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Ziheng Jiang

Ziheng Jiang has 4 papers in our library, in 2018–2024, most of them on GPU & parallel computing, Language models & pretraining; often with Arvind Krishnamurthy, Carlos Guestrin, Eddie Yan.

4 papers in our library · 2018–2024

OpenAlex · ORCID · Wikidata · h-index 15 · 49 works · 2,162 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 789 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #1696

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

🏢PowerChinaBeijing · China2025–2026 · 4 paperscurrent🎓Jiangnan UniversityWuxi · China2026 · 1 papercurrent🎓University of WashingtonSeattle · United States2018–2023 · 10 papers🎓Beijing Institute of TechnologyBeijing · China2010–2015 · 8 papers🏢ByteDanceBeijing · China2024–2025 · 2 papers🎓Dalian Maritime UniversityDalian · China2022 · 2 papers🏢JingdongBeijing · China2018 · 2 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 8The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksHardware and Architecture · 4 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workComputer Networks and Communications · 1 workArtificial Intelligence · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.GPU & parallel computing · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20242 papers

2010s

20191 paper20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning (2018 · 1,384 citations)
    Deep learning models increasingly need to run on very different hardware: server GPUs, phones, embedded boards, and custom accelerators such as FPGAs and TPU-like chips.
  2. Learning to Optimize Tensor Programs (2019 · 269 citations)
    Deep learning systems depend on fast tensor operators such as matrix multiplication and convolution. Today these come mostly from hand-tuned vendor libraries like cuDNN, which cover only a narrow set of hardware and make every new device expensive to support. The authors treat operator implementation as a search…
  3. MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs (2024 · 157 citations)
    When an LLM training job takes over a whole cluster of tens of thousands of GPUs, two engineering problems dominate.
  4. FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion (2024 · 67 citations)
    Tensor parallelism splits individual layers of large models across GPUs. This lowers latency, but it adds AllGather and ReduceScatter traffic that can take a large share of runtime. Earlier overlap schemes, such as TransformerEngine, cut a GEMM and its collective into a few chunks, one per device, and schedule them…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. TVM: An Automated End-to-End Optimizing Compiler for Deep LearningarXiv:1802.04799Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.2018 · 1,384 citations
  2. Learning to Optimize Tensor ProgramsarXiv:1805.08166Tianqi Chen, Lianmin Zheng, Eddie Yan, et al.2019 · 269 citations
  3. A Hardware–Software Blueprint for Flexible Deep Learning Specialization ↗IEEE Micro · no summary yetThierry Moreau et al.2019 · 184 citations
  4. MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUsarXiv:2402.15627Ziheng Jiang, Haibin Lin, Yinmin Zhong, et al.2024 · 157 citations
  5. FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel FusionarXiv:2406.06858Li-Wen Chang, Wenlei Bao, Qi Hou, et al.2024 · 67 citations

 Co-authors here

12 of 15 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 789 times (of the 149,439 papers whose bibliographies we read); 59 citing papers with a page here →.

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