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<!DOCTYPE html>
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<title>Shivaram Venkataraman</title>
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<h1 style="text-align:center;">Shivaram Venkataraman</h1>
<h4 style="text-align:center;">Assistant Professor, Computer Science, University of Wisconsin-Madison</h4>
<h4 style="text-align:center;">Office: 7367 CS. Email: shivaram at cs.wisc.edu</h4>
</header>
<!-- section id="downloads" class="clearfix">
<a href="https://github.com/shivaram" id="view-on-github" class="button"><span>View on GitHub</span></a>
</section -->
<section id="links">
<table style="border:0px">
<tr>
<td width="25%" style="border:0px;">
<h3>
<a href="#teaching" class="link">
Teaching
</a>
</h3>
</td>
<td width="25%" style="border:0px;">
<h3>
<a href="#students" class="link">
Group
</a>
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<h3>
<a href="#pubs">
Publications
</a>
</h3>
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<td width="25%" style="border:0px;">
<h3>
<a href="publications/shivaram_cv.pdf">
CV
</a>
</h3>
</td>
</tr>
</table>
</h3>
</section>
<hr>
<section id="main_content">
<!-- <h3>About</h3> -->
<p>I am an Assistant Professor in the <a href="http://cs.wisc.edu">Computer Science Department</a> at University of Wisconsin, Madison.
My research interests are in designing systems and algorithms for large scale data analysis and machine learning.
My <a href="projects.html">dissertation research</a> looked at abstractions that make it
easier to express new machine learning algorithms and systems that can improve their
performance.
</p>
<p>
Before coming to Madison, I was a post-doctoral researcher in the <a href="https://www.microsoft.com/en-us/research/group/systems-research-group-redmond/">Systems
Research Group</a> at Microsoft Research in Redmond. Previously, I completed my PhD from UC Berkeley where I was advised by
<a href="http://cs.berkeley.edu/~istoica">Ion Stoica</a> and <a href="https://cs.uchicago.edu/directory/michael-franklin">Mike Franklin</a>.
I also have a Masters from University of Illinois at Urbana-Champaign and worked in the <a href="http://srg.cs.illinois.edu">Systems Research Group</a>, with
Prof. Roy Campbell.
</p>
<!-- p style="text-align:center;font-weight:500">I will be joining <a
href="http://cs.wisc.edu">University of Wisconsin, Madison</a> as an assistant professor in Fall 2018 !
</p -->
<!-- p style="text-align:center;font-weight:500">I am graduating in May 2017 and am looking for academic jobs.<br>
<a href="application/shivaram_cv.pdf"> CV </a> -
<a href="application/shivaram_research.pdf"> Research Statement </a> -
<a href="application/shivaram_teaching.pdf"> Teaching Statement </a>
</p-- >
<!--p>In 2009, I completed my Masters at University of Illinois at
Urbana-Champaign and worked in the <a
href="http://srg.cs.illinois.edu">Systems Research Group</a>, under
the guidance of Prof. Roy Campbell.
Before that I worked as a Software Engineer at <a
href="http://www.google.com/about/jobs/locations/bangalore/">Google,
Bangalore</a> for
three years and I hold a Bachelor’s degree in Computer Science from
<a href="http://www.bits-pilani.ac.in">Birla Institute of Technology and
Science, Pilani, India</a>.</p -->
<hr>
<h3 id="teaching">Teaching</h3>
<p>CS 537 Intro to OS: <a href="http://pages.cs.wisc.edu/~shivaram/cs537-sp23">S23 <a href="http://pages.cs.wisc.edu/~shivaram/cs537-sp20">S20</a>
<a href="http://pages.cs.wisc.edu/~shivaram/cs537-sp19">S19</a>
</p>
<p>CS 744 Big Data Systems:
<a href="http://pages.cs.wisc.edu/~shivaram/cs744-fa22">F22</a>
<a href="http://pages.cs.wisc.edu/~shivaram/cs744-fa21">F21</a>
<a href="http://pages.cs.wisc.edu/~shivaram/cs744-fa20">F20</a>
<a href="http://pages.cs.wisc.edu/~shivaram/cs744-fa19">F19</a>
<a href="http://pages.cs.wisc.edu/~shivaram/cs744-fa18">F18</a>
</p>
<p>CS 839: Advanced Machine Learning Systems: <a href="http://pages.cs.wisc.edu/~shivaram/cs839-sp22">S22</a>
</p>
<hr>
<h3 id="students">Group</h3>
<ul>
<li>Saurabh Agarwal (Phd Student, co-advised with Dimitris Papailiopoulos)</li>
<li>Jason Mohoney (Phd Student, co-advised with Theodoros Rekatsinas)</li>
<li>Konstantinos Kanellis (Phd Student)</li>
<li>Rutwik Jain (Phd Student, co-advised with Matt Sinclair)</li>
<li>Brandon Tran (Phd Student, co-advised with Matt Sinclair)</li>
<li>Song Bian (Phd Student)</li>
<li>Minghao Yan (Phd Student)</li>
<li>Johannes Freischuetz (Phd Student)</li>
<li>Tzu-Tao Chang (Phd Student)</li>
</ul>
<h3 id="students">Alumni</h3>
<ul>
<li>Pengfei Zheng (Post-doc, co-advised with Aditya Akella)</li>
<li>Aditi Singh (MS)</li>
<li>Rachit Tibrewal (MS)</li>
<li>Olesia Elfimova (MS, to Dropbox)</li>
<li>Adarsh Kumar (MS, to Amazon Alexa AI)</li>
<li>Arjun Balasubramanian (MS, to Amazon AWS)</li>
<li>Wei Hao (BS, to Columbia)</li>
<li>Yiheng Xu (BS, to Maryland)</li>
<li>Yuhan Liu (BS, to UChicago)</li>
<li>Ziyi Zhang (BS, to UChicago)</li>
<li>Rui Pan (BS, to Princeton)</li>
<li>Lynn Liu (BS, to UC Berkeley)</li>
<li>Prasoon Sinha (BS, to UT Austin)</li>
<li>Anze Xie (BS, to UCSD)</li>
<li>Anders Carlsson (BS, to Amazon)</li>
</ul>
<hr>
<h3 id="pubs">Recent Publications</h3>
<p>
Meguru Yamazaki, Shivaram Venkataraman
<a href="javascript:void(0);">CO2: Precise Attention Score Observation for improving KV
Cache Replacement in Large Language Model</a> - Efficient Systems for Foundation Models
(ES-FoMO) Workshop at the International Conference on Machine Learning (ICML) 2024
</p>
<p>
Rutwik Jain, Brandon Tran, Keting Chen, Matthew Sinclair, Shivaram Venkataraman
<a href="javascript:void(0);">
PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters
</a> - International Conference for High Performance Computing, Networking, Storage and
Analysis (Supercomputing 2024)
</p>
<p>Konstantinos Kanellis, Johannes Freischuetz, Shivaram Venkataraman <a href="https://dl.acm.org/doi/pdf/10.1145/3650203.3663336">Nautilus: A Benchmarking
Platform for DBMS Knob Tuning</a> - DEEM Workshop 2024
</p>
<p>Saurabh Agarwal, Bilge Acun, Basil Homer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu
<a href="https://arxiv.org/abs/2403.08058">CHAI: Clustered Head Attention for Efficient
LLM Inference</a> - ICML 2024
</p>
<p>Song Bian, Dacheng Li, Hongyi Wang, Eric Xing, Shivaram Venkataraman <a
href="https://proceedings.mlsys.org/paper_files/paper/2024/file/71381211d0abef73ed1887b83c4547b1-Paper-Conference.pdf">Does
compressing activations help model parallel training?</a> - MLSys 2024
</p>
<p>Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman <a href="https://arxiv.org/abs/2312.12621">Blox: A
Modular Toolkit for Deep Learning Schedulers</a> - Eurosys 2024
</p>
<h4>2023</h4>
<p>
Saurabh Agarwal, Chengpo Yan, Ziyi Zhang, Shivaram Venkataraman
<a href="https://arxiv.org/abs/2202.12429">
BagPipe: Accelerating Deep Recommendation Model Training
</a> - SOSP 2023
</p>
<p>
Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang
<a href="https://arxiv.org/abs/2306.14086"> Mirage: Towards Low-interruption Services on Batch GPU
clusters with Reinforcement Learning
</a> - International Conference for High Performance Computing, Networking, Storage and
Analysis (Supercomputing 2023)
</p>
<p>
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
<a href="https://arxiv.org/abs/2202.02365">
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
</a> - Eurosys 2023
</p>
<p>
Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella
<a href="https://arxiv.org/abs/2210.00093">Shockwave: Fair and Efficient Cluster Scheduling for Dynamic
Adaptation in Machine Learning</a> - NSDI 2023
</p>
<p>
Harsh Darshan Sapra, Olesia Elfimova, Sahana Upadhya, Lukas Desorcy, Michael Wagner,
Shivaram Venkataraman, Chol-Bum Kweon, Sage Kokjohn, Justin Shumaker <a href="https://www.sae.org/publications/technical-papers/content/2023-01-0522/">Estimating
Battery State-of-Charge within 1% using Machine Learning and Physics-based Models</a> - SAE World Congress 2023
<h4>2022</h4>
<p>
Prasoon Sinha, Akhil Guliani, Rutwik Jain, Matthew Sinclair, Shivaram Venkataraman
<a href="https://arxiv.org/abs/2208.11035">
Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems
</a> - International Conference for High Performance Computing, Networking, Storage and
Analysis (Supercomputing 2022)
</p>
<p>
Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman
<a href="https://arxiv.org/abs/2203.05128">
LlamaTune: Sample-Efficient DBMS Configuration Tuning
</a> - VLDB 2022
</p>
<p>
Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris Papailiopoulos
<a href="https://arxiv.org/abs/2103.00543">
On the Utility of Gradient Compression in Distributed Training Systems
</a> - MLSys 2022
</p>
<h4>2021</h4>
<p>
Anze Xie, Anders Carlsson, Jason Mohoney, Roger Waleffe , Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman
<a href="http://vldb.org/pvldb/vol14/p2759-mohoney.pdf">
Demonstration of Marius: Graph Embeddings with a Single Machine
</a> - VLDB 2021
</p>
<p>
Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella
<a href="https://dl.acm.org/doi/10.1145/3488659.3493778">Doing more by doing less: how structured partial backpropagation improves deep
learning clusters</a>
- DistributedML Workshop at CoNEXT 2021
</p>
<p>
Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang
<a href="https://arxiv.org/abs/2107.01739">
KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks
</a> - International Conference for High Performance Computing, Networking, Storage and Analysis (SC21)
</p>
<p>
Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
<a href="https://arxiv.org/abs/2101.08358">
Marius: Learning Massive Graph Embeddings on a Single Machine
</a> - OSDI 2021
</p>
<p>
Arjun Singhvi, Arjun Balasubramanian, Kevin Houck, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella
<a href="https://dl.acm.org/doi/10.1145/3472883.3486981">Atoll: A Scalable Low-Latency Serverless Platform</a>
</a> - SoCC 2021
</p>
<p>
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris Papailiopoulos
<a href="https://arxiv.org/abs/2010.16248">Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
</a> - MLSys 2021
</p>
<p>
Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai and Rahul Potharaju
<a href="https://arxiv.org/abs/2010.03035">
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo
</a> - NSDI 2021
</p>
<p>
Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman
<a href="https://arxiv.org/abs/2102.01386">
AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning
</a> - arXiv preprint <a href="https://github.com/uw-mad-dash/AutoFreeze">code</a>
</p>
<p>
Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella
<a href="https://arxiv.org/abs/2101.07344">
Accelerating Deep Learning Inference via Learned Caches
</a> - arXiv preprint
</p>
<h4>2020</h4>
<p>
Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman
<a href="https://dl.acm.org/doi/10.1145/3419111.3421287">Serverless Linear Algebra</a> - SoCC 2020
</p>
<p>
Konstantinos Kanellis, Ramnatthan Alagappan, Shivaram Venkataraman.
<a href="https://www.usenix.org/conference/hotstorage20/presentation/kanellis">
Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs
</a> - HotStorage 2020
</p>
<p>
Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, and Aditya Akella, Amar Phanishayee, Shuchi Chawla.
<a href="publications/themis-nsdi2020.pdf">
Themis: Fair and Efficient GPU Cluster Scheduling
</a> - NSDI 2020
</p>
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Nikhil Devanur, Jorgen Thelin, Ion Stoica
<a href="publications/blink-mlsys2020.pdf">
Blink: Fast and Generic Collectives for Distributed ML
</a> - MLSys 2020
</p>
<h4>2019</h4>
<p>
Jack Kosaian, K.V. Rashmi, Shivaram Venkataraman
<a href="publications/sosp2019parity-models.pdf">Parity Models: Erasure-Coded Resilience for Prediction Serving
Systems</a> - SOSP 2019
</p>
<p>
Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang
<a href="https://www.usenix.org/system/files/atc19-jeon.pdf">Analysis of Large-Scale
Multi-Tenant GPU Clusters for DNN Training Workloads</a> - USENIX ATC 2019
</p>
<p>
John Emmons, Sadjad Fouladi, Ganesh Ananthanarayanan, Shivaram Venkataraman, Silvio Savarese, Keith Winstein
<a href="publications/hotvid06-final.pdf">Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary</a> -
Hot Topics in Video Analytics and Intelligent Edges (HotEdgeVideo 2019)
</p>
<p>
Qifan Pu, Shivaram Venkataraman, Ion Stoica
<a href="publications/locus-nsdi19.pdf">Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure</a> - NSDI 2019
</p>
<h4>2018</h4>
<p>
Anand Padmanabha Iyer, Zaoxing Liu and Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
<a href="publications/asap-osdi18.pdf">ASAP: Fast, Approximate Pattern Mining at Scale</a> - OSDI 2018
</p>
<p>Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Shivaram Venkataraman, Paramvir Bahl, and Matthai Philipose, Phillip B. Gibbons, Onur Mutlu
<a href="publications/focus-osdi18.pdf">Focus: Querying Large Video Datasets with Low Latency and Low Cost</a> - OSDI 2018
</p>
<p>Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa,
Terry Kim, Saravanam Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao
<a href="publications/chi-vldb18.pdf">Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems</a> - VLDB 2018
<h4>2017</h4>
<p>
Shivaram Venkataraman
<a href="publications/shivaram-dissertation.pdf">System Design for Large Scale Machine Learning</a> - PhD Dissertation
<p>
Shivaram Venkataraman, Aurojit Panda, Kay Ousterhout, Michael Armbrust, Ali Ghodsi, Michael J. Franklin, Benjamin Recht, Ion Stoica
<a href="publications/drizzle-sosp17.pdf">Drizzle: Fast and Adaptable Stream Processing at Scale</a> - SOSP 2017
</p>
<p>Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht
<a href="publications/pywren-socc17.pdf">Occupy the Cloud: Distributed Computing for the 99% </a> - SoCC 2017 - <a href="https://arxiv.org/abs/1702.04024">arxiv version</a>
</p>
<p>
Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht
<a href="publications/acc-gs-icml17.pdf">Breaking Locality Accelerates Block Gauss-Seidel </a> - ICML 2017 <a href="https://arxiv.org/abs/1701.03863">arxiv version</a>
</p>
<p>
Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht
<a href="publications/keystoneml-icde17.pdf">KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics </a> - ICDE 2017 <a href="https://arxiv.org/abs/1610.09451">arxiv version</a>
</p>
<p>
Omid Alipourfard, Jianshu Chen, Hongqiang Liu, Shivaram Venkataraman, Minlan Yu, Ming Zhang
<a href="publications/cherrypick-nsdi17.pdf">Cherry Pick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics</a> - NSDI 2017
</p>
<p> Please see <a href="publications.html"> here </a> for a complete list.
<hr>
<!-- h3 id="talks">Selected Talks</h3 -->
<!-- p>
<em>Low Latency Execution for Apache Spark</em> at
<a href="https://spark-summit.org/2016/events/low-latency-execution-for-apache-spark/">Spark Summit 2016</a>
</p>
<p>
<em>Ernest: Efficient Performance Prediction for Large Scale Advanced Analytics</em> at
<a href="talks/ernest-nsdi-2016.pdf">NSDI 2016</a>
</p>
<p>
<em>SparkR: Scaling R Programs with Spark</em> at
<a href="talks/sparkr-sigmod-talk.pdf">SIGMOD 2016</a>, <a href="talks/sparkr-summit-2015.pdf">Spark Summit 2015</a>
</p>
<p>
<em>The Power of Choice in Data-Aware Cluster Scheduling</em> at
<a href="talks/kmn-osdi-talk.pdf">OSDI 2014</a>
</p>
<p>
<em>Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices</em> at
<a href="talks/presto-eurosys13-talk.pdf">Eurosys 2013</a>
</p>
<p>
<em>Probabilistically Bounded Staleness for Practical Partial Quorums</em>
joint talk with Peter Bailis, at <a href="talks/pbs-vldb12-talk.pdf">VLDB 2012</a>
</p>
<p>
<em>Using R for Iterative and Incremental Processing</em> at
<a href="talks/presto-hotcloud12-talk.pdf">HotCloud 2012</a>
</p>
<p>
<em>Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory</em> at
<a href="talks/nvm-fast11-talk.pdf">FAST 2011</a>
</p>
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