BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach
Summary: BladeDISC is a compiler-driven optimizer for dynamic-shape ML workloads, tackling fusion and codegen with unknown shapes. Key ideas: symbolic shape representation and shape-information propagation to enable shape-agnostic fusion and generic codegen. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhen Zheng (Alibaba)
- 2. Zaifeng Pan (Alibaba; Renmin University of China)
- 3. Dalin Wang (Alibaba; Renmin University of China)
- 4. Kai Zhu (Alibaba)
- 5. Wenyi Zhao (Alibaba)
- 6. Tianyou Guo (Alibaba)
- 7. Xiafei Qiu (Alibaba)
- 8. Minmin Sun (Alibaba)
- 9. Junjie Bai (Alibaba)
- 10. Feng Zhang (Renmin University of China)
- 11. Xiaoyong Du (Renmin University of China)
- 12. Jidong Zhai (Tsinghua University)
- 13. Wei Lin (Alibaba)
BibTeX Citation
@inproceedings{zheng_sigmod23,
title = {{BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach}},
author = {Zheng, Zhen and Pan, Zaifeng and Wang, Dalin and Zhu, Kai and Zhao, Wenyi and Guo, Tianyou and Qiu, Xiafei and Sun, Minmin and Bai, Junjie and Zhang, Feng and Du, Xiaoyong and Zhai, Jidong and Lin, Wei},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3617327},
url = {https://dl.acm.org/doi/10.1145/3617327},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,769 | Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 30 of 30 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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