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Shivaram Venkataraman

Shivaram Venkataraman has 2 papers in our library, in 2019, most of them on GPU & parallel computing, Bias, fairness & safety; often with Amar Phanishayee, Aditya Akella, Fan Yang.

2 papers in our library · 2019

OpenAlex · ORCID · Wikidata · h-index 31 · 154 works · 7,271 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 261 times in our library · 18 more in our library without a page yet · ⭐ Paper Stars ranking: #5832

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Wisconsin–MadisonMadison · United States2019–2026 · 67 papersEmployment · Associate Professor, Computer Sciencescurrent🎓ETH ZurichZurich · Switzerland2026 · 2 paperscurrent🎓University of California, BerkeleyBerkeley · United States2012–2019 · 26 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2010–2011 · 5 papers🎓University of Wisconsin SystemMadison · United States2018–2021 · 4 papers🏢MicrosoftRedmond · United States2018–2019 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksComputer Networks and Communications · 2 worksHardware and Architecture · 2 worksInformation Systems · 2 works

🧭 Topics 2Research subjects and languages the work is about.GPU & parallel computing · 2 worksBias, fairness & safety · 1 work

 Works by year

2010s

20192 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads (2019 · 179 citations)
    The paper examines how deep learning training behaves on a shared, production GPU cluster at Microsoft. Such clusters differ from classic big-data clusters in two ways: a GPU cannot easily be split among users, and multi-GPU jobs must have all their GPUs allocated at once (gang scheduling). The authors describe…
  2. Themis: Fair and Efficient GPU Cluster Scheduling (2019 · 87 citations)
    Organizations increasingly pool GPUs into shared clusters for ML training, but fair-sharing schedulers built for big-data analytics, such as DRF, handle these workloads poorly.

All works

20 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Apache Spark ↗Communications of the ACM · no summary yetMatei Zaharia et al.2016 · 2,436 citations
  2. Occupy the cloud ↗Symposium on Cloud Computing · no summary yetEric Jonas et al.2017 · 412 citations
  3. Ernest: efficient performance prediction for large-scale advanced analytics ↗Networked Systems Design and Implementation · no summary yetShivaram Venkataraman et al.2016 · 338 citations
  4. Probabilistically bounded staleness for practical partial quorums ↗Proceedings of the VLDB Endowment · no summary yetPeter D. Bailis et al.2012 · 185 citations
  5. Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training WorkloadsarXiv:1901.05758Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.2019 · 179 citations
  6. Drizzle ↗Symposium on Operating Systems Principles · no summary yetShivaram Venkataraman et al.2017 · 145 citations
  7. Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure ↗Networked Systems Design and Implementation · no summary yetQifan Pu, Shivaram Venkataraman, Ion Gabriel Stoica2019 · 117 citations
  8. Cake ↗ACM Symposium on Cloud Computing (SoCC) · no summary yetAndrew Wang et al.2012 · 101 citations
  9. The power of choice in data-aware cluster scheduling ↗Operating Systems Design and Implementation · no summary yetShivaram Venkataraman et al.2014 · 91 citations
  10. Themis: Fair and Efficient GPU Cluster SchedulingarXiv:1907.01484Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, et al.2019 · 87 citations
  11. The case for tiny tasks in compute clusters ↗no summary yetKay Ousterhout et al.2013 · 76 citations
  12. numpywren: serverless linear algebra ↗arXiv (Cornell University) · no summary yetVaishaal Shankar et al.2018 · 69 citations
  13. Blink: Fast and Generic Collectives for Distributed ML ↗arXiv:1910.04940 · no summary yetGuanhua Wang et al.2019 · 58 citations
  14. Occupy the Cloud: Distributed Computing for the 99% ↗arXiv (Cornell University) · no summary yetEric Jonas et al.2017 · 55 citations
  15. Quantifying eventual consistency with PBS ↗The VLDB Journal · no summary yetPeter D. Bailis et al.2013 · 46 citations
  16. Bridging the GAP ↗no summary yetAnand Padmanabha Iyer et al.2018 · 17 citations
  17. Cake: Enabling High-level SLOs on Shared Storage Systems ↗no summary yetAndrew Wang et al.2012 · 11 citations
  18. PBS at work ↗Proceedings - ACM-SIGMOD International Conference on Management of Data · no summary yetPeter D. Bailis et al.2013 · 9 citations
  19. Sweet storage SLOs with Frosting ↗no summary yetAndrew Wang et al.2012 · 9 citations
  20. Record Placement Based on Data Skew Using Solid State Drives ↗Lecture notes in computer science · no summary yetJun Suzuki et al.2014 · 3 citations

 Co-authors here

4 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 261 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

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