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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>
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Teaching
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<h3 id="pubs">Publications</h3>
<!-- label class="filter-type" for="filter"><h4>Presto</h4></label>
<input type="checkbox" id="filter" -->
<h4>2021</h4>
<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
</p>
<p>
Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
<a href="https://arxiv.org/abs/2101.08358">
Learning Massive Graph Embeddings on a Single Machine
</a> - arXiv preprint
</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>
<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="javascript:void(0);">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>
Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, and Aditya Akella
<a href="publications/freeze-hotcloud19.pdf">Accelerating Deep Learning Inference via Freezing</a> - HotCloud 2019
</p>
<p>
Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman
<a href="publications/oneaccess-hotcloud19.pdf">The Case for Unifying Data Loading in Machine Learning Clusters</a> - HotCloud 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>
Vaishaal Shankar, Karl Krauth, Qifan Pu, Eric Jonas, Shivaram Venkataraman, Ion
Stoica, Benjamin Recht, Jonathan Ragan-Kelley
<a href="https://arxiv.org/abs/1810.09679">numpywren: serverless linear algebra</a> - arxiv preprint
</p>
<p>
Jack Kosaian, K.V. Rashmi, Shivaram Venkataraman
<a href="https://arxiv.org/abs/1806.01259"> Learning a Code: Machine Learning for
Approximate Non-Linear Coded Computation </a> - arxiv preprint
</p>
<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
<p>Anand Iyer, Aurojit Panda, Shivaram Venkatraman, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica
<a href="publications/gap-grades18.pdf">Bridging the GAP: Towards Approximate Graph
Analytics</a> - GRADES-NDA 2018.
<p>Anand Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
<a href="publications/asap-hotcloud18.pdf">Towards Fast and Scalable Graph Pattern
Mining</a> - HotCloud 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>
<h4>2016</h4>
<p>
Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez
<a href="http://arxiv.org/abs/1702.05865">Hemingway: Modeling Distributed Optimization Algorithms</a> - Learning Systems Workshop, NIPS 2016
</p>
<p>
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
<a href="http://cacm.acm.org/magazines/2016/11/209116-apache-spark">Apache Spark: A Unified Engine for Big Data Processing</a> - CACM Contributed Article, Nov 2016
<p>
Shivaram Venkataraman, Zongheng Yang, Michael J Franklin, Ben Recht, Ion Stoica
<a href="publications/ernest-nsdi.pdf">Ernest: Efficient Performance Prediction for Large
Scale Advanced Analytics</a> - NSDI 2016
</p>
<p>
Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui
Meng, Reynold Xin, Ali Ghodsi, Michael Franklin, Ion Stoica, Matei Zaharia
<a href="publications/sparkr-sigmod.pdf">SparkR: Scaling R Programs with Spark</a> - SIGMOD 2016
</p>
<p>
Reza Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan
Sparks, Aaron Staple, Matei Zaharia
<a href="publications/matrix-spark-kdd.pdf">Matrix Computations and Optimization in Apache
Spark</a> - KDD 2016. Best Paper runner-up, Applied Data Science Track.
</p>
<p>Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Ben Recht
<a href="http://arxiv.org/abs/1602.05310">Large Scale Kernel Learning using Block Coordinate
Descent</a> - arxiv preprint
</p>
<h4>2015</h4>
<p>
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
<a href="http://arxiv.org/abs/1505.06807">MLlib: Machine Learning in Apache Spark</a> -
JMLR 17(34):1–7, 2016
</p>
<h4>2014</h4>
<p> Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael Franklin, Ion Stoica
<a href="publications/kmn-osdi-final.pdf">The Power of Choice in Data-Aware Cluster Scheduling</a> - OSDI 2014
</p>
<p>Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica
<a href="publications/pbs-cacm.pdf">Quantifying eventual consistency with PBS - CACM
Research Highlight</a> August 2014
</p>
<h4>2013</h4>
<p> Kay Ousterhout, Aurojit Panda, Joshua Rosen, Shivaram Venkataraman,
Reynold Xin, Sylvia Ratnasamy, Scott Shenker, Ion Stoica
<a href="publications/tinytasks-hotos13.pdf">The Case for Tiny Tasks in Compute Clusters</a> - HotOS 2013
</p>
<p>Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, and Robert S. Schreiber
<a href="publications/presto-eurosys13.pdf">Presto: Distributed Machine Learning and Graph Processing with Sparse
Matrices</a> - Eurosys 2013 </p>
<p>Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica
<a href="publications/pbs-demo-sigmod12.pdf">PBS at Work: Advancing Data Management with
Consistency Metrics.</a> - Demo at SIGMOD 2013</p>
<h4>2012</h4>
<p>Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, and Randy Katz
<a href="publications/cake-socc12.pdf">Cake: Enabling High-level SLOs
on Shared Storage Systems </a> - SoCC 2012
</p>
<p>Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, and Randy Katz
<a href="publications/frosting-hotcloud12.pdf">Sweet Storage SLOs
with Frosting</a> - HotCloud 2012
</p>
<p>Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, and Robert S. Schreiber
<a href="publications/presto-hotcloud12.pdf">Using R for Iterative and
Incremental Processing</a> - HotCloud 2012</p>
<p>Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica
<a href="publications/pbs-vldb-journal.pdf">Quantifying Eventual
Consistency with PBS</a> - VLDB Journal Special Edition - Best of VLDB 2012</p>
<p>Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica
<a href="publications/pbs-vldb12.pdf">Probabilistically Bounded
Staleness for Practical Partial Quorums</a> - VLDB 2012</p>
<h4>Earlier Work</h4>
<p>
<a href="publications/nvm-masters-thesis.pdf">
Storage system design for non-volatile byte-addressable memory using
consistent and durable data structures</a> - Masters Thesis, University of Illinois, Urbana-Champaign 2011
</p>
<p>Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy Campbell
<a href="publications/nvm-fast11.pdf">Consistent and Durable Data Structures for Non-Volatile
Byte-Addressable Memory</a> - FAST 2011
</p>
<p>Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy Campbell
<a href="publications/nvm-nvmw11.pdf">Redesigning Data Structures for Non-Volatile Byte-Addressable
Memory</a> - Non-Volatile Memories Workshop 2011
</p>
<p>Reza Farivar, Harshit Kharbanda, Shivaram Venkataraman, Roy Campbell
<a href="publications/align-inpar12.pdf">An Algorithm for Fast Edit
Distance Computation on GPUs</a> - IEEE Innovative Parallel Computing
(InPar) 2012</p>
<p>Abhishek Verma, Shivaram Venkataraman, Matthew Caesar, and Roy H. Campell
<a href="publications/dataintensive-chapter.pdf">Scalable Storage for
Data-intensive Computing</a> - Handbook of Data-Intensive Computing, Springer Science, 2011.
</p>
<p>Ellick Chan, Shivaram Venkataraman, Nadia Tkach, Kevin Larson, Alejandro Gutierrez and Roy H. Campbell
<a href="publications/cafegrind-sadfe11.pdf">Characterizing Data
Structures for Volatile Forensics</a> - Workshop on Systematic Approaches to Digital
Forensic Engineering (SADFE), 2011</p>
<p>Elllick Chan, Shivaram Venkataraman, Francis David, Amey Chaugule, Roy Campbell
<a href="publications/forenscope-acsac10.pdf">Forenscope: A Framework
for Live Forensics</a> - ACSAC 2010</p>
<p>Abhishek Verma, Xavier Llora, Shivaram Venkataraman, David Goldberg and Roy Campbell
<a href="publications/ecga-cec10.pdf">Scaling eCGA Model Building via
Data Intensive Computing</a> - IEEE Congress on Evolutionary Computation, CEC 2010</p>
<!--p>Will Dietz, Kevin Larson, Shivaram Venkataraman
<a href="publications/mozyg-final-report.pdf">MoZyg: Secure Framework for Cross Platform Applications
on Mobile Devices</a> - Course Project Report for CS 523 - Advanced Operating Systems at
University of Illinois, 2010 <a href="talks/mozyg-final-slides.pdf">Slides (pdf)</a>
<a href="http://wdtz.org/cs523/">Source code</a> </p-->
<!--p>Abhishek Verma, Shivaram Venkataraman, Matthew Caesar, Roy
Campbell - Efficient Metadata Management for Cloud Computing
applications – University of Illinois, Urbana-Champaign, Technical
Report, January 2010 (link)</p -->
<!-- p>Reza Farivar, Shivaram Venkataraman, Ellick Chan, Yanen Li,
Abhishek Verma, Roy Campbell - Distributed Approximate Short Gene
Sequence Alignment - Poster Session at Bioengineering at Illinois Day,
University of Illinois, April 2010</p -->
<!-- p>Shivaram Venkataraman, Niraj Tolia, Roy Campbell - Consistent and
Durable Data Structures for Non-Volatile Byte-Addressable Memory -
Poster Session at the 9th USENIX Symposium on Operating Systems Design
and Implementation (OSDI 2010), Vancouver, BC, Canada, October 2010
(pdf)</p -->
<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>
<hr-->
</section>
</div>
</div>
</body>
</html>