivaram Venkataraman

CoRR, 2024

GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval.

Dong Liu, Roger Waleffe, Meng Jiang, Shivaram Venkataraman

CoRR, 2024

Decoding Speculative Decoding.

Minghao Yan, Saurabh Agarwal, Shivaram Venkataraman

CoRR, 2024

Does Compressing Activations Help Model Parallel Training?

Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

CHAI: Clustered Head Attention for Efficient LLM Inference.

Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Blox: A Modular Toolkit for Deep Learning Schedulers.

Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman

Proceedings of the Nineteenth European Conference on Computer Systems, 2024

Nautilus: A Benchmarking Platform for DBMS Knob Tuning.

Konstantinos Kanellis, Johannes Freischuetz, Shivaram Venkataraman

Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning, 2024

2023

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices.

Minghao Yan, Hongyi Wang, Shivaram Venkataraman

CoRR, 2023

F2: Designing a Key-Value Store for Large Skewed Workloads.

Konstantinos Kanellis, Badrish Chandramouli, Shivaram Venkataraman

CoRR, 2023

Bagpipe: Accelerating Deep Recommendation Model Training.

Saurabh Agarwal, Chengpo Yan, Ziyi Zhang, Shivaram Venkataraman

Proceedings of the 29th Symposium on Operating Systems Principles, 2023

Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning.

Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang

Proceedings of the International Conference for High Performance Computing, 2023

Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning.

Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella

Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, 2023

MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks.

Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman

Proceedings of the Eighteenth European Conference on Computer Systems, 2023

2022

LlamaTune: Sample-Efficient DBMS Configuration Tuning.

Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman

Proc. VLDB Endow., 2022

BagPipe: Accelerating Deep Recommendation Model Training.

Saurabh Agarwal, Ziyi Zhang, Shivaram Venkataraman

CoRR, 2022

Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine.

Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman

CoRR, 2022

Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems.

Prasoon Sinha, Akhil Guliani, Rutwik Jain, Brandon Tran, Matthew D. Sinclair, Shivaram Venkataraman

Proceedings of the SC22: International Conference for High Performance Computing, 2022

On the Utility of Gradient Compression in Distributed Training Systems.

Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

2021

The Roaming Edge and its Applications.

Suman Banerjee, Remzi H. Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, Shivaram Venkataraman

GetMobile Mob. Comput. Commun., 2021

Demonstration of Marius: Graph Embeddings with a Single Machine.

Anders Carlsson, Anze Xie, Jason Mohoney, Roger Waleffe, Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman

Proc. VLDB Endow., 2021

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning.

Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman

CoRR, 2021

Learning Massive Graph Embeddings on a Single Machine.

Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

CoRR, 2021

Accelerating Deep Learning Inference via Learned Caches.

Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella

CoRR, 2021

KAISA: an adaptive second-order optimizer framework for deep neural networks.

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian T. Foster, Zhao Zhang

Proceedings of the International Conference for High Performance Computing, 2021

Marius: Learning Massive Graph Embeddings on a Single Machine.

Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman

Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation, 2021

Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo.

Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju

Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, 2021

Adaptive Gradient Communication via Critical Learning Regime Identification.

Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

Doing more by doing less: how structured partial backpropagation improves deep learning clusters.

Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella

Proceedings of the DistributedML '21: Proceedings of the 2nd ACM International Workshop on Distributed Machine Learning, 2021

Atoll: A Scalable Low-Latency Serverless Platform.

Arjun Singhvi, Arjun Balasubramanian, Kevin Houck, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella

Proceedings of the SoCC '21: ACM Symposium on Cloud Computing, 2021

2020

Learning-Based Coded Computation.

Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman

IEEE J. Sel. Areas Inf. Theory, 2020

Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.

Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos

CoRR, 2020

Themis: Fair and Efficient GPU Cluster Scheduling.

Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla

Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, 2020

Blink: Fast and Generic Collectives for Distributed ML.

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica

Proceedings of the Third Conference on Machine Learning and Systems, 2020

Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs.

Konstantinos Kanellis, Ramnatthan Alagappan, Shivaram Venkataraman

Proceedings of the 12th USENIX Workshop on Hot Topics in Storage and File Systems, 2020

Serverless linear algebra.

Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Benjamin Recht, Ion Stoica, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman

Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

2019

Archipelago: A Scalable Low-Latency Serverless Platform.

Arjun Singhvi, Kevin Houck, Arjun Balasubramanian, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella

CoRR, 2019

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads.

Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla

CoRR, 2019

Parity Models: A General Framework for Coding-Based Resilience in ML Inference.

Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

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

CoRR, 2019

Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads.

Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang

Proceedings of the 2019 USENIX Annual Technical Conference, 2019

Parity models: erasure-coded resilience for prediction serving systems.

Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman

Proceedings of the 27th ACM Symposium on Operating Systems Principles, 2019

Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure.

Qifan Pu, Shivaram Venkataraman, Ion Stoica

Proceedings of the 16th USENIX Symposium on Networked Systems Design and Implementation, 2019

Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary.

John Emmons, Sadjad Fouladi, Ganesh Ananthanarayanan, Shivaram Venkataraman, Silvio Savarese, Keith Winstein

Proceedings of the 2019 Workshop on Hot Topics in Video Analytics and Intelligent Edges, 2019

Accelerating Deep Learning Inference via Freezing.

Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella

Proceedings of the 11th USENIX Workshop on Hot Topics in Cloud Computing, 2019

The Case for Unifying Data Loading in Machine Learning Clusters.

Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman

Proceedings of the 11th USENIX Workshop on Hot Topics in Cloud Computing, 2019

Serverless Event-Stream Processing over Virtual Actors.

Philip A. Bernstein, Todd Porter, Rahul Potharaju, Alejandro Z. Tomsic, Shivaram Venkataraman, Wentao Wu

Proceedings of the 9th Biennial Conference on Innovative Data Systems Research, 2019

2018

Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems.

Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanam Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao

Proc. VLDB Endow., 2018

numpywren: serverless linear algebra.

Vaishaal Shankar, Karl Krauth, Qifan Pu, Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley

CoRR, 2018

Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation.

Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman

CoRR, 2018

ASAP: Fast, Approximate Graph Pattern Mining at Scale.

Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

Focus: Querying Large Video Datasets with Low Latency and Low Cost.

Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodík, Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, Onur Mutlu

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

Towards Fast and Scalable Graph Pattern Mining.

Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica

Proceedings of the 10th USENIX Workshop on Hot Topics in Cloud Computing, 2018

Bridging the GAP: towards approximate graph analytics.

Anand Padmanabha Iyer, Aurojit Panda, Shivaram Venkataraman, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica

Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 2018

2017

System Design for Large Scale Machine Learning.

Shivaram Venkataraman

PhD thesis, 2017

Hemingway: Modeling Distributed Optimization Algorithms.

Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez

CoRR, 2017

Occupy the Cloud: Distributed Computing for the 99%.

Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht

CoRR, 2017

Drizzle: Fast and Adaptable Stream Processing at Scale.

Shivaram Venkataraman, Aurojit Panda, Kay Ousterhout, Michael Armbrust, Ali Ghodsi, Michael J. Franklin, Benjamin Recht, Ion Stoica

Proceedings of the 26th Symposium on Operating Systems Principles, 2017

CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics.

Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, Ming Zhang

Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, 2017

Breaking Locality Accelerates Block Gauss-Seidel.

Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht

Proceedings of the 34th International Conference on Machine Learning, 2017

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics.

Evan Randall Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht

Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

Occupy the cloud: distributed computing for the 99%.

Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht

Proceedings of the 2017 Symposium on Cloud Computing, SoCC 2017, Santa Clara, CA, USA, 2017

2016

MLlib: Machine Learning in Apache Spark.

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

J. Mach. Learn. Res., 2016

Large Scale Kernel Learning using Block Coordinate Descent.

Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht

CoRR, 2016

Apache Spark: a unified engine for big data processing.

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

Commun. ACM, 2016

SparkR: Scaling R Programs with Spark.

Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael J. Franklin, Ion Stoica, Matei Zaharia

Proceedings of the 2016 International Conference on Management of Data, 2016

Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics.

Shivaram Venkataraman, Zongheng Yang, Michael J. Franklin, Benjamin Recht, Ion Stoica

Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation, 2016

Matrix Computations and Optimization in Apache Spark.

Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Aaron Staple, Matei Zaharia

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015

linalg: Matrix Computations in Apache Spark.

Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Alexander Ulanov, Matei Zaharia

CoRR, 2015

2014

Quantifying eventual consistency with PBS.

Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica

Commun. ACM, 2014

Record Placement Based on Data Skew Using Solid State Drives.

Jun Suzuki, Shivaram Venkataraman, Sameer Agarwal, Michael J. Franklin, Ion Stoica

Proceedings of the Big Data Benchmarks, Performance Optimization, and Emerging Hardware, 2014

The Power of Choice in Data-Aware Cluster Scheduling.

Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael J. Franklin, Ion Stoica

Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation, 2014

2013

PBS at work: advancing data management with consistency metrics.

Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2013

The Case for Tiny Tasks in Compute Clusters.

Kay Ousterhout, Aurojit Panda, Josh Rosen, Shivaram Venkataraman, Reynold Xin, Sylvia Ratnasamy, Scott Shenker, Ion Stoica

Proceedings of the 14th Workshop on Hot Topics in Operating Systems, 2013

Presto: distributed machine learning and graph processing with sparse matrices.

Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber

Proceedings of the Eighth Eurosys Conference 2013, 2013

2012

Probabilistically Bounded Staleness for Practical Partial Quorums.

Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica

Proc. VLDB Endow., 2012

Sweet Storage SLOs with Frosting.

Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, Randy H. Katz

Proceedings of the 4th USENIX Workshop on Hot Topics in Cloud Computing, 2012

Using R for Iterative and Incremental Processing.

Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber

Proceedings of the 4th USENIX Workshop on Hot Topics in Cloud Computing, 2012

Cake: enabling high-level SLOs on shared storage systems.

Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Randy H. Katz, Ion Stoica

Proceedings of the ACM Symposium on Cloud Computing, SOCC '12, 2012

2011

Characterizing Data Structures for Volatile Forensics.

Ellick Chan, Shivaram Venkataraman, Nadia Tkach, Kevin Larson, Alejandro Gutierrez, Roy H. Campbell

Proceedings of the 2011 IEEE Sixth International Workshop on Systematic Approaches to Digital Forensic Engineering, 2011

Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory.

Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy H. Campbell

Proceedings of the 9th USENIX Conference on File and Storage Technologies, 2011

2010

Scaling eCGA model building via data-intensive computing.

Abhishek Verma, Xavier Llorà, Shivaram Venkataraman, David E. Goldberg, Roy H. Campbell

Proceedings of the IEEE Congress on Evolutionary Computation, 2010

Forenscope: a framework for live forensics.

Ellick Chan, Shivaram Venkataraman, Francis M. David, Amey Chaugule, Roy H. Campbell

Proceedings of the Twenty-Sixth Annual Computer Security Applications Conference, 2010