ETO: Accelerating Optimization of DNN Operators by High-Performance Tensor Program Reuse
Summary: ETO accelerates DNN operator optimization via cross-operator tensor program reuse, with defined reuse conditions. A reuse-based tuner prunes search space and bridges to boost cross-operator reuse, interfacing with backends for fast, effective tuning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jingzhi Fang (Hong Kong University of Science and Technology)
- 2. Yanyan Shen (Shanghai Jiao Tong University)
- 3. Yue Wang (Shenzhen University)
- 4. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@article{fang_vldb22,
title = {{ETO: Accelerating Optimization of DNN Operators by High-Performance Tensor Program Reuse}},
author = {Fang, Jingzhi and Shen, Yanyan and Wang, Yue and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {2},
pages = {183--195},
doi = {10.14778/3489496.3489500},
url = {https://doi.org/10.14778/3489496.3489500},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,900 | Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving | 2025 | SIGMOD | 5.7430032e-05 |
| 9,475 | BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach | 2023 | SIGMOD | 5.2634238e-05 |
| 9,881 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 5.2040783e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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