Shivaram Venkataraman

Assistant Professor, Computer Science, University of Wisconsin-Madison

Office: 7367 CS. Email: shivaram at cs.wisc.edu

Publications

  1. Tzu-Tao Chang, Shivaram Venkataraman Eva: Cost-Efficient Cloud-Based Cluster Scheduling - Eurosys 2025

  2. Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman TUNA: Tuning Unstable and Noisy Cloud Applications - Eurosys 2025

  3. Minghao Yan, Saurabh Agarwal, Shivaram Venkataraman Decoding Speculative Decoding - NAACL 2025

  4. Meguru Yamazaki, Shivaram Venkataraman CO2: Precise Attention Score Observation for improving KV Cache Replacement in Large Language Model - Efficient Systems for Foundation Models (ES-FoMO) Workshop at the International Conference on Machine Learning (ICML) 2024

  5. Rutwik Jain, Brandon Tran, Keting Chen, Matthew Sinclair, Shivaram Venkataraman PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters - International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing 2024)

  6. Konstantinos Kanellis, Johannes Freischuetz, Shivaram Venkataraman Nautilus: A Benchmarking Platform for DBMS Knob Tuning - DEEM Workshop 2024

  7. Saurabh Agarwal, Bilge Acun, Basil Homer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu CHAI: Clustered Head Attention for Efficient LLM Inference - ICML 2024

  8. Song Bian, Dacheng Li, Hongyi Wang, Eric Xing, Shivaram Venkataraman Does compressing activations help model parallel training? - MLSys 2024

  9. Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman Blox: A Modular Toolkit for Deep Learning Schedulers - Eurosys 2024

  10. Saurabh Agarwal, Chengpo Yan, Ziyi Zhang, Shivaram Venkataraman BagPipe: Accelerating Deep Recommendation Model Training - SOSP 2023

  11. Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang Mirage: Towards Low-interruption Services on Batch GPU clusters with Reinforcement Learning - International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing 2023)

  12. Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks - Eurosys 2023

  13. Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning - NSDI 2023

  14. Harsh Darshan Sapra, Olesia Elfimova, Sahana Upadhya, Lukas Desorcy, Michael Wagner, Shivaram Venkataraman, Chol-Bum Kweon, Sage Kokjohn, Justin Shumaker Estimating Battery State-of-Charge within 1% using Machine Learning and Physics-based Models - SAE World Congress 2023

  15. Prasoon Sinha, Akhil Guliani, Rutwik Jain, Matthew Sinclair, Shivaram Venkataraman Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems - International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing 2022)

  16. Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman LlamaTune: Sample-Efficient DBMS Configuration Tuning - VLDB 2022

  17. Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris Papailiopoulos On the Utility of Gradient Compression in Distributed Training Systems - MLSys 2022

  18. Anze Xie, Anders Carlsson, Jason Mohoney, Roger Waleffe , Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman Demonstration of Marius: Graph Embeddings with a Single Machine - VLDB 2021

  19. Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella Doing more by doing less: how structured partial backpropagation improves deep learning clusters - DistributedML Workshop at CoNEXT 2021

  20. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks - International Conference for High Performance Computing, Networking, Storage and Analysis (SC21)

  21. Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman Marius: Learning Massive Graph Embeddings on a Single Machine - OSDI 2021

  22. Arjun Singhvi, Arjun Balasubramanian, Kevin Houck, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella Atoll: A Scalable Low-Latency Serverless Platform - SoCC 2021

  23. Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris Papailiopoulos Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification - MLSys 2021

  24. Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai and Rahul Potharaju Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo - NSDI 2021

  25. Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning - arXiv preprint code

  26. Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella Accelerating Deep Learning Inference via Learned Caches - arXiv preprint

  27. Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman Serverless Linear Algebra - SoCC 2020

  28. Konstantinos Kanellis, Ramnatthan Alagappan, Shivaram Venkataraman. Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs - HotStorage 2020

  29. Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, and Aditya Akella, Amar Phanishayee, Shuchi Chawla. Themis: Fair and Efficient GPU Cluster Scheduling - NSDI 2020

  30. Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Nikhil Devanur, Jorgen Thelin, Ion Stoica Blink: Fast and Generic Collectives for Distributed ML - MLSys 2020

  31. Jack Kosaian, K.V. Rashmi, Shivaram Venkataraman Parity Models: Erasure-Coded Resilience for Prediction Serving Systems - SOSP 2019

  32. 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

  33. 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 - Hot Topics in Video Analytics and Intelligent Edges (HotEdgeVideo 2019)

  34. Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, and Aditya Akella Accelerating Deep Learning Inference via Freezing - HotCloud 2019

  35. Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman The Case for Unifying Data Loading in Machine Learning Clusters - HotCloud 2019

  36. Qifan Pu, Shivaram Venkataraman, Ion Stoica Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure - NSDI 2019

  37. Jack Kosaian, K.V. Rashmi, Shivaram Venkataraman Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation - arxiv preprint

  38. Anand Padmanabha Iyer, Zaoxing Liu and Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica ASAP: Fast, Approximate Pattern Mining at Scale - OSDI 2018

  39. Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Shivaram Venkataraman, Paramvir Bahl, and Matthai Philipose, Phillip B. Gibbons, Onur Mutlu Focus: Querying Large Video Datasets with Low Latency and Low Cost - OSDI 2018

  40. 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 - VLDB 2018

  41. Anand Iyer, Aurojit Panda, Shivaram Venkatraman, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica Bridging the GAP: Towards Approximate Graph Analytics - GRADES-NDA 2018.

  42. Anand Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica Towards Fast and Scalable Graph Pattern Mining - HotCloud 2018

  43. Shivaram Venkataraman System Design for Large Scale Machine Learning - PhD Dissertation

  44. 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

  45. Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht Occupy the Cloud: Distributed Computing for the 99% - SoCC 2017 - arxiv version

  46. Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht Breaking Locality Accelerates Block Gauss-Seidel - ICML 2017 arxiv version

  47. Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics - ICDE 2017 arxiv version

  48. Omid Alipourfard, Jianshu Chen, Hongqiang Liu, Shivaram Venkataraman, Minlan Yu, Ming Zhang Cherry Pick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics - NSDI 2017

  49. Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez Hemingway: Modeling Distributed Optimization Algorithms - Learning Systems Workshop, NIPS 2016

  50. 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 - CACM Contributed Article, Nov 2016

  51. Shivaram Venkataraman, Zongheng Yang, Michael J Franklin, Ben Recht, Ion Stoica Ernest: Efficient Performance Prediction for Large Scale Advanced Analytics - NSDI 2016

  52. Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael Franklin, Ion Stoica, Matei Zaharia SparkR: Scaling R Programs with Spark - SIGMOD 2016

  53. Reza Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia Matrix Computations and Optimization in Apache Spark - KDD 2016. Best Paper runner-up, Applied Data Science Track.

  54. Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Ben Recht Large Scale Kernel Learning using Block Coordinate Descent - arxiv preprint

  55. Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar MLlib: Machine Learning in Apache Spark - JMLR 17(34):1–7, 2016

  56. Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael Franklin, Ion Stoica The Power of Choice in Data-Aware Cluster Scheduling - OSDI 2014

  57. Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica Quantifying eventual consistency with PBS - CACM Research Highlight August 2014

  58. Kay Ousterhout, Aurojit Panda, Joshua Rosen, Shivaram Venkataraman, Reynold Xin, Sylvia Ratnasamy, Scott Shenker, Ion Stoica The Case for Tiny Tasks in Compute Clusters - HotOS 2013

  59. Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, and Robert S. Schreiber Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices - Eurosys 2013

  60. Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica PBS at Work: Advancing Data Management with Consistency Metrics. - Demo at SIGMOD 2013

  61. Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, and Randy Katz Cake: Enabling High-level SLOs on Shared Storage Systems - SoCC 2012

  62. Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, and Randy Katz Sweet Storage SLOs with Frosting - HotCloud 2012

  63. Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, and Robert S. Schreiber Using R for Iterative and Incremental Processing - HotCloud 2012

  64. Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica Quantifying Eventual Consistency with PBS - VLDB Journal Special Edition - Best of VLDB 2012

  65. Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica Probabilistically Bounded Staleness for Practical Partial Quorums - VLDB 2012

  66. Storage system design for non-volatile byte-addressable memory using consistent and durable data structures - Masters Thesis, University of Illinois, Urbana-Champaign 2011

  67. Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy Campbell Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory - FAST 2011

  68. Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy Campbell Redesigning Data Structures for Non-Volatile Byte-Addressable Memory - Non-Volatile Memories Workshop 2011

  69. Reza Farivar, Harshit Kharbanda, Shivaram Venkataraman, Roy Campbell An Algorithm for Fast Edit Distance Computation on GPUs - IEEE Innovative Parallel Computing (InPar) 2012

  70. Abhishek Verma, Shivaram Venkataraman, Matthew Caesar, and Roy H. Campell Scalable Storage for Data-intensive Computing - Handbook of Data-Intensive Computing, Springer Science, 2011.

  71. Ellick Chan, Shivaram Venkataraman, Nadia Tkach, Kevin Larson, Alejandro Gutierrez and Roy H. Campbell Characterizing Data Structures for Volatile Forensics - Workshop on Systematic Approaches to Digital Forensic Engineering (SADFE), 2011

  72. Elllick Chan, Shivaram Venkataraman, Francis David, Amey Chaugule, Roy Campbell Forenscope: A Framework for Live Forensics - ACSAC 2010

  73. Abhishek Verma, Xavier Llora, Shivaram Venkataraman, David Goldberg and Roy Campbell Scaling eCGA Model Building via Data Intensive Computing - IEEE Congress on Evolutionary Computation, CEC 2010