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Wencong Xiao

Wencong Xiao has 2 papers in our library, in 2019–2024, most of them on Language models & pretraining, GPU & parallel computing, Efficient models & compression; often with Amar Phanishayee, Fan Yang, Shivaram Venkataraman.

2 papers in our library · 2019–2024

OpenAlex · ORCID · Wikidata · h-index 13 · 38 works · 1,122 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 253 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5569

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

🏢ByteDanceBeijing · China2025–2026 · 5 paperscurrent🏢Alibaba GroupHangzhou · China2020–2025 · 14 papers🎓Beihang UniversityBeijing · China2015–2019 · 9 papers🏢MicrosoftRedmond · United States2019–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 worksInformation Systems · 2 worksHardware and Architecture · 1 work

🧭 Topics 3Research subjects and languages the work is about.GPU & parallel computing · 1 workEfficient models & compression · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20241 paper

2010s

20191 paper

 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. Llumnix: Dynamic Scheduling for Large Language Model Serving (2024 · 119 citations)
    Requests to a served LLM vary widely in prompt length, output length and latency needs. Neither their run time nor their KV-cache memory growth is known in advance. Dispatching each request once, with policies inherited from classic DNN serving, therefore causes preemptions, queuing delays, memory fragmentation…

All works

4 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. Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training WorkloadsarXiv:1901.05758Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.2019 · 179 citations
  2. Llumnix: Dynamic Scheduling for Large Language Model ServingOSDI 2024Biao Sun, Ziming Huang, Hanyu Zhao, et al.2024 · 119 citations
  3. Balanced Sparsity for Efficient DNN Inference on GPU ↗arXiv:1811.00206 · no summary yetZhuliang Yao et al.2018 · 102 citations
  4. SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetShi‐Jie Cao et al.2019 · 77 citations

 Co-authors here

6 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 253 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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